`/`
` lines (inline tags like / in prose
+are left intact).
+
+Idempotent. Usage:
+ python3 clean_llms_md.py _site # walk *.llms.md under a directory
+ python3 clean_llms_md.py a.llms.md b.md # specific files
+"""
+
+import re
+import sys
+from pathlib import Path
+
+# A line that is ONLY an opening/closing (optionally indented). Block-level
+# wrapper noise — never matches inline tags mid-prose.
+_BARE_DIV = re.compile(r"^[ \t]*]*>[ \t]*$")
+
+
+def clean_text(text: str) -> str:
+ out = [ln for ln in text.split("\n") if not _BARE_DIV.match(ln)]
+ cleaned = "\n".join(out)
+ # collapse the blank-line runs a removed wrapper can leave behind (3+ → 2)
+ cleaned = re.sub(r"\n{3,}", "\n\n", cleaned)
+ return cleaned
+
+
+def clean_file(path: Path) -> bool:
+ original = path.read_text(encoding="utf-8")
+ cleaned = clean_text(original)
+ if cleaned != original:
+ path.write_text(cleaned, encoding="utf-8")
+ return True
+ return False
+
+
+def _iter_targets(args):
+ for a in args:
+ p = Path(a)
+ if p.is_dir():
+ yield from p.rglob("*.llms.md")
+ elif p.exists():
+ yield p
+
+
+def main(argv) -> int:
+ if not argv:
+ print(__doc__)
+ return 1
+ changed = 0
+ for path in _iter_targets(argv):
+ if clean_file(path):
+ changed += 1
+ print(f"clean_llms_md: cleaned {changed} file(s)")
+ return 0
+
+
+if __name__ == "__main__":
+ sys.exit(main(sys.argv[1:]))
diff --git a/.github/scripts/build/fill_version.py b/.github/scripts/build/fill_version.py
index 48594a7f..0f35b09a 100644
--- a/.github/scripts/build/fill_version.py
+++ b/.github/scripts/build/fill_version.py
@@ -6,13 +6,17 @@
version, so we fill it each build, like intros and changelogs.
The value is the tracked version from .llm_cache/versions.json, looked up via the
-original-filename field. No record yet -> {major}.0.0 from the _vN.qmd name. No
+original-filename field. The doc's own `version:` frontmatter is never trusted as
+input - it is output we overwrite. No record yet -> seed one (max(major,1).0.0,
+so a _v0 or a name without _vN starts at 1.0.0) so the doc becomes tracked. No
bump is computed here.
"""
import argparse
+import json
import re
import sys
+from datetime import date
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1])) # .github/scripts
@@ -68,7 +72,8 @@ def main():
vf = Path(args.versions_file) if args.versions_file else repo_root / VERSIONS_FILE
versions = load_json_or_empty(vf, label="versions")
- filled = baseline = 0
+ filled = baseline = seeded = 0
+ today = date.today().isoformat()
for qmd in sorted(Path(args.docs_dir).rglob("*.qmd")):
if {"_site", ".quarto", "_meta"} & set(qmd.parts):
continue
@@ -79,31 +84,49 @@ def main():
_, end = bounds
src = fm_value(lines, end, "original-filename")
- major = major_from_name(src) if src else None
- if major is None:
- major = major_from_name(qmd.name)
- if major is None:
- print(f"[fill_version] {qmd}: no _vN in filename, skipping")
- continue
-
+ mj = major_from_name(src) if src else None
+ if mj is None:
+ mj = major_from_name(qmd.name)
+ # First published version is 1.0.0: a _v0 or a name without _vN starts
+ # at 1.0.0, while _v4 etc. keep their filename major.
+ baseline_major = max(mj, 1) if mj is not None else 1
+
+ key = f"DOCS/{src}" if src else None
tracked = None
- if src:
- entry = versions.get(f"DOCS/{src}") or versions.get(src)
+ if key:
+ entry = versions.get(key) or versions.get(src)
tracked = (entry or {}).get("current_version")
- if tracked and tracked.split(".")[0] == str(major):
+ if tracked and (mj is None or tracked.split(".")[0] == str(mj)):
version = tracked
else:
- version = f"{major}.0.0"
+ version = f"{baseline_major}.0.0"
baseline += 1
+ # No record yet: seed one so the doc becomes tracked, rather than
+ # re-deriving the fallback every build. Keyed by original-filename.
+ if key and key not in versions:
+ versions[key] = {
+ "current_version": version,
+ "last_bump": "initial",
+ "last_bump_reason": "First release",
+ "last_release_tag": "initial",
+ "last_updated": today,
+ "major_from_filename": baseline_major,
+ }
+ seeded += 1
if set_version(lines, end, version):
qmd.write_text("".join(lines), encoding="utf-8")
filled += 1
+ if seeded:
+ vf.parent.mkdir(parents=True, exist_ok=True)
+ with vf.open("w", encoding="utf-8") as f:
+ json.dump(versions, f, indent=2, sort_keys=True)
+
print(
f"[fill_version] set version on {filled} files "
- f"({baseline} fell back to {{major}}.0.0)"
+ f"({baseline} used max(major,1).0.0 baseline, {seeded} new cache entries)"
)
diff --git a/.github/scripts/build/group_docs_by_category.py b/.github/scripts/build/group_docs_by_category.py
index 811e275e..204b96a9 100644
--- a/.github/scripts/build/group_docs_by_category.py
+++ b/.github/scripts/build/group_docs_by_category.py
@@ -2,6 +2,7 @@
import shutil
import json
import secrets
+import sys
from pathlib import Path
import yaml
@@ -13,19 +14,15 @@
".quarto",
}
-# Category to directory mapping
-# Multiple categories can map to the same directory
-CATEGORY_TO_DIRECTORY_MAP = {
- "guidelines": "guidelines",
- "products": "products",
-}
-
# Change working directory to root of the repository
script_dir = Path(__file__).parent
root_dir = script_dir / "../../../"
os.chdir(root_dir.resolve())
+sys.path.insert(0, str(script_dir.parent.resolve()))
+from helpers.categories import directory_for, non_browsable_names # noqa: E402
+
# Non-browsable doc mapping file
NON_BROWSABLE_MAP_PATH = Path(".github/non_browsable_doc_map.json")
@@ -67,13 +64,7 @@ def add_secret_mapping(mappings, source, base, url):
def get_directory_for_category(category):
- if not category:
- return "uncategorized"
-
- if category in CATEGORY_TO_DIRECTORY_MAP:
- return CATEGORY_TO_DIRECTORY_MAP[category]
-
- return category
+ return directory_for(category)
def read_qmd_frontmatter(file_path):
@@ -168,7 +159,7 @@ def group_qmd_files_by_category(source_dir="origin_DOCS", target_dir="DOCS"):
rel_source = str(qmd_file.relative_to(source_path))
project_name = qmd_file.parts[1] if len(qmd_file.parts) > 2 else ""
- if category == "non-browsable":
+ if category in non_browsable_names():
nb_dir = target_path / "non-browsable"
nb_dir.mkdir(exist_ok=True)
mapping = get_secret_mapping_for_source(secret_mappings, rel_source)
diff --git a/.github/scripts/build/inject_changelog.py b/.github/scripts/build/inject_changelog.py
index 1a04fa87..0e1abbb0 100755
--- a/.github/scripts/build/inject_changelog.py
+++ b/.github/scripts/build/inject_changelog.py
@@ -1,10 +1,15 @@
#!/usr/bin/env python3
+# ruff: noqa: E402
+import panflute as pf
import os
import json
import sys
import re
from html.parser import HTMLParser
-import panflute as pf
+
+# Monkeypatch RAW_FORMATS to bypass strict obsolete panflute validators
+pf.elements.RAW_FORMATS.add("typst")
+pf.elements.RAW_FORMATS.add("pdf")
CHANGE_LOG_PATH = os.path.abspath(
os.path.join(os.path.dirname(__file__), "../../../.llm_cache/change_logs.json")
@@ -199,24 +204,15 @@ def add_changelog(doc):
# Render a
- summary as a real BulletList so it survives in
# every output format; otherwise fall back to plain text.
- if sanitized_summary.startswith("
- (.*?) ", sanitized_summary, re.DOTALL - ) - if list_items: - list_items_elements = [] - for item in list_items: - clean_item = re.sub(r"<[^>]+>", "", item).strip() - # ListItem expects a list of Block elements - list_item = pf.ListItem(pf.Plain(pf.Str(clean_item))) - list_items_elements.append(list_item) - bullet_list = pf.BulletList(*list_items_elements) - summary_cell = pf.TableCell(bullet_list) + if "
- " in sanitized_summary: + items = re.findall(r"
- (.*?) ", sanitized_summary, re.DOTALL) + if items: + bulleted = "\n".join(f"• {re.sub(r'<[^>]+>', '', x).strip()}" for x in items) + summary_cell = pf.TableCell(pf.Plain(pf.Str(bulleted))) else: - clean_summary = re.sub(r"<[^>]+>", "", sanitized_summary) - summary_cell = pf.TableCell(pf.Plain(pf.Str(clean_summary))) + summary_cell = pf.TableCell(pf.Plain(pf.Str(re.sub(r'<[^>]+>', '', sanitized_summary).strip()))) else: - summary_cell = pf.TableCell(pf.Plain(pf.Str(sanitized_summary))) + summary_cell = pf.TableCell(pf.Plain(pf.Str(re.sub(r'<[^>]+>', '', sanitized_summary).strip()))) row = pf.TableRow(date_cell, version_cell, summary_cell) body_rows.append(row) diff --git a/.github/scripts/categories.yml b/.github/scripts/categories.yml new file mode 100644 index 00000000..27921345 --- /dev/null +++ b/.github/scripts/categories.yml @@ -0,0 +1,22 @@ +# Single source of truth for DOCS document categories. +# Add / remove / edit categories ONLY here — read via helpers/categories.py by: +# - validate_qmd_files.py (allowed values for the `category:` field) +# - group_docs_by_category.py (output-directory routing + non-browsable branch) +# +# Fields per entry: +# name (required) the value authors put in `category:` frontmatter; also the output folder. +# Must be lowercase kebab-case ([a-z][a-z0-9-]*) and must NOT collide with a managed +# directory (_meta, assets, _site, .quarto, templates, theme, includes). +# browsable (optional, default TRUE) set `false` only to hide a category; omit otherwise. +# directory (optional) output folder, ONLY if it must differ from `name` (none do today). +# +# NOTE (scope): today the `browsable:false` flag is honoured by group_docs_by_category.py only. +# generate_index_all.py and remove_non_browsable.py still key on the literal name "non-browsable". +# Adding a NEW browsable:false category therefore also needs those scripts updated (a follow-up). +categories: + - name: guidelines + - name: products + - name: reports # reserved for future use + - name: uncategorized + - name: non-browsable + browsable: false diff --git a/.github/scripts/filters/simplify_tables_gfm.lua b/.github/scripts/filters/simplify_tables_gfm.lua new file mode 100644 index 00000000..b530159f --- /dev/null +++ b/.github/scripts/filters/simplify_tables_gfm.lua @@ -0,0 +1,238 @@ +-- Simplify raw-HTML tables in the gfm (.llms.md) output ONLY. +-- +-- Complex tables (merged cells) are emitted as raw `
- decompression of the image data, +
- geometric model computation: geolocation information, coarse interband / interdetector registration, +
- SWIR pixels re-arrangement. +
- refers always to one Datatake; +
- refer to one or several Datastrip from the same Datatake; +
- may cover the full Datatake or an extract of the Datatake. +
- Radiometric corrections:
+
-
+
- dark signal, pixel response non-uniformity, crosstalk correction, defective pixels; +
- high spatial resolution bands restoration: deconvolution and denoising based on a wavelet processing. +
+ - Physical geometric model refinement using GCPs provided by the GRI; this model is not applied to the image but appended to the metadata +
- Singular pixels detections (defectives pixels, saturations, no-data). +
- GeoTIFF, 16bit, 3 bands, no compression, ToA reflectance +
- False colour composite using S2 bands 8, 11 and 4 (NIR, SWIR, red) +
- 10 meter spatial resolution +
- Re-projection to national projection as specified by EEA with EPSG codes +
- No geometric improvements +
- No radiometric improvements +
- Band 11 brought to 10 m by HPF sharpening +
- no modification of projection, format, naming convention, radiometry, meta data +
- all bands in original resolution +
- Re-projection to national projection as specified by EEA with EPSG codes +
- False colour composite using L8 bands 5, 6, 4 (NIR, SWIR, red) +
- 15 meter spatial resolution +
- No geometric improvements +
- DN to ToA reflectance conversion, followed by HPF sharpening +
- no modification of projection, format, naming convention, radiometry, meta data +
- all bands in original resolution +
- Sand +
- Bedrock +
- Sealed surfaces +
- Permanent inland lakes (natural) +
- Artificial ponds (permanent fishponds, reservoir) +
- Natural ponds (permanent open water surfaces of inland or coastal wetlands) +
- Rivers +
- Channels (permanently with water) +
- Coastal water surfaces: lagoons, estuaries +
- Liquid dump sites (permanent) +
- Temporary water surfaces associated to permanent water bodies +
- Temporary natural (e.g. steppe) lakes and temporary artificial lakes (e.g. cassettes of fishponds) +
- Intermittent rivers +
- Flood areas +
- Water-logged areas +
- Wet agricultural fields, including rice fields +
- Intertidal areas +
- Open water bodies (including floating or emergent vegetation)
+
-
+
- Permanent lakes, reservoirs, ponds +
- Rivers +
+ - Temporary open water bodies (intermittent rivers, changing lake/reservoir levels) +
- Temporarily inundated areas (due to snow melt, floods, or rain) +
- Wet agricultural fields, including rice fields and water-logged areas +
- Transitional coastal water bodies (lagoons, estuaries) +
- Sea and ocean (sea water beyond a boundary provided by the EEA) +
- Permanent snow and glaciers +
- Evergreen/deciduous broadleaved, sclerophyllous and coniferous trees of any use +
- Forests (grown-up and under development) +
- Orchards, olive groves, fruit and other tree plantations, agro-forestry areas +
- Transitional woodland, forests in regeneration +
- Groups of trees within urban areas (alleys, wooded parks and gardens) +
- Forest management/use features inside forests (forest roads, firebreaks, thinnings, forest nurseries, etc.) +
- Forest damage features inside forests (partially burnt areas, storm damages, insect-infested damages, etc.) +
- Open areas within forests (roads, permanently open vegetated areas, clear cuts, fully burnt areas, other severe forest damage areas, etc.) +
- Dwarf shrub-covered areas, such as moors and heathland +
- Vineyards +
- Dwarf pine / green alder in alpine areas +
- Mediterranean shrublands (macchia, garrigue etc.) +
- Shrubland +
- DWH_MG2b_CORE_03 +
- D2_MG2b_NARA_011b +
- D2_MG2b_LOLA_011b +
- VHR_IMAGE_2015 +
- SPOT-5 (2.5m) +
- SPOT-6 (1.5m/4.0m) +
- SPOT-7 (4.0m) +
- Pléiades (2.0m) +
- WorldView-2 (2.0m) +
- VHR_IMAGE_2018 +
- SPOT-6 (1.5m/4.0m) +
- SPOT-7 (4.0m) +
- Pléiades (2.0m) +
- WorldView-2 (2.0m) +
- SuperView-1 (2.0m) +
- KOMPSat (2.0m) +
- Planet Dove (4.0m) +
- Deimos-2 (4.0m) +
- TripleSat-1 (4.0m) +
- DWH_MG2b_CORE_03 +
- D2_MG2b_NARA_011b +
- D2_MG2b_LOLA_011b +
- VHR_IMAGE_2015 +
- VHR_IMAGE_2018 +
- SPOT-5 (2.5m) +
- SPOT-6 (1.5m/4.0m) +
- SPOT-7 (4.0m) +
- Pléiades (2.0m) +
- WorldView-2 (2.0m) +
- SuperView-1 (2.0m) +
- KOMPSat (2.0m) +
- Planet Dove (4.0m) +
- Deimos-2 (4.0m) +
- TripleSat-1 (4.0m) +
- All errors are resolved +
- Adjacent polygons on the border with different codes are manually recoded to the correct class +
- The boundary of the skeleton is not visible in the final product +
- the wrong classification affects the class on the first code level (e.g. Cropland instead of woodland) +
- the wrong classification affects change situations (the wrong classes are corrected to be able to represent the changes properly) +
- Errors resulting of the input data of N2K, RZ and CLC will not be corrected (errors directly taken from the input data to the skeleton) +
- decompression of the image data, +
- geometric model computation: geolocation information, coarse interband / interdetector registration, +
- SWIR pixels re-arrangement. +
- refers always to one Datatake; +
- refer to one or several Datastrip from the same Datatake; +
- may cover the full Datatake or an extract of the Datatake. +
- Radiometric corrections:
+ dark signal, pixel response non-uniformity, crosstalk correction, defective pixels;
+ - high spatial resolution bands restoration:
+ deconvolution and denoising based on a wavelet processing.
+ - Physical geometric model refinement using GCPs provided by the GRI; this model is not applied to the image but appended to the metadata +
- Singular pixels detections (defectives pixels, saturations, no-data). +
- GeoTIFF, 16bit, 3 bands, no compression, ToA reflectance +
- False colour composite using S2 bands 8, 11 and 4 (NIR, SWIR, red) +
- 10 meter spatial resolution +
- Re-projection to national projection as specified by EEA with EPSG codes +
- No geometric improvements +
- No radiometric improvements +
- Band 11 brought to 10 m by HPF sharpening +
- no modification of projection, format, naming convention, radiometry, meta data +
- all bands in original resolution +
- Re-projection to national projection as specified by EEA with EPSG codes +
- False colour composite using L8 bands 5, 6, 4 (NIR, SWIR, red) +
- 15 meter spatial resolution +
- No geometric improvements +
- DN to ToA reflectance conversion, followed by HPF sharpening +
- no modification of projection, format, naming convention, radiometry, meta data +
- all bands in original resolution +
- …
- "):
- list_items = re.findall(
- r"
| Sentinel-2 Multispectral Imager (MSI) | +||
| Swath width (km) | +290 | +|
| Number of bands | +13 (altogether) | +|
| 4 | +in VIS | +|
| 6 | +in NIR | +|
| 3 | +in SWIR | +|
| Ground sampling distance (m) | +10 | +bands 2,3,4 (VIS) and band 8 (NIR) | +
| 20 | +bands 5,6,7,8a (NIR) and bands 11,12 (SWIR) | +|
| 60 | +band 1 (VIS), band 9 (NIR) and band 10 (SWIR) | +|
| Bit depth (recording) | +12 | +|
| Repeat cycle at the Equator (days) | +10 (with 1 satellite) 5 (with 2 satellites) |
+ |
| Data access | +free, full and open access | +|
| Delivered resolution (m) | +10 / 20 / 60 (depending on band) | +|
| + | Bandwidth: lower wavelength – upper wavelength [µm] |
+ Remark | +|||
| + | Sentinel-2 MSI |
+ Landsat-7 ETM |
+ IRS (Resource- sat) LISS-III |
+ SPOT-4 HRV |
+ |
| 1 | +0.433-0.453 | ++ | + | + | VIS band. Main use: atmospheric correction (aerosols) | +
| 2 | +0.458-0.523 | +0.45-0.52 (TM1) | ++ | + | VIS: blue band | +
| 3 | +0.543-0.578 | +0.53-0.61 (TM2) | +0.52-0.59 (MS1) | +0.50-0.59 (XI1) | +VIS: green band | +
| 4 | +0.650-0.681 | +0.63-0.69 (TM3) | +0.62-0.68 (MS2) | +0.61-0.68 (XI2) | +VIS: red band | +
| 5 | +0.698-0.713 | ++ | + | + | NIR: vegetation red edge band | +
| 6 | +0.733-0.748 | ++ | + | + | NIR: vegetation red edge band | +
| 7 | +0.773-0.793 | ++ | + | + | NIR: vegetation red edge band | +
| 8 | +0.735-0.950 | +0.75-0.90 (TM4) | +0.77-0.86 (MS3) | +0.78-0.89 (XI3) | +NIR band | +
| 8a | +0.855-0.875 | ++ | + | + | NIR: vegetation red edge band | +
| 9 | +0.935-0.955 | ++ | + | + | NIR band. Main use: atmospheric correction (water vapor) | +
| 10 | +1.365-1.395 | ++ | + | + | SWIR band. Main use: atmospheric correction (cirrus clouds) | +
| 11 | +1.565-1.655 | +1.55-1.75 (TM5) | +1.55-1.70 (MS4) | +1.58-1.70 (XI4) | +SWIR band | +
| 12 | +2.100-2.280 | +2.09-2.35 (TM7) | ++ | + | SWIR band | +
| Level-1A | +Corresponds to the systematic processing steps that must be applied before any further processing. It includes:
+
|
+
+ One Level-1A/B/C product:
+
|
+
| Level-1B | +Radiometrically corrected and geo-refined product obtained by performing corrections on the Level-1A data and refining its geometric model. + The radiometric corrections are applied but the geo-refinement model is only appended to the metadata and not applied to the product. Corrections include: +
|
+ |
| Level-1C | +Geo-coded top-of atmosphere (TOA) reflectance with a sub-pixel multi-spectral and multi-date registration. + Ortho-image product, i.e. a map projection of the acquired image using a DEM to correct ground geometric distortions. + Note that the reflectance meaningful values go from "1" to "65535" as "0" is reserved for the NO_DATA. + A cloud, land and water mask is associated to the product. + L1C products are resampled with a constant GSD (Ground Sampling Distance) of 10m, 20m and 60m according to the native resolution of the different spectral bands. + |
+ + |
| + | Visual product | +Full product | +
| Sentinel 2A/B | +
+
|
+
+
|
+
| Landsat 8 | +
+
|
+
+
|
+
| Letter code | +L2012 | +Lch | +L2018 | +Short explanation | +Remark | +
| A2012 ≥ 25 | +Ach≥ 5 | +A2018 ≥ 25 | +|||
| A | +1 | +1 | +1 | +Simple change | +Occurs the most frequently | +
| B | +1 | +0 | +1 | +Small change in existing polygon | +Occurs frequently; not interpreted -> max. 5 ha error in CLC2018 | +
| C | +1 | +1 | +0 | +Disappearance of polygon | +Seldom occurs | +
| D | +1 | +0 | +0 | +Disappearance of polygon with small change | +Occurs very seldom, not interpreted -> max. 5 ha error in CLC2018 | +
| E | +0 | +1 | +1 | +Emerging of new polygon | +T is used to avoid > 5 ha <25 ha error in CLC2018 | +
| F | +0 | +0 | +1 | +Emerging of new polygon with small change | +T is used to avoid > 20 ha < 25 ha error in CLC2018 | +
| G | +0 | +1 | +0 | +Change only | +Occurs frequently | +
| H | +0 | +0 | +0 | +Small change only | +Not interpreted | +
+
+#### D. Polygon disappearing with small change: a polygon decreases to < 25 ha with a change < 5 ha
+
+In a few cases, existing polygons decrease to a size < 25 ha with a change < 5 ha. As change is < 5 ha, the changed patch should not be delineated. This causes a minor (< 5 ha) mistake in CLC2018.
+
+Remark: 10% exaggeration in size is allowed (i.e. 4.5 ha new residential area is better to enlarge to 5 ha in order to keep it in CLC-Change).
+
+#### E. New polygon: a polygons grows > 25 ha with a change > 5 ha
+
+The simplest case of this type is the emerging of a new patch > 25 ha (Fig. 13).
+
+CLC2018
+
+
+If a patch that existed in 2012, but used to be < 25 ha (thus not mapped in CLC2012) grows with a change > 5-ha so that it exceeds the 25-ha limit in 2018, a so-called „technical change" polygon must also be applied. Besides delineating the real change (grown part of the polygon), the non-changed (originally existing) part must be delineated as well, with identical code~2012~ and code~2018~ and an additional attribute marking it as technical change. Using up the two types of change polygons, the patch will be included in CLC2018 automatically, whereas the technical change polygon will be deleted later from the final CLC-Change database (Fig. 13). (For more information on technical changes see its definition at Ch. 4.3.1).
+
+
+
+In order to avoid inaccuracies being introduced into CLC2018, the same method is applied also in cases when the real change is > 25 ha so that it would make up a new polygon itself in CLC2018. This case too, a real change polygon must be drawn over the changed ("new") part and a technical change polygon must be drawn above the non-changed ("already existing") part if > 5 ha (Fig. 15).
+
+
+
+A special case of this type (combined with type C) is the code change of a polygon (Fig. 16).
+
+CLC2018
+
+
+#### F. New polygon with small change: a polygon grows > 25 ha with a change < 5 ha
+
+In the few cases when polygon grows over 25 ha with a real change < 5 ha, the real change should be added to the technical change polygon as well. Without using technical change, we would introduce a major (between 20 and 25 ha) mistake into CLC2018 Fig. 17). Using technical change the newly appearing polygon will be included in CLC2018, while the change database will not contain any polygon here (no real change > 5 ha).
+
+Remark: 10% exaggeration in size is allowed (i.e. 4.5 ha new sport and recreation area is better to enlarge to 5 ha to keep it in CLC-Change; it leads to case E).
+
+CLC2018
+
+
+#### G. Only change: changes in a non-existing polygon
+
+This type includes cases when the change polygon is not connected to a valid polygon neither in CLC2012 nor in CLC2018, while valid (> 5 ha) change occurred. This type of change also must be coded according to their real change process (Figs. 18 and 19).
+
+
+(222) > 5 ha disappears because of being turned into arable land (211). Both patches must be delineated as changes (211-121 and 222-211), as being > 5 ha. No new polygons emerge in CLC2018, as corresponding change polygons are generalised.
+
+
+
+#### H. Small change (< 5 ha) in not existing polygon (< 25 ha)
+
+As polygons in all three databases are smaller than their respective area limits, this case should not be dealt with.
+
+### Treating changes in by-definition heterogeneous classes – changes on landscape level
+
+CLC nomenclature includes some land cover classes that by definition represent heterogeneous landscapes, thus certain polygons are made up a mosaic of smaller homogenous patches, most of them < 25 ha. This means a shift from a dominantly feature-level mapping generally applied by CLC to a landscape-level approach for classes especially: 242, 243 and 313.[^9] If individual land cover changes occur within polygons of these classes in a way that they altogether change the characteristics of the area on a landscape level, then change polygons should be delineated on a landscape level, too. Let us take for example a 243 polygon, being mostly agricultural landscape with mosaic of small (< 25 ha) patches of semi natural features: forest, bushes, wetlands and / or natural grassland. If a few of the bushes are cut and turned into arable land, the main character of the polygon does not change, it is still an agricultural landscape with significant amount of natural features. This case changes must be mapped individually as 324-211, thus they will represent real changes in the CLC-Change database, whereas in CLC2018 the 243 polygon will be left unchanged as new 211 patches will be generalized into 243 (Fig. 20).
+
+
+
+It might happen however, that due to an economic / social impact (say change in EU subsidisation system) or for some natural phenomena all or most of the natural patches are turned into arable land, turning the whole landscape's character into agricultural. The area is not a mosaic of natural patches and agricultural land any more, but mostly arable land. This case the change happened on the landscape level, so the change polygon will include the whole area, its code pair being 243-211. It is only in these cases that the delineation of individual changes can be replaced by landscape-level change mapping (Figs. 21 and 22).
+
+[^9]: Heterogeneous classes are not to be confused with general rule of CLC mapping i.e. all classes might have some portion with different land cover. E.g. Discontinuous urban fabric (112) might include < 25 ha parks, water bodies, industry etc.
+
+
+
+
+
+Processes showing to the opposite direction (from homogeneous to heterogeneous landscape) should be treated similarly (Figs. 23 and 24).
+
+
+
+IMAGE/CLC2012 IMAGE 2018 CLC CHANGE CLC2018
+
+
+
+Similar approach should be applied for all three by-definition heterogeneous classes: i.e. 313, 242, 243.
+
+## CLC2018 Support Package (InterChange software)
+
+The CLC2012 Support Package, developed by ETC-SIA has been successfully used by more than 20 national teams in implementing CLC2012. It is a set of standalone applications developed with Embarcadero Delphi XE2 and TatukGIS Developer Kernel. Thus, user does not need to purchase, install and tune any other software to carry out CLC change mapping [24]. The CLC2012 Support Package, being a specialized, task oriented software tool, significantly facilitated updating, change detection and mapping, quality control and correction of CLC databases by means of computer-assisted visual photointerpretation [4].
+
+The tool has been upgraded for the CLC2018 project, called now CLC2018 Support Package [31]. Main novelties of the tool are as follows:
+
+- supports the use of Sentinel-2 imagery,
+- versions for both 32-bit and 64-bit operation systems,
+- enhanced error checking function,
+- new, more efficient editing tool ("re-shape") in the revision window,
+- various convenience functions.
+
+Like its predecessors, the CLC2018 Support Package consists of three modules:
+
+1. **InterChange** for interpreting land cover changes: Provides a tool for the revision of CLC2012 land cover database and supports the interpretation of land cover changes in order to create the `CLC-Change~2012-2018~` database. The program provides a convenient and easy-to-use interface for editing polygons in CLC2012 and `CLC-Change~2012-2018~` databases, for viewing and modification of polygon data and for finding and correction (revision) of errors generated during interpretation and editing.
+2. **InterCheck** for checking CLC databases: Serves the checking of revised CLC2012 (or CLC2018) and `CLC-Change~2012-2018~` data. InterCheck program has been prepared primarily for supporting the CLC Technical Team, although national central teams might apply it as a tool for checking of the completed CLC2018 and `CLC-Change~2012-2018~` databases. Many file formats are supported, not only those that has been prepared with **InterChange**.
+3. **LUCAS Photo Viewer**: Program displays the main data and field photos of the LUCAS2012 and LUCAS2015[^10]) sampling point selected in InterChange or InterCheck program.
+
+### User registration
+
+CLC2018 Support Package is available for all participating national teams **free of charge**. The submission of a **registration form** is the sole requirement of using the software. This (besides keeping record of users) enables developer to contact users in case an update of the software package is released. The registration form and the Support Package can be downloaded from: | + | 1st verification | +2nd verification | +Data to provide; data format |
+ Remark | +
| sample selection by | +national team | +CLC2018 Technical Team | +revised CLC2012 (or CLC201812) and CLC-Change2012-2018 in shapefile format | +samples should be completed, i.e. full area interpreted | +
| sample size to verify | +a single area, about 50 km x 50 km in size within a Sentinel-2 tile (or Landsat 8 image) | +about 50 km x 50 km areas within Sentinel-2 tiles (or Landsat 8 image). Number of areas depends on size of the country / region (see Table 19). | +||
| IMAGE2012 to provide | +area(s) covering the sample interpretation(s) | +IRS / SPOT / RapidEye data covering the sample area; in GeoTiff, LAN format | +all used IMAGE2012 data have to be sent | +|
| IMAGE2018 | +ID (file name) of S2 (or Landsat-8) images used in deriving CLC data should be provided for each sample | ++ | images to be sent by NT only if not the centrally provided IMAGE2018 is used | +|
| Orthophotos and topographic maps | ++ | recommended if available; WMS access welcome | +||
¹Magellium (lead); ²Finnish Meteorological Institute (FMI). | +|---------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------| +| **Project Owner** | Joanna Przystawska | +| **Project Manager** | Florence Marti | +| **Document Code** | HR-WSI-DT-066-MAG_ATBD_ICE | +| **Document Version** | 1.4 | +| **Distribution** | Public | +| **Date** | 20/01/2026 | +### Document Approver(s) and Reviewer(s) + +| Name | Role | Action | Date | +|----------------------------------------------------------|------------------------------------------------------|-----------------------------------------|------------------------------------------------| +| Joanna Przystawska | Project Owner | Revision | 11/12/2024 | +| Lorenzo Solari | Project Owner (deputy) | Revision | 11/12/2024 | +| Joanna Przystawska | Project Owner | Revision | 07/03/2025 | +| Lorenzo Solari | Project Owner (deputy) | Revision | 07/03/2025 | + +### Document history + +| Revision | Date | Created by | Short description of changes | +|---------------------------|-----------------------------------|--------------------------------------------------------|----------------------------------------------------------------------------------| +| 0.1 | 16/04/2024 | F. Marti (Magellium) and the HR-WSI consortium | First draft | +| 1.0 | 22/11/2024 | F. Marti (Magellium) and the HR-WSI consortium | First version of the document. In line with: WIC S2 V100; WIC S1 V100; WIC S1+S2 V100; AWIC V100; ICD V100 | +| 1.1 | 28/02/2025 | F. Marti (Magellium) and the HR-WSI consortium | Account for EEA revision.
In line with: WIC S2 V100; WIC S1 V100; WIC S1+S2 V100; AWIC V100; ICD V100 | +| 1.2 | 14/03/2025 | F. Marti (Magellium) and the HR-WSI consortium | Account for EEA revision.
In line with: WIC S2 V100; WIC S1 V100; WIC S1+S2 V100; AWIC V100; ICD V100 | +| 1.3 | 23/12/2025 | M. Denisselle (Magellium) and the HR-WSI consortium | Upgrade of WIC S1 to V101.
In line with: WIC S2 V100; WIC S1 V101; WIC S1+S2 V100; AWIC V100; ICD V100 | +| 1.4 | 20/01/2025 | M. Denisselle (Magellium) and the HR-WSI consortium | Update of the thresholds of the ICD-QA layer. | +```{=html} +
[AUX3] Version 2022_1 | PANDA COPERNICUS
February 2024 | Resampling at 20m using the GdalWarp¹ 'cubic' algorithm.
Cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM¹ - using GdalWarp. | WIC S2 | +| COP-DEM_EEA-10-DGED
[AUX3] Version 2022_1
&
COP-DEM_GLO-30-DGED
[AUX4] Version 2022_1 | PANDA COPERNICUS
February 2024 | Resampling using GdalWarp² bilinear algorithm and cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM¹.
For S1 preprocessing, SAR wet snow detection, WDS and SWS, additional data preparation as described in Section 2.3. | S1 preprocessing & WIC S1 | + +```{=typst} +#set page(flipped: false, paper: "a4") +#set text(size: 11pt) +``` + +¹ GDAL 3.8.3 Python package distributed on the conda-forge channel for Python 3.12.1 +² GDAL 3.4.3 Python package + +The DEMS EEA-10 and GLO-30 are prepared in the S2 tiling grid. The GLO-30 DEM is oversampled to 10m by 10m pixel size. The EEA-10-based DEM is used as the primary DEM over the EEA38+UK domain. In case of missing pixels, and along the boundary of the EEA38+UK domain, the oversampled DEM from the DEM GLO-30 is used to ensure that radar layover, foreshortening and shadow masks are also calculated for mountain areas along the EEA38+UK boundary. The orthometric heights of the merged DEM are converted to ellipsoidal heights referring to the WGS84 ellipsoid applying the EGM2008 geoid⁴. + +The 10m DEM is used for the S1 SAR preprocessing module and the preparation of the radar layover, foreshortening and shadow mask (Section 4). It is resampled to 60m by 60m pixel size for the WIC S1 processing routine (Section 6). + +## Preparation of the radar layover, foreshortening and shadow masks + +For each Sentinel-1 track, a radar layover, foreshortening and shadow mask is calculated during the geocoding process in the S1 SAR Preprocessing Module (Section 4). This step requires SAR imaging geometry and a DEM as input. The 10m DEM built from the EEA-10 and GLO-30 DEMs as introduced in Section 2.1 is used. + +The resulting layers are aggregated from 10m by 10m pixel spacing to 60m by 60m pixel spacing using a median filter for the radar layover, foreshortening, and shadow masks. They are then used for the WIC S1 products (Section 6). + +## Copernicus High Resolution Layers + +CLMS delivers several High Resolution Layers (HRL) at 10m pixel spacing, over the EEA38+UK domain and the United Kingdom (UK). Table 3 details which HRLs are used by which processing routines. + +The HRLs are delivered as individual raster files in the ETRS89 LAEA coordinate system at the resolution of 10m x 10m. They are reprojected to the UTM/WGS84 coordinate system and cropped to the range of the Sentinel-2 tiling grid and resampled to 60m x 60m pixel spacing for WIC S1 production. + +³ WGS84/UTM or "EPSG:326XX" where XX is the UTM zone over which the S2 tile lies. XX also corresponds to the first 2 digits of a tile name (e.g. 32TLR). The auxiliary data are generated over the EEA38+UK (covering UTM zones 25 to 38). + +⁴ https://earth-info.nga.mil/GandG/wgs84/gravitymod/egm2008/egm08_wgs84.html + +```{=html} +
+```
+| | | | |
+|-------------------------------------------------------------------|--------------------------------------------|----------------------------------------------------|-------------------------------------|
+| HRL-Tree Cover Density
Version 2018, 10m LAEA projection (EPSG:3035) [AUX5] | CLMS portal
(Feb. 2024) | Resampling at 60m and cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM⁵ using a majority resampling method⁶. | WIC S1 | +| HRL-Imperviousness Density
Version 2018, 10m LAEA projection (EPSG:3035) [AUX6] | CLMS portal
(Feb. 2024) | Resampling at 60m and cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM⁵ using a majority resampling method⁶. | WIC S1 | +| HRL-Grassland
Version 2018, 10m LAEA projection (EPSG:3035) [AUX7] | CLMS portal
(Feb. 2024) | Resampling at 60m and cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM⁵ using a majority resampling method⁶. | WIC S1 | +| HRL-Water And Wetness
Version 2018, 10m LAEA projection (EPSG:3035) [AUX8] | CLMS portal
(Feb. 2024) | Wetness information has been removed entirely, other classes are kept.
Cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM⁵ using a majority resampling method⁶. | WIC S1 (resampling at 60m)
WIC S2, WIC S1+S2, ICD (resampling at 20m)
Only used for initialising the NRT and delayed-time productions (more details in Section 2.4) | +```{=html} +
+```
+
+## Preparation of the dynamic water mask used for the annual productions of WIC S1, WIC S2 and ICD data
+
+WIC S1 processing is performed within the extent of water bodies, while the generation of WIC S2 relies on the surface water extent during the post-processing stage. The ICD aggregates WIC data over a specified water area as well. All WIC S1, WIC S2, and ICD products rely on the same water mask for a given hydrological year.
+
+For most WIC and ICD products, inland water areas are defined using a water mask derived from the HR-WSI Water Cover Duration (WCD) product. This product provides the frequency of water occurrences at the pixel scale (10m x 10m) over the hydrological year, which spans from 1 September to 31 August, as detailed in the water ATBD [AD1] and PUM [AD4]. The WCD product for hydrological year N-1 serves as the basis for calculating WIC S1, WIC S2, and ICD products for year N (1 September N-1 to 31 August N).
+
+⁵ WGS84/UTM or "EPSG:326XX" where XX is the UTM zone over which the S2 tile lies. XX also corresponds to the first 2 digits of a tile name (e.g. 32TLR). The auxiliary data are generated over the EEA38+UK (covering UTM zones 25 to 38).
+
+⁶ The GdalWarp 'mode' algorithm is a majority algorithm that selects the value which appears most often of all the sampled points. In the case of ties, the first value identified as the mode will be selected - GDAL 3.8.3 Python package distributed on the conda-forge channel for Python 3.12.1.
+
+At the end of each hydrological year, the corresponding WCD product is generated and used to create the new water mask (as described below). The water mask used for the current production is then switched for the new one.
+
+::: {.tbl-caption}
+```{=typst}
+#set text(size: 9pt, fill: rgb("#3E6893"))
+```
+Table 4. HR-WSI production phases and water mask used to initialise the WIC S1, WIC S2 and ICD generations.
+:::
+
+| HR-WSI production phase/hydrological year | Water mask |
+|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
+| S1 and S2 observations from September 2024 to August 2025 (NRT processing) | Based on the HRL-WAW 2018 product, available at the time of processing (see Section 2.3) |
+| S1 and S2 observations from September 2016 to August 2017 (delayed-time processing of the archive) | Based on the HRL-WAW 2018 product, available at the time of processing (see Section 2.3) |
+| Other production periods, i.e. other hydrological years | Based on the WCD product of the previous hydrological year (more details on its computation below) |
+
+A water mask is obtained from the WCD product by applying thresholds to the frequency of water occurrences. As a result, two classes are obtained:
+
+- Permanent water: Water probability ≥ 60% (366 days)
+- Temporary water: 0% > Water probability > 60% (366 days)
+
+The WCD product is in the same spatial extent as the WIC product, but the pixel spacing is 10m x 10m. Thus, the water mask is resampled to:
+
+- 20m x 20m pixel spacing for WIC S2 and ICD processing routines
+- 60m x 60m pixel spacing for WIC S1 processing routine
+
+using the same method which is used in resampling the High Resolution Layers (see Section 2.3).
+
+## Preparation of the river network database used for the WIC S1 and AWIC productions
+
+Ice production relies on a river network database which describes the extent of water bodies on a European scale: EU-Hydro River Network database (2006-2012) version 1 [AUX9] (LAEA projection (EPSG:3035), Vector - Minimum Mapping Unit: 1 ha). It is delivered by the CLMS.
+
+**Preparation for the WIC S1 production**
+
+The categorisation of lake and river areas, used in quality control flag generation of the WIC S1 product, is based on the EU-Hydro hydrographic database [AUX9]. InlandWater polygons are used as lakes and River_net_p, and Transit_p polygons are used as rivers. The water bodies in vector format in EU-Hydro database are first categorised into lakes and rivers, then are
+
+- River parts with polygons longer than 40 kilometres
+- Rivers connecting lakes (e.g. Vistån River in Sweden)
+- Canals
+- Ditches
+- Lakes
+
+This translates to the following polygon layers from the EU-Hydro database:
+
+- InlandWater
+- Transit_p
+- Ditches_p
+- Subset of the Canals_p
+ - polygons longer than 20 km
+- Subset of the River_Net_p
+ - polygons longer than 40 km
+ - rivers connecting lakes
+
+From those layers, 10 km long sections are created for the following:
+
+- Rivers longer than 40 km (from River_Net_p layer)
+- Rivers connecting lakes
+- Canals longer than 20 km (from Canals_p)
+- Ditches longer than 20 km (from Ditches_p)
+
+These divisions are computed from the mouth of the rivers and canals to their source (see Annex A for details). These linear feature layers contain the river and canal network of each EU-Hydro river basin (see Figure 2). They are used to compute the `river_km_id`. River flow directions are checked and corrected when necessary.
+
+
+
+4. Generating relevant buffers from 10 km long linear features depending on the river or canal characteristics (width, number of curves, islands etc.).
+5. Dividing each river, ditch or canal polygon from step 1 into 10 km long sections and adding a river_km attribute.
+
+## Preparation of the daily meteorological data used for the production of WIC S1
+
+Daily average wind speed and 5-day sum of 2m daily average air temperature from 3-hourly estimations of 2m air temperature, 10m wind direction (u-component) and wind speed (v-component) are generated daily in FMI. 3-Hourly estimations are obtained from ECMWF Atmospheric Model high resolution 10-day forecast (Set I - HRES) dataset [AUX10], disseminated daily at 06:00 hours. The 5-day sum of daily average air temperature is calculated for each cell of the grid (0.1 degrees) after converting temperature values into degrees Celsius. The daily average 10m wind speed is calculated after calculating 3-hourly wind speed from u- (eastward wind) and v- (northward wind) components. Generated meteorological data is made available by SFTP protocol, retrieved once by the HRWSI production system during the processing of WIC S1 every day. After the first successful daily retrieval of meteorological data, it is kept in HRWSI storage to be used by remaining processing routines for the day.
+
+from a Sentinel-2 L1C product using the MAJA software. MAJA stands for the MACCS-ATCOR Joint Algorithm, where MACCS is the Multi-Temporal Atmospheric Correction and Cloud Screening software, developed by CNES and CESBIO, and ATCOR is the atmospheric correction software developed by DLR. MAJA's cloud screening method has been developed for high resolution sensors (e.g. Sentinel-2, Formosat-2, LANDSAT, VENµS) and a large range of applications. HR-WSI relies on the MAJA software to obtain surface reflectances (Sentinel-2 L2A product) and a good-quality Cloud Classification (CC) from a Sentinel-2 L1C product.
+
+MAJA code is open source under Apache licence and available from a Gitlab repository [AUX11]. This section is not intended to be exhaustive, but rather to summarise the principles underlying the MAJA, particularly as regards cloud detection. For more information, readers are invited to read the publicly accessible ATBD (Hagolle et al., 2017).
+
+As summarised in Figure 3, the outputs of the MAJA algorithm (Section 3.2.6) are stored temporarily for further use for:
+
+- Fractional Snow Cover (FSC) product [AD2]
+- S2-based Water/Ice Cover (WIC S2) product (Section 5)
+- S2-based monthly water masks processing [AD1]
+
+
+
+## Retrieval algorithm
+
+### Outline
+
+What makes MAJA unique is the fact that it combines multi-temporal and multi-spectral methods to better detect clouds and estimate atmospheric aerosol content. The use of multi-temporal information improves data quality and robustness but also introduces constraints to the processing, as discussed in Section 3.2.7.
+
+the same shape.
+
+Additionally, the method incorporates classical multispectral properties:
+
+- High clouds have a high reflectance in the Sentinel-2 water vapour absorption band at 1380 nm.
+- Clouds are generally brighter and whiter than most surfaces.
+
+The detection of cloud shadows also involves multi-temporal criteria. While dark zones exist naturally, making it challenging to distinguish them from shadows, shadows cause a sudden apparent darkening of the surface, which can be used for detection.
+
+A final geometrical processing step aligns clouds with their shadows.
+
+Since the boundaries of clouds are fuzzy, and as the multispectral registration applies to the earth's surface and not to the clouds, a buffer is added around the clouds. This results in a classification bias toward greater severity, as part of the buffer may include cloud-free areas. However, this approach is preferred to mitigate the significant impact that undetected clouds could have on downstream applications.
+
+### Related and existing applications
+
+MAJA has been used with several satellites (e.g. Formosat-2, LANDSAT, VENµS) and is currently used in the processing chain that generates the Theia Snow collection [AUX12] from Sentinel-2 observations.
+
+It is also used operationally at DLR, CNES, and the Norwegian and Romanian space agencies. It is available as an open-source software and is downloaded on average once per day.
+
+### Alternative methodologies
+
+Cloud detection algorithms vary in their approach and are based on various spectral properties, spatial, and temporal features, as well as machine learning methods (Skakun et al., 2022). These include for instance, Fmask, initially developed for Landsat imagery (Zhu et al., 2015), (Frantz et al., 2018), S2cloudless [AUX13], the IdePix plugin (Wevers et al., 2021) or the Sen2cor tool, the processor for Sentinel-2 Level 2A product generation (Louis et al., 2016) [AUX14] which similarly to MAJA perform both atmospheric correction and cloud screening. All three adopt a mono-temporal approach, i.e. process single-scenes.
+
+### Input data
+
+The MAJA software works on a Sentinel-2 tile level. For HR-WSI, it is configured to use the following input data:
+
+- Sentinel-2 L1C product [AUX15],
+- Metadata from previous Sentinel-2 L2A products, including a composite image built based on previous cloud-free acquisitions,
+- A digital elevation model (DEM): Copernicus DEM: GLO-30, (DGED format), dataset ID:
+
+Each L1C product leads to a CC product and an L2A product used as input for other HR-WSI processing tasks.
+
+Intermediate data from the resulting L2A product are stored for further internal usage and are not disseminated publicly a metadata file with the updated composite image and the following rasters in Geographic Tagged Image File Format (GeoTIFF):
+
+- SRE images: ground reflectances without the correction of slope effects or surface reflectance
+- FRE images: ground reflectance with the correction of slope effects, or flat reflectance
+- CLM mask: mask describing the cloud and cloud shadow coverage coded over 8 bits
+- MG2: geophysical mask coded over 8 bits
+- EDG: edge mask (actual border of the image within the tile extent)
+
+Readers are referred to a more exhaustive description of the MAJA L2A product [AUX17] and to the ESA Sentinel-2 documentation for spectral bands description [AUX18].
+
+A CC product consists of three GeoTIFF raster files and a metadata file in Extensible Markup Language (XML). The raster files are encoded as 8-bit unsigned integers and retained in the projection, extent and resolution of the initial SWIR band of the Sentinel-2 L2A product (WGS84/UTM with a pixel size of 20 m by 20 m). The list of raster files is as follows.
+
+- CC: cloud and cloud shadow classification from MAJA
+- QAFLAGS: bit-coded quality flags
+- CC-QA: quality value from 0 (high quality) to 3 (minimal quality) for CC
+
+Readers are referred to the product user manual [AD3] for a complete description of the CC product.
+
+### Methodology
+
+#### MAJA cloud and cloud shadow detection
+
+The MAJA ATBD is publicly accessible (Hagolle et al., 2017).
+
+The MAJA method is a recurrent algorithm that processes images in chronological order. Like any recurrent process, it requires initialisation. The different processing modes are described later in this section.
+
+MAJA performs several operations to obtain surface reflectances and generate a high-quality cloud mask for a Sentinel-2 scene:
+
+- Estimation of water vapour content
+- Correction for molecular absorption (including water vapour)
+- Detection of clouds, cloud shadows, water, coarse detection of snow
+- Estimation of aerosol content and aerosol optical thickness (AOT)
+
+For the first image in the time series, a basic atmospheric correction is performed and the clouds are detected using the mono-temporal approach. After each processing, a composite image is updated with the unclouded pixels from the processed date. This composite image is then used as a reference for cloud detection and AOT estimate.
+
+**Cloud detection**
+
+The mono-temporal cloud detection relies on thresholding methods based solely on spectral information. The first test is based on the cirrus band (at 1380 nm 60m - B10) which effectively detects the high clouds (above 2000m, including thin cirrus). Additional thresholds are required, in particular in the visible/near-infrared (VNIR) and short-wave infrared (SWIR). This is an important first step, as subsequent modules (e.g. aerosol and water vapour retrieval) use this information.
+
+In nominal mode (multi-temporal), the cloud detection method is based on a large number of tests, the most efficient of which are:
+
+- A mono-temporal test based on the cirrus band: this test uses a threshold that varies according to the terrain elevation.
+- A multi-temporal test: this test detects a sharp increase of the blue surface reflectance (492 nm, 10 m resolution, B2), compared to the composite image, which indicates presence of a cloud. Since a similar increase can occur on the ground, the pixel is classified as a cloud if the difference in the blue reflectance reaches a certain threshold. The threshold value depends on the age of the pixel in the composite image relative to the pixel of the current image.
+- A final step to avoid over-detection of clouds: for each potential cloud detected by the previous tests, this last test measures the correlation of the pixel neighbourhood with the previous images. As it is unlikely that two different clouds at the same location on successive dates have the same shape, if a large correlation is observed, the pixel is finally not declared as a cloud.
+
+**Cloud shadow detection**
+
+Cloud shadows appear dark, but they are not the only dark objects within a Sentinel-2 image. They can be confused with water areas, bare soils or terrain shadows. Typically, cloud shadows are cast by clouds located within the image. These shadowed pixels are identified through the intersection between potential shadowed pixels (the projected surface of the previously detected cloud) with darkened pixels. In cases where cloud shadows are cast by clouds lying outside of the image boundary, the search is limited to an area near the image edges, based on solar angles. In both cases, darkened pixels are classified using an adaptive threshold method based on the red band and the composite image.
+
+**Dilation of the detected cloud mask**
+
+MAJA software is optimised to minimize cloud cover omissions. Cloud edges are typically fuzzy, and some parts could be undetected. Additionally, clouds scatter light in their neighbourhood,
+
+performed at a coarse resolution (see MAJA configuration section). Afterwards, all masks obtained are oversampled to the full resolution (10/20m) before applying the corrections. This step also helps speed up the MAJA processing.
+
+
+
+**Cloud Classification (CC) formatting**
+
+The Cloud Classification (CC) layer provides information on the cloud cover and the cloud shadows detected by MAJA. It is derived from the CLM, the MG2 and the EDG masks, which are the MAJA's outputs.
+
+The CC layer is intended to give a classification of the scene, however, users should note that a single pixel could have been identified as both cloud-covered and cloud-shaded, as the retrieval methods are performed independently.
+
+From the CC raster is fulfilled according to the following priority rules:
+
+- High clouds (CLM bit 7 == 1),
+- Other clouds (CLM bit 1 == 1 & bit 7 == 0),
+- Cloud shadow ((CLM bit 5 == 1 | CLM bit 6 == 1) & CLM bit 1 == 0 & bit 7 == 0),
+- Cloud and cloud shadow-free pixels: all other pixels that are not outside the acquisition area are assigned to this class. Pixels outside the acquisition area are set to "no data" (from EDG mask).
+
+**Quality assessment (QA) data**
+
+A QA value is reported for each pixel to characterise the quality of the cloud retrieval, based on various indicators. The QA values are derived from a confidence index which itself results from the combination of various flags (QAFLAGS) that are described below. Each flag is a binary value set to one if true and zero otherwise.
+
+Various information can be retrieved from the CLM raster:
+
+- MAJA indicates for each cloud pixel if it was detected via mono-temporal thresholds, multi-temporal thresholds, or both. The multi-temporal approach detects approximately twice as many clouds as the single-temporal approach, which uses more flexible thresholds to avoid overdetection. Two bits are provided in the QAFLAGS layer, as follows:
+
+reflectances. One bit is provided in the QAFLAGS layer, as follows:
+
+- thinnest clouds (CLM - bit 4)
+
+- MAJA specifies how a pixel has been assigned to the cloud shadow class, either through the "geometric approach" for shadows cast by a previously detected cloud, or through the "radiometric approach" to identify cloud shadows originating outside the image. In the latter case, the detection quality is lower, and users are informed about the possibility of missing cloud shadows at the edges of the image. Two bits are provided in the QAFLAGS layer, as follows:
+ - cloud shadows cast by a detected cloud (CLM - bit 5)
+ - cloud shadows cast by a cloud outside image (CLM - bit 6)
+
+Finally, one bit is provided in the QAFLAGS layer to describe the open water areas detected by MAJA. These are retrieved from the geophysical mask:
+
+- Water mask (MG2 - bit 0)
+
+Then, the CC-QA value is computed, starting from the highest quality (0) and being degraded by 1 when the “cloud shadow cast by a cloud outside image” condition is met. The possible values for CC-QA are as follows:
+
+- CC-QA = 0: High quality
+- CC-QA= 1: Medium quality
+
+#### MAJA processing modes
+
+To process a given Sentinel-2 tile for date D, MAJA requires the results of the processing of the previous observation date available (denoted as D-1) from the same tile as input. As a result, the acquisitions must be processed in chronological order. The processing of L2A for date D, using L2A of date D-1 and L1C of date D as inputs is referred to as the nominal processing mode (Figure 5).
+
+
+
+As for all recurrent algorithms, MAJA requires an initialisation. MAJA has two initialisation modes (Figure 6):
+
+at a lower resolution, except for the final iteration, which generates the L2A product for date D.
+
+
+
+For most of the tiles, MAJA needs to be initialised only once when the HR-WSI processing chain is first started. This standard initialisation is performed in backward mode with N set to 10 dates before starting the NRT processing. However, due to interruptions in Sentinel-2 acquisitions MAJA must be re-initialised during the NRT operations if the interruption exceeds 60 days. To comply with NRT requirements, these re-initialisations are done in init mode in the following cases:
+
+- In high-latitude regions, where acquisitions stop during winter due to polar darkness. Hence the L2A process must be reinitialised every year when acquisition restarts.
+- In other regions, if all acquisitions are done with fully overcast conditions over a period exceeding 60 days (though this is very rare). For instance, in the Theia Land Data Centre, this situation has occurred in some tiles of countries of equatorial Africa during the rainy season but has never been observed for tiles located in Europe (even in typically cloudy regions like western France or Scotland).
+
+### MAJA configuration
+
+The main parameters impacting the L2A production for HR-WSI of CLMS are listed below. Some parameters have been changed compared to standard GIPP, as follows:
+
+- **Maximum cloud percentage**
+ The maximum cloud percentage defines the percentage of cloud cover within a tile above which MAJA does not generate the product. The default value is set to 90%, which is considered a balanced compromise saving processing resources without losing too many valid pixels.
+ The experience with the Theia service production shows that the default value discards one-third of the Sentinel-2 tiles. However, a few clear pixels are lost, and these are often small spots in between clouds and therefore often affected by adjacency effects, undetected cloud shadows, an inaccurate estimate of aerosol optical thickness, etc. This default value is also applied for HR-WSI production.
+ `Version 2018, 10m LAEA projection (EPSG:3035) [AUX5] | CLMS portal
(Feb. 2024) | Resampling at 60m and cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM⁵ using a majority resampling method⁶. | WIC S1 | +| HRL-Imperviousness Density
Version 2018, 10m LAEA projection (EPSG:3035) [AUX6] | CLMS portal
(Feb. 2024) | Resampling at 60m and cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM⁵ using a majority resampling method⁶. | WIC S1 | +| HRL-Grassland
Version 2018, 10m LAEA projection (EPSG:3035) [AUX7] | CLMS portal
(Feb. 2024) | Resampling at 60m and cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM⁵ using a majority resampling method⁶. | WIC S1 | +| HRL-Water And Wetness
Version 2018, 10m LAEA projection (EPSG:3035) [AUX8] | CLMS portal
(Feb. 2024) | Wetness information has been removed entirely, other classes are kept.
Cropping on the S2 tiles covering EEA38+UK, in projection WGS84/UTM⁵ using a majority resampling method⁶. | WIC S1 (resampling at 60m)
WIC S2, WIC S1+S2, ICD (resampling at 20m)
Only used for initialising the NRT and delayed-time productions (more details in Section 2.4) | +```{=html} +
(middle of range) [nm] | Spatial resolution [m] | +|--------------------------------------------------------|-----------------------------------------------------------------------------------------------|-------------------------------------------------| +| B2 (blue) | 490 | 10 | +| B3 (green) | 560 | 10 | +| B4 (red) | 665 | 10 | +| B8 (NIR) | 842 | 10 | +| B11 (SWIR) | 1610 | 20 | + +- Cloud mask: 8-bit encoded single-band raster at 20m spatial resolution. Only the first bit, which gathers all clouds and all shadows, is used. +- Geophysical mask: 8-bit encoded single-band raster at 20m spatial resolution; which includes masks related to the quality of the acquisition with regards to geophysical conditions, such as solar angle and clouds. +- Copernicus Digital Elevation Model (DEM) - EEA10 (see Section 2). +- Slope derived from the above DEM. Like the DEM data, it is static for each tile (see Section 2). +- Water mask. An inland water mask delineates the area where the RF classifier's identification of snow must be reinterpreted as a snow-covered ice pixel. It is also used in the quality layers of the product. Users should refer to Section 2.4 to know the source of the water mask for a given WIC S1 product. In most cases, it is derived from the HR-WSI WCD product and represents the conditions of the previous hydrological year. + +### Output data + +For each L2A product, four raster files in Geographic Tagged Image File Format (GeoTIFF) and a metadata file in Extensible Markup Language (XML) are produced. The raster files are coded in 8-bit unsigned integer and maintain the projection, extent and resolution of the initial 20m resolution bands of the Sentinel-2 L2A product (UTM/WGS84 with a pixel size of 20m x 20m). + +The list of raster files is as follows: + +- WIC: water and ice extent + +product. + +### Methodology + +The WIC S2 processing consists in three main processing steps (Figure 9): + +- Input data pre-processing +- Classification by the RF algorithm +- Post-processing, which includes generating the quality layers and preparing the final WIC S2 format + + + +**Step 1: Pre-processing** + +In this step, all the inputs required by the classifier are prepared. + +Four flat surface reflectance bands (B2 B3, B4, B8) are resampled from 10m to 20m, the same resolution as the output WIC S2 product. The downsampling is done using the GdalWarp + +(McFEETERS, 1996): + +$$NDWI = (\rho_{green} - \rho_{NIR}) / (\rho_{green} + \rho_{NIR})$$ +Equation 2 + +- NDSI (normalised difference snow index) used for better distinction of snow (Dozier, 1989): + +$$NDSI = (\rho_{green} - \rho_{SWIR}) / (\rho_{green} + \rho_{SWIR})$$ +Equation 3 + +- NDVI (normalised difference vegetation index) used to identify vegetation (Rouse et al., 1973; Tucker, 1979): + +$$NDVI = (\rho_{NIR} - \rho_{red}) / (\rho_{NIR} + \rho_{red})$$ +Equation 4 + +In the above equations, + +- $\rho_{green}$ is the flat surface reflectance in the green (B3) spectral band at 0.56 µm; +- $\rho_{red}$ is the flat surface reflectance in the red (B4) spectral band at 0.67 µm; +- $\rho_{NIR}$ is the flat surface reflectance in the NIR (B8) spectral band at 0.8 µm; +- $\rho_{SWIR}$ is the flat surface reflectance in the SWIR (B11) spectral band at 1.6 µm. + +The three spectral indices are then binarized, meaning that if the value exceeds or is equal to a threshold, it becomes 1; otherwise it becomes 0. The threshold values for NDWI, NDSI and NDVI are respectively 0.3, 0.4 and 0.1. Those thresholds were set up after several iterations, in order to improve the water and ice detection. The binarization was made in order to help the classifier to identify the pixel land cover, meaning that above the NDWI, NDSI and NDVI thresholds, the pixel might be considered respectively as water, snow or vegetation. + +Two other indices are used for the WIC S2 classification: + +- The standard deviation of the local gradient of blue (B2): + - First, the local gradient of blue is computed, using a Sobel filter in a 5*5 pixels window centred on the pixel, + - Then, the standard deviation of this local gradient of blue is computed, using a 5*5 pixels window centred on the pixel. +- The bounded SWIR band (B11): + - The flat surface reflectance in the SWIR spectral band is bounded. All reflectance values below 200 are reassigned to 200, and all reflectance values above 600 are reassigned to 600. + +In summary, the inputs to the RF classifier are: + +- Binarized NDVI + +**Step 2: Inference using the RF classifier** + +The static classifier is applied to the full Sentinel-2 image (see Section 5.2.8), transforming it into a labelled image with the following classes: + +- Water +- Snow and ice +- Other features + +This labelled image then serves as input for the post-processing step. + +The classification probability generated by the RF algorithm is stored in the PRB layer and is subsequently used for post-processing and the creation of quality layers. + +**Step 3: Post-processing, including the preparation of quality layers** + +Additional steps are necessary to obtain the final classes of the WIC S2 main layer: + +- Water +- Snow-covered or snow-free ice +- Other features +- Cloud +- No data + +There are three steps involved in formatting: + +- The cloud mask is applied to discriminate between the cloud and cloud shadow pixels. +- Pixels outside of the satellite swath are identified by looking for 'no data' in the B12 layer, and this consequent 'no data' mask is applied to the WIC, PRB and WIC-QA layers. +- The water mask is applied to differentiate between ‘snow and ice' pixels above water bodies and those over land. Since snow on ice cannot be distinguished from snow on land using optical observations, 'snow and ice' pixels within the water mask are classified as ice and assigned the label 'snow-covered or snow-free ice'. Meanwhile, snow pixels outside the water mask area are reclassified as 'other features'. + +Three other post-processing steps are applied to enhance the WIC S2 classification in cases where the classifier's performance is deemed suboptimal (see Section 5.2.9): + +- Potential misclassified pixels as 'snow-covered or snow-free ice' in land/water interfaces: if a pixel is classified as 'snow-covered or snow-free ice' and has a classification probability of less than 90% and a NDWI value greater than 0.1, then it is reclassified as 'water'. +- Potential misclassified pixels as 'other features' on water: if a pixel is classified as 'other features', has a NDVI value greater than 0.1 and a difference between the classification probabilities of classes 'water' and 'other features' lower than 20%, then it is reclassified as 'water'. +- Potential misclassified pixels as 'water' in shadow: if a pixel is classified as 'water', has a classification probability lower than 80% and is located in a topographical shadow, then it is reclassified as 'other features'. This step requires a topographical shadow mask (including both self and cast shadows) that varies by season and tile location. This mask + +score (among four possible scores) generated from the PRB layer, and the QAFLAGS layer which includes quality-related bits (see Section 5.2.6). + +**PRB layer** + +The Random Forest classifier gives, for each pixel, a class along with a probability for each class. This layer gives the RF highest probability (meaning the probability of the class affected by the classifier), except for: + +- Cloud and no data pixels, which are given the same value as those from the WIC layer +- Pixels affected by post-processing in the WIC layer: the RF probability of the new class is retrieved and replaces the probability of the previously affected class + +**QAFLAGS layer** + +Quality flags are stored in the QAFLAGS layer, as binary values and are set to one if true and zero otherwise. The first flag identifies topographical shadows in the image (mask obtained during post-processing), the second indicates the potential presence of water based on the annual static water mask and the third indicates which pixels are affected by the WIC layer post-processing (i.e. pixels which class has been modified). + +- Hillshade +- Presence of water +- Pixels affected by post-processing + +All flags are set to zero outside of the detection area. + +**WIC-QA layer** + +Then a confidence score is computed in three successive steps, based on three factors: + +- The classification confidence, coming directly from the Random Forest classifier: + - WIC-QA = 0 if: 80 <= classification probability + - WIC-QA = 1 if: 67 <= classification probability < 80 + - WIC-QA = 2 if: 50 <= classification probability < 67 + - WIC-QA = 3 if: classification probability < 50 +- The agreement between the classification and the water mask: + - If the water mask indicates water absence, and the pixel is classified as 'water' or 'snow-covered or snow-free ice', the WIC-QA value is increased by 1 + - If the water mask indicates water presence, and the pixel is classified as 'other features', the WIC-QA value is increased by 1 +- The hillshade presence: + - The WIC-QA value is increased by 1 if the pixel is located in a topographic shadow + +The possible values of WIC-QA are as follows: + +- WIC-QA = 0: high quality +- WIC-QA = 1: medium quality +- WIC-QA = 2: low quality +- WIC-QA = 3: minimal quality + +The Random Forest classifier was trained using 20.000 pixels from 4 Sentinel-2 MAJA L2A images at 20m resolution, i.e. 80.000 pixels used in total. Those scenes were chosen for the variety of landscapes, locations, and period of the year that they represent (Table 6). Also, each of them has special features on which the classifier is expected to perform well. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 6. Description of the Sentinel-2 scenes using for WIC S2 classifier training +::: + +| Tile id | Date | Location | Characteristics | +|--------------------------|--------------------------------------------|----------------------------------------------------|------------------------------------------------------------------------------| +| 48UVC | 2020/04/23 | Lake Baïkal, Russia | Thin ice, thin rivers, dark soil | +| 36UUB | 2019/02/17 | River Dnieper, Ukraine | Thin rivers | +| 16TGS | 2019/01/13 | Lake Huron, Canada | Snow/ice, almost black and white scene | +| 22MGE | 2021/07/19 | Marajó Bay, Brazil | Turbid water | + +**1- Labelling of the images of the training dataset** + +The images were labelled as part of the work of Jugier et al. (2022) on classifying water and ice from Sentinel-2 imagery using machine learning methods. It is a semi-automated process with the following steps. + +A preliminary labelling is made, using the CNES Active Learning for Cloud Detection (ALCD) software [AUX25], which is a semi-automatic tool using a Random Forest algorithm applied on Sentinel-2 L1C products, to produce labelled images at a 60m resolution. The labelling includes the following classes: land, water, snow, thin ice, thick ice, mineral turbidity, organic turbidity, vegetation, other, clouds, clouds shadows, salt sea. + +While the ALCD tool's primary objective is to identify cloud pixels on a Sentinel-2 L1C image, it can also be used for snow or ice detection. It requires three inputs: a cloud-free scene, a cloudy scene to classify and a DEM product. ALCD starts by combining the input raster products and creating one empty shapefile for each label defined by the user. The user then adds reference vector points for each label in the QGIS software and runs ALCD again to start the classification. If the classified image is not satisfactory, the user can add new vector points to refine the model and redo a classification iteratively until he is satisfied with the results. At the end of a classification, ALCD generates four outputs: a labelled image, a confidence map, a contour map and statistics on the classification. + +An extra step is performed by an operator using the GIMP software, to improve the labelling at a pixel level on hydrological areas (lakes, rivers, salt sea), and outside hydrological areas to correct serious mistakes. + +The classes of the labelled images are then merged into macro-classes, to allow a more robust classifier trained on less classes: + +- Water, mineral turbidity and organic turbidity are merged into water + +| Class | Occurrences | +|--------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------| +| Water | 49637 (62 %) | +| Snow or ice | 20915 (26 %) | +| Other features | 9448 (12 %) | + +**2- Training of the classifier** + +The classifier is now trained on the following indices, computed for the four scenes of the training database: + +- Binarised NDVI +- Binarised NDSI +- Binarised NDWI +- Standard deviation of blue gradient (B2) +- Bounded B11 +- Slope, which is static for a given tile + +**3-Testing of the classifier** + +The performances of the classifier are tested on 27 Sentinel-2 L2A images, described in Table 8. These images were labelled in the same way as the training images, into 3 thematic classes. + +| | | | +|-----------------------------------------|----------------------------------------------------------------------------|-----------------------------------------------------------------------------------| +| 37WDN | 2020/02/20 | White Sea | +| 33VVF | 2020/02/24 | Vänern Lake | +| 34WFT | 2020/02/28 | Gulf of Bothnia | +| 48UWC | 2020/04/10 | Baïkal Lake | +| 48UVC | 2020/04/10 | Baïkal Lake | +| 19UDP | 2021/02/21 | St-Laurent River | +| 18FXK | 2021/03/03 | Argentino Lake | +| 46SBA | 2021/04/04 | Tibet | +| 45SYR | 2021/04/04 | Tibet | +| 14UNC | 2021/05/08 | Winnipeg Lake | +| 42QXM | 2021/04/25 | Rahn Kutch | +| 36SWH | 2021/07/12 | Tuz Lake | +| 30TXR | 2021/07/17 | Garonne estuary | +| 31TGM | 2019/06/29 | Leman Lake | +| 36MUC | 2021/07/23 | Victoria Lake | +| 16UDU | 2019/02/18 | Superior Lake | +| 17TML | 2019/02/19 | Georgia Bay | +| 34WET | 2020/02/06 | Gulf of Bothnia | +| 21MXT | 2020/07/28 | Rio Tapajos | +| 33PVQ | 2020/12/24 | Tchad Lake | +| 33VVF | 2021/02/13 | Vänern Lake | +| 45SXR | 2021/10/14 | Tibet | +| 21HWB | 2021/06/14 | Rio de la Plata | +| 20JNM | 2021/07/23 | Mar Chiquita | +| 38UQE | 2021/04/14 | Volga River | + +| Other features | 327103 (61 %) | +|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------| + +The results of the comparison between the classifier results and the test images are collected in a confusion matrix (Table 11 - page 41), whose definition is given in Table 10. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 10. Confusion matrix definition +::: + +```{=html} +
| + | RF Classifier prediction | +|||
| Other features | +ice | +Water | +||
| Expert label |
+ Other features | +n11 | +n12 | +n13 | +
| Snow and ice | +n21 | +n22 | +n23 | +|
| Water | +n31 | +n32 | +n33 | +|
label** | | | | +|----------------------------------------------------------------------------------------------------------|-------------------------------|-------------------------------|-------------------------------| +| Ice | 0.04 | 0.90 | 0.06 | +| Water | 0.02 | 0.05 | 0.93 | + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 12. Classification report of the Random Forest classifier used in WIC S2 product +::: + +```{=html} +
| + | Precision | +Recall | +F1-score | +Support | +Accuracy | +
| Other features | +0.98 | +0.91 | +0.94 | +327103 | +0.91 | +
| Ice | +0.80 | +0.90 | +0.85 | +119567 | +|
| Water | +0.85 | +0.93 | +0.89 | +93330 | +|
| macro avg | +0.88 | +0.91 | +0.89 | +540000 | +|
| weighted avg | +0.92 | +0.91 | +0.91 | +540000 | +
Preference order is as follows: cloud or cloud shadow > open water > snow-covered or snow-free ice > other features > no data | +| WIC-QA | From both WIC S2 input products, the WIC-QA value from the product with the WIC value with the highest preference order is kept.
Preference order is as follows: cloud or cloud shadow > open water > snow-covered or snow-free ice > other features > no data | +| QAFLAGS | From both WIC S2 input products, the QAFLAGS value with the highest preference order is kept.
Preference order is as follows: any activated bit or bits > no activated bit. | + +The WIC S1+S2 product uses Sentinel-2 data as the primary source, with Sentinel-1 data as a complement. To match the WIC S2 product, the WIC S1+S2 product has a 20 metre pixel spacing. Since the WIC S1 product has a 60 metre pixel spacing, it is resampled to 20 metres using the GdalWarp 'average' method. + +**Step 4. Processing: Merging of the input WIC products** + +- If there are two WIC S1 products, corresponding to two different acquisitions for the day, they are merged separately with the WIC S2 product of the day. For each couple of input product (one WIC S1, one WIC S2), the following merging policy is applied for each pixel to create the WIC layer of the output WIC S1+S2: + - If the WIC S2 information is 'open water', 'snow-covered or snow-free ice' or 'other features', this information is kept. + - If the WIC S2 information is 'cloud or cloud shadow', it is replaced with the WIC S1 classification if it is 'open water' or 'snow-covered or snow-free ice'. + - If the WIC S2 information is 'no data', it is replaced with WIC S1 classification. + +**Step 5. Post-processing: Quality Assessment** + +The QAFLAGS and WIC-QA layers of the WIC S1+S2 output product are derived from the input WIC S1 and WIC S2 QAFLAGS and WIC-QA layers. + +For a detailed description of the QAFLAGS and WIC-QA layers of the WIC S1 product, see Section 6.2.7. + +For a detailed description of the QAFLAGS and WIC-QA layers of the WIC S2 product, see Section 5.2.7. + +First, a new flag is created in order to indicate, at pixel level, from which input product the classification comes from. This source_data_flag is active when the pixel information comes from the WIC S1 input product, and is inactive when it comes from the WIC S2 input product. + +Then, all the QAFLAGS corresponding to the source of the data are copied into the WIC S1+S2 QAFLAGS layer. Consequently, as the WIC S1 QAFLAGS layer contains 9 bits, while the WIC S2 QAFLAGS layer contains 7 bits, 2 bits are unused in the WIC S1+S2 QAFLAGS layer when the source_data_flag is inactive. To sum up, the QAFLAGS contains the following classes: + +- When the source_data_flag is active (meaning the classification comes from WIC S1) + - bit 0: temporary water flag + - bit 1: standing or uncategorized water flag + - bit 2: temperature data flag + - bit 3: summer day flag + - bit 4: imperviousness flag + - bit 5: tree cover density flag + - bit 6: grassland flag + - bit 7: wind speed data flag + - bit 8: windy flag + - bit 9: data source +- bit 5: tree cover density flag +- bit 6: grassland flag +- bit 7: unused +- bit 8: unused +- bit 9: data source + +The QA layer of the WIC S1+S2 output product is created following the same approach: if the pixel information comes from the WIC S1 input product, the WIC S1 QA value is assigned to this pixel, and likewise for the WIC S2. All flags are set to zero outside of the detection area. + + + +The WIC S1+S2 product heavily depends on the revisit rate and overlapping occurrences of S1 and S2. The 'cloud and cloud shadow' and 'no data' classes derived from S2 observations cannot always be completely replaced by S1 data, particularly when the overlap between the two products is partial. As a result, gaps in the WIC S1+S2 thematic information may still occur. + +**Rapidly changing ice cover** + +Since the WIC S1 and WIC S2 data used for the WIC S1+S2 product are acquired at different times of the day, the combined product's quality may decrease in certain situations. This is particularly true when weather and environmental conditions change rapidly between the two acquisition times. Rapidly melting ice, or ice moving quickly due to currents, can also lead to discrepancies between the two input products. This issue is more relevant to river ice, as lake ice is generally less prone to movement. + +## Quality assessment + +As part of the HR-WSI project, an independent assessment of the HR-WSI WIC S1+S2 products will be carried out over a dozen European Sentinel-2 tiles, covering the period 2020-2021. This particular ensemble of sites was selected to represent a variety of topographies, climates and land cover types and for which auxiliary validation datasets are available. Results will be then made available in a validation report. + +The Aggregated Water and Ice Cover (AWIC) information is stored in a geodatabase and accessible from a REST API. It describes the surface coverage of lakes and rivers defined by the EU-Hydro database (described in Section 2.5) and is a spatial aggregation of the Water and Ice Cover products (WIC S1, WIC S2 and WIC S1+S2) described in Sections 5, 6, and 7. It provides the percentage of open water, snow-covered or snow-free ice, as well as other categories (clouds, radar shadow / layover / foreshortening, other features and no data), for individual lakes and over 10 km longitudinal sections of rivers for each acquisition date. + + + +The AWIC geodatabase is fed daily from the input WIC products computed on this day. For each day, the source Water and Ice Cover product information (WIC S1, WIC S2, WIC S1+S2) used for + +each basin, river division and kilometre numeration are done beforehand (more details in Section 2.5). The percentage of coverage for each class from the WIC products is then calculated for each hydrographic element (individual lakes or 10 km river sections) for every WIC product. Finally, the statistical information for these hydrographic elements is stored in a PostGIS geodatabase [AUX29]. + +The procedure to use the REST API to access the geodatabase and examples of applications of AWIC products can be found in the product user manual [AD3]. + +## Retrieval algorithm + +### Outline + +The AWIC geodatabase is updated daily. The latest WIC products generated from Sentinel-1 (WIC S1) and Sentinel-2 (WIC S2) observations are identified alongside combined products (WIC S1+S2). Each input WIC product is then spatially aggregated on lakes or 10 km long river segments at each river basin level. The resulting statistics indicate the percentage coverage of each class from the WIC products of the day. These statistics are eventually added to the geodatabase. + +Additionally, complementary static information about the hydrographic network is stored within the persistent geodatabase. It consists of: + +- Polygon vector data circling the river basin or lake basin (AOI) +- River or lake name +- River kilometre from the mouth (only for rivers) +- Area of the lake (only for lakes) +- River segment (only for rivers) + +### Assumptions + +As the AWIC product is generated from the WIC S1, WIC S2 and WIC S1+S2 datasets, the basic underlying assumptions are those of WIC S1 (see Section 6.2.2.), WIC S2 (see Section 5.2.2) and WIC S1+S2 (see Section 7.2.2.). + +### Related and existing applications + +**Lake Ice Extent from Copernicus Monitoring Land Service - global component (LIE)** + +The Lake Ice Extent in the Copernicus Global Land Service is an ice product derived from optical satellite imagery at 250 m resolution. While the algorithm was initially developed using MODIS, it has since been further refined to operate with data from the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on NOAA-20 (JPSS-1), which is currently in use. The classification is pixel-based, using a threshold approach. Cloud-free freshwater bodies are classified into three classes: fully snow-covered ice, partially snow-covered ice/clear ice, and open water. The + +The ICE algorithm for AWIC is configured to use the following input data: + +- All available WIC S1, WIC S2, and WIC S1+S2 products processed by the HR-WSI system. More precisely, for each day and each S2 tile, all the available WIC S1, WIC S2 and WIC S1+S2 products are used. +- Polygon layers - river segments and lakes - created from the EU-Hydro database (see Section 2.5) and covering the 33 European river basins. + +### Output data + +AWIC geometries are stored in a PostGIS geodatabase [AUX29] format in the European ETRS89 LAEA coordinate system (EPSG: 3035). After the statistics computation, the geodatabase is filled with the spatially aggregated data on the geometries, individual lakes and 10 km long river sections, for each EU-Hydro river basin. + +Each record in the AWIC product refers to a particular geometry feature (lake or river section) and is assigned with the percentage of coverage of each class derived from the relevant WIC S1/WIC S2/WIC S1+S2 product (as detailed in Section 8.2.7.): + +- Open water, +- Snow-covered or snow-free ice, +- Cloud or cloud shadow, +- Radar shadow / layover / foreshortening, +- Other features (land, vegetation, salt sea, other), +- No data. + +Three columns give information about the source of the aggregated data: + +- Type: indicates if the AWIC is derived from S1, S2 or both S1 and S2 data. +- Percentage of S1 pixels: shows the percentage of exploitable pixels (i.e. excluding no data pixels) derived from an S1 observation. +- Percentage of S2 pixels: shows the percentage of exploitable pixels (i.e. excluding no data pixels) derived from an S2 observation. + +For each AWIC, a quality value (QA) is also computed (see Section 8.2.7.). AWIC information is also delivered with metadata. + +Users should refer to the product user manual for ice products [AD3] for a complete description of the AWIC product format. + +### Methodology + +All processing is done with GDAL [AUX30] and is presented in Figure 15 (page 63). + +First, a subset of the EU-Hydro river basins has been processed according to the logic exposed in Section 8.2.5. + +- Import the EU-Hydro river basins intersecting the Sentinel-2 tiles of harvested WIC products. Several EU-Hydro river basins can intersect a single Sentinel-2 tile. +- If necessary, merge all WIC S2 products relating to the same date and tile. If the two products overlap, a fusion approach is applied to the classification results. It is described in the WIC S1+S2 section, in particular Section 5.2.7., subsection "Preparation of the input WIC products". + +**WIC data merging** + +- Merge WIC products covering the EU-Hydro river basin into one raster, for each possible group of WIC products (from WIC S2, WIC S1 morning, WIC S1 afternoon, WIC S1+S2 morning, WIC S1+S2 afternoon). If the tiles overlap, a fusion approach is applied. Table 14 presents the merging policy applicable when two products of the same type overlap. If the resulting raster does not cover all objects from the river basin, the area for which the WIC is not provided is set to the 'no data' class. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 14. Overlapping rules in fusion of input WIC products +::: + +| Layer | Merging policy | +|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| WIC | Values are set by preference order: cloud or cloud shadow > open water > snow-covered or snow-free ice > other features > no data | + +**Statistics computation** + +- Compute coverage statistics for all the previously retrieved EU-Hydro river basin geometry according to the following formulas: + - water_perc = (open water/N) \* 100(%), + - ice_perc = (snow-covered or snow-free ice/N) \*100(%), + - other_perc = (other features/N) \*100(%), + - cloud_perc = (clouds/N) \*100(%), + - shadow_perc = (radar shadow/N) \*100 (%) + - nd_perc = (no_data/N) \*100(%), + +where + +- N is the number of pixels within a particular geometry, +- pen water is the number of pixels classified as 'open water' within a particular geometry, +- snow-covered or snow-free ice is the number of pixels classified as 'snow-covered or snow-free ice' within a particular geometry, +- other features is the number of pixels classified as 'other features' within a particular geometry, +- clouds is the number of pixels classified as 'clouds' within a particular geometry + +particular geometry. + +- Compute the percentage of pixels coming from S1 (respectively S2) observation for exploitable pixels (i.e. excluding no data pixels), according to the following formulas: + - s1_perc = (s1 pixels/N_exploitable) \*100 (%), + - s2_perc = (s2 pixels/N_exploitable) \*100 (%) + +where + +- s1 pixels = number of pixels from an S1 observation +- s2 pixels = number of pixels from an S2 observation +- N_exploitable = number of exploitable pixels (N - no data) +- Calculate the QA according to the following formula: + `AWIC_confidence = mean(WIC_confidence_level)`, + where the WIC_confidence_level is the QA value for each pixel, coming from the input WIC product (see the "Methodology" sections associated with the input product). + +The four QA possible values for the AWIC product are as follows. + +- 0 if 0.5 > AWIC_confidence ≥ 0 indicating high quality +- 1 if 1.5 > AWIC_confidence ≥ 0.5 indicating medium quality +- 2 if 2.5 > AWIC_confidence ≥ 1.5 indicating low quality +- 3 if 3.0 ≥ AWIC_confidence ≥ 2.5 indicating minimal quality + +- Assign coverage statistics and date of source image acquisition to the AWIC geodatabase. + + + +### Limitations + +Misclassification errors in the WIC S1 and WIC S2 products directly propagate into the cover fractions of the AWIC product. More details regarding WIC S2, WIC S1, and WIC S1+S2 limitations can be found in sections ‘Limitations' 5.2.9., 6.2.8., and 7.2.8. respectively. + +## Quality assessment + +As part of the HR-WSI project, an independent assessment of the HR-WSI AWIC data will be carried out over a dozen European Sentinel-2 tiles, covering the period 2020-2021. This particular set of sites was selected to represent a variety of topographies, climates and land cover types and for which auxiliary validation datasets are available. Results will be then made available in a validation report. + +The Ice Cover Duration (ICD) product provides the duration of the ice season on an annual basis at 20m resolution. The metric is over a hydrological year (01 September to 31 August) at European level, on inland water pixels within the EEA38+UK area. It is derived from a time series of binary ice cover maps, derived from WIC S1, WIC S2 and WIC S1+S2, using the Let It Snow software (LIS). LIS is originally designed and developed for snow cover processing and temporal aggregation, but the linear interpolation part of the algorithm which ICD uses is identical. As such, the input products, WIC S1, WIC S2 and WIC S1+S2 are converted to FSC-like intermediate products to be used as input to LIS software. Output of LIS software is then converted to ICD main layer and QA layers are processed separately. Additionally, the product is masked with a water mask, derived annually from the HR-WSI Water Cover Duration (WCD) product. The source of this water mask may vary if the WCD was unavailable during processing due to the initialization of the production; further details can be found in Section 2.4. to include only inland water pixels and ensure consistency between the ice products. + +LIS software was developed by CESBIO and CNES. The code is open source under Apache licence and available from a Gitlab repository [AUX31]. + +Note that ICD products are delivered in various projections/pixel spacing definitions. For more details, readers are referred to Section 10. + +## Retrieval algorithm + +### Outline + +The module collects all ice cover observations over a given tile and time period, in this case one year. For each day, which products to be used is decided according to their availability and if necessary, multiple products are merged into one. For each pixel, a linear interpolation along the time axis is performed to obtain a continuous time series at a daily time step. From this time series the total number of ice days is computed (ICD). Then, the water mask is applied, to limit the ICD extent to inland waters within EEA38+UK area. + +### Assumptions + +The algorithm assumes that a daily time series of ice cover absence or presence can be reconstructed by linear interpolation from irregularly observed ice cover maps. + +### Related and existing applications + +CLMS HRWSI Snow Phenology product uses the same application on snow cover maps from S2 only and S1 and S2 observations [AD2]. + +### Alternative methodologies + +A similar method of ice phenology extraction on lakes by thresholding SAR based backscatter thresholding but obtaining the thresholds by analysing time series of average backscattering coefficients is studied by Murfitt and Duguay (2020). The method extracts ice-off and ice-on dates, which then can be used to derive the duration of the ice cover. On the other hand, the method uses SAR data directly, rather than ice cover products. + +### Input data + +The ICD algorithm is configured to use the following input data. + +- HR-WSI Water and Ice Cover (WIC) products covering the hydrological year, defined from 1st of September to the 31st of August of the following year. Additional data are required before the beginning and after the end of the hydrological year in order to manage the linear interpolation at the edges of the period. Margins are set to 30 days in order to maximise the probability to have at least 1 observation for all the tile pixels (see Section 9.2.7 - data collection) + - WIC S1 (Water and Ice Cover, based on Sentinel-1 data), + - WIC S2 (Water and Ice Cover, based on Sentinel-2 data), + - WIC S1+S2 (Water and Ice Cover, based on Sentinel-1 and Sentinel-2 data). +- Water mask + - Water mask. An inland water mask defines the area over which the ICD is computed. This mask is the same as the one used for the WIC inputs. Users should refer to Section 2.4 to know the source of the water mask for a given WIC S1+S2 product. In most cases, it is derived from the HR-WSI WCD product and reflects conditions of the previous hydrological year. + +### Output data + +Each ICD product is formed of four raster files in Geographic Tagged Image File Format (GeoTIFF) and a metadata file in Extensible Markup Language (XML). The raster files are coded in 16-bit unsigned integers and maintain the projection, extent and resolution of the initial 20m resolution bands of the Sentinel-2 L2A product (UTM/WGS84 with a pixel size of 20m x 20m). + +The list of raster files is as follows: + +- ICD: number of days with ice cover on water covered area +- NOBS1: Number of days with S1 based observations over the hydrological year (data at margin is not included) +- NOBS2: Number of days with S2 based observations over the hydrological year (data at margin is not included) +- ICD-QA: confidence level of the ICD layer from 0 (high quality) to 3 (minimal quality) + +The ICD processing is performed at the tile level. + +**Preprocessing - data collection** + +All the Water and Ice Cover (WIC) products covering the period under consideration are collected. This period is defined through parameters given in a configuration file: + +- `date_start`: the target start date for the beginning of the time range, set to the 1st of September N-1 where N is the hydrological year under consideration +- `date_end`: the target stop date for the end of the time range, set to the 31st of August N + +Additional ice products are collected with a margin of ±30 days outside the requested time range to avoid any extrapolation at the edges of the water year: + +- `Date_margin` is set to 30 days in HR-WSI + +The algorithm does not store the entire content of WIC products, but only the following layers: + +- WIC layer: water and ice extent +- WIC-QA layer: quality for the water and ice extent +- QAFLAGS layer: Quality control flags (only for WIC S1+S2) + +**Data fusion and preference** + +At this point, all the WIC products available for the full time period are gathered. The data fusion step is applied for each day of the time period. There can be several WIC products available for a given date, reporting different ice cover classification for the same pixels. Ice cover status for a given pixel for a given day is decided before the processing. For each day, if any WIC product exists, they are fused and converted to one FSC-like product. During this operation, the module also counts the number of Sentinel-1 and Sentinel-2 observations (NOBS1 and NOBS2) over the hydrological year, according to which sensor type the final value in daily FSC-like products are selected from. + +The decision of which pixel's value is final is made according to the following rules, in hierarchical order, for different cases: + +**Product type for the given day and tile:** + +- If at least one WIC S1+S2 product exists, WIC S1+S2 products are used and WIC S1 and WIC S2 products are discarded. +- If no WIC S1+S2 product exists, then all of the WIC S1 and WIC S2 products are used. + +**Using these product types, for each pixel for the given and and tile:** + +- If the information is available from only one product, it is used. In case of availability from multiple products: + - Observations over gaps: Ice or open water classes are preferred over gap classes (cloud, radar shadow or no data). In case of observations from multiple products: + +After the final values are decided, the daily product is converted to FSC-like products by replacing "ice covered" by “snow”, “open water” by “no snow" and gap classes by "no data." FSC-like product is stored as an intermediate product with an appropriate file name convention for LIS software. + +**Resampling of the input WIC S1 product** + +The ICD, WIC S1+S2 and WIC S2 products have a 20 metre pixel spacing, while the WIC S1 product has a 60 metre pixel spacing. Therefore, if any WIC S1 product is used as input, it is resampled to a 20 metre pixel spacing. As the resampling is done from higher to lower pixel spacing, the nearest neighbour method is used. + +**Masking non-water areas** + +Merged WIC layers for each day considered are masked with the water mask to include only water pixels. This is to ensure consistency between the ice products. + +**Data conversion** + +The interpolation processing will rely on two binary information, ice presence/absence and invalid/valid data. + +The fused WIC layer is converted into binary ice masks `ice_mask`: + +- 0: no ice (when WIC is open water, clouds, radar shadow, other features or no data) +- 1: ice (when WIC is snow-covered or snow-free ice) + +Binary masks `invalid_mask` (cloud/radar shadow/no data) are also created, and indicate the true usability of the acquisition. + +- 0: valid (WIC class is open water, snow-covered or snow-free ice or other features) +- 1: invalid (WIC class is cloud, radar shadow, or no data) + +**Time series interpolation** + +For each pixel of the tile, a linear interpolation is performed on the stack of `ice_mask` to obtain a complete time series with a daily time step. At this point, the interpolated ice mask time series is available (interp_ice_mask). + +**Post-processing** + +For each pixel, the interpolated binary ice mask time series is analysed to derive the ice covered days. The daily binary ice masks interp_ice_mask are stacked and summed into one annual ice map. It results in the ice cover duration (ICD) in days between 0 and 366. + +**Quality layers** + +Sentinel-1 based and Sentinel-2 based observations (NOBS1 and NOBS2) is already computed during the "data fusion" stage. + +The ICD variable is characterised by a confidence index (ICD-QA): + +- 0: High quality +- 1: Medium quality +- 2: Low quality +- 3: Minimal quality + +The resulting quality confidence level is decided according to the ice cover duration and intervals of number of days with exploitable observations (NOBS = NOBS1 + NOBS2); + +- ICD-QA = 3 if NOBS < 40 or ICD < 30 +- ICD-QA = 2 if 40 <= NOBS < 80 and ICD >= 30 +- ICD-QA = 1 if 80 <= NOBS < 120 and ICD >= 30 +- ICD-QA = 0 if 120 <= NOBS and ICD >= 30 + +### Limitations + +The LIS algorithm was developed for snow phenology and specifically for mountain ranges and therefore the ice phenology based on S1 and S2 observations should be treated with caution. + +The quality of the interpolation is highly dependent on the number of usable observations over the hydrological year, and more specifically on the sampling of the observations contributing to the time series. As a consequence, missing observations due to cloud cover, radar shadow or no data cause interpolation artefacts by increasing the time gap between two consecutive actual observations. This effect is particularly important in polar regions where optical acquisitions are absent for a period of time. + +The quality of the interpolation is also dependent on the quality of the ice products used in the time series, any wrong ice detection will indifferently be interpolated and will propagate the error. + +The ICD product is not intended for studying short-term extreme ice events, as it employs a linear interpolation approach over the entire hydrological year. + +## Quality assessment + +As part of the HR-WSI project, an independent assessment of the HR-WSI ICD products will be carried out over a dozen European Sentinel-2 tiles, covering the period 2020-2021. This particular set of sites was selected to represent a variety of topographies, climates and land cover types and for which auxiliary validation datasets are available. Results will be then made available in a validation report. + +The resampled products are detailed in Table 15 and the LAEA grid is shown in Figure 17. + + + + + +| | | +|--------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------| +| ICD product defined on S2 tiling grid (110km x 110 km), in the UTM/WGS84 projection (EPSG:326xx\*), with a pixel size of 20m x 20m | km), in the LAEA projection (EPSG:3035), with a pixel size of 20m x 20m | +| | ICD product defined on the European tiles (100km x 100 km), in the LAEA projection (EPSG:3035), with a pixel size of 100m x 100m | + +Reprojection and resampling are performed using the *warp* method of the GDAL library and requires that all the native products are generated and available over the entire EEA38+UK. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 16. List of the abbreviations and acronyms +::: + +| Abbreviation | Name | Reference | +|---------------------------------------|----------------------------------------------------------------------|-------------------------------------------------------------------------------------------| +| **ALCD** | Active Learning for Cloud Detection | | +| **API** | Application Programming Interface | | +| **CDSE** | Copernicus Data Space Ecosystem | https://dataspace.copernicus.eu/ | +| **CLMS** | Copernicus Land Monitoring Service | https://land.copernicus.eu | +| **CNES** | French Space Study Center | https://cnes.fr/ | +| **DIAS** | Data and Information Access Services | | +| **ECMWF** | European Centre for Medium-Range Weather Forecasts | | +| **EEA** | European Environment Agency | www.eea.europa.eu | +| **GIMP** | Image Manipulation Program | https://www.gimp.org/ | +| **HRES** | Highest RESolution model from ECMWF | | +| **HRL-WAW** | High Resolution Layers - Water & Wetness | https://land.copernicus.eu | +| **HRWL** | High Resolution Water Layer | | +| **LAEA** | Lambert azimuthal equal-area projection | | +| **NDSI** | Normalised Difference Snow Index | | +| **NDVI** | Normalised Difference Vegetation Index | | +| **NDWI** | Normalised Difference Water Index | | +| **QA** | Quality assessment | | +| **QGIS** | Geographic Information System software | https://www.qgis.org/ | +| **REST** | REpresentational State Transfer | | +| **RLIE S1 / RLIE S2** | HR-S&I River and Lake Ice Extent based on Sentinel-1 / Sentinel-2 | https://land.copernicus.eu/ | +| **SAR** | Synthetic aperture radar | | +| **S1/S2** | Sentinel-1/Sentinel-2 | | +| **UK** | The United Kingdom | | +| **UTM** | Universal Transverse Mercator | | +| **WCD** | Water Cover Duration | | +| **WEKEO** | WEKEO European cloud infrastructure (DIAS) | | +| **WGS84** | World Geodetic System 1984 | | + +with a Supervised Active Learning Procedure, Remote Sens., 11, 433, https://doi.org/10.3390/rs11040433, 2019. + +Chu, T., Das, A., and Lindenschmidt, K.-E.: Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study, Remote Sens. 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E.: Improvement and expansion of the Fmask algorithm: + +```{=typst} +#set page(flipped: true, paper: "a3") +#set text(size: 8pt) +``` + +| Id. | Document | +|-------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **AUX1** | European Environment Information and Observation Network (Eionet), https://www.eionet.europa.eu/, accessed: 09/2024 | +| **AUX2** | PIANetary Data Access catalogue - Copernicus Space Component Data Access by ESA/EC https://panda.copernicus.eu/web/cds-catalogue, accessed: 01/2024 | +| **AUX3** | Copernicus DEM: EEA-10, (DGED), dataset ID: COP-DEM_EEA-10-DGED https://panda.copernicus.eu/web/cds-catalogue, accessed: 01/2024 | +| **AUX4** | Copernicus DEM: GLO-30, (DGED), dataset ID: COP-DEM_GLO-30-DGED https://panda.copernicus.eu/web/cds-catalogue, accessed: 01/2024 | +| **AUX5** | HRL Tree Cover Density (TCD), 10m, 2018. E.U. Copernicus Land Monitoring Service (CLMS). https://doi.org/10.2909/486f77da-d605-423e-93a9-680760ab6791. https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density/tree-cover-density-2018, accessed: 02/2024 | +| **AUX6** | HRL Imperviousness Density (IMD), 10m, 2018. E.U. Copernicus Land Monitoring Service (CLMS). https://doi.org/10.2909/3bf542bd-eebd-4d73-b53c-a0243f2ed862. https://land.copernicus.eu/en/products/high-resolution-layer-imperviousness/imperviousness-density-2018, accessed: 02/2024 | +| **AUX7** | HRL Grassland (GRA), 10m, 2018. E.U. Copernicus Land Monitoring Service (CLMS). https://doi.org/10.2909/60639d5b-9164-4135-ae93-fb4132bb6d83. https://land.copernicus.eu/en/products/high-resolution-layer-grassland/grassland-2018, accessed: 02/2024 | +| **AUX8** | HRL Water Layer (WL), 10m, 2018. E.U. Copernicus Land Monitoring Service (CLMS). https://doi.org/10.2909/7992f641-bf77-47b7-b0c1-74fc832b78b1. https://land.copernicus.eu/en/products/high-resolution-layer-water-and-wetness/water-and-wetness-status-2018, accessed: 02/2024 | +| **AUX9** | CLMS, EU-Hydro database (version 1.0) https://land.copernicus.eu/en/products/eu-hydro/eu-hydro-river-network-database, accessed 10/04/2024 | +| **AUX10** | Atmospheric Model high resolution 10-day forecast (Set I - HRES), https://www.ecmwf.int/en/forecasts/datasets/set-i, accessed: 09/2024 | + +```{=typst} +#set page(flipped: false, paper: "a4") +#set text(size: 11pt) +``` + +https://www.theia-land.fr/en/snow-ice/ Data delivery through https://theia.cnes.fr/; https://catalogue.theia-land.fr/ and soon on https://hydroweb.next.theia-land.fr/, accessed: 01/02/2024 +| **AUX13** | A. Zupanc, Improving Cloud Detection with Machine Learning, https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13 (2017),, accessed: 14/02/2024 | +| **AUX14** | Sentinel-2 Level-2A Algorithm Theoretical Basis Document. Louis, J. (2021), https://step.esa.int/thirdparties/sen2cor/2.10.0/docs/S2-PDGS-MPC-L2A-ATBD-V2.10.0.pdf, accessed: 09/2024 | +| **AUX15** | Sentinel-2 Products Specification Document, https://sentinel.esa.int/documents/247904/685211/sentinel-2-products-specification-document, accessed: 14/02/2024 | +| **AUX16** | MAJA GIPP parameter files repository: https://gitlab.orfeo-toolbox.org/maja/maja-gipp2/-/tree/master/SENTINEL2?ref_type=heads, accessed: 14/02/2024 | +| **AUX17** | MAJA Sentinel-2 L2A product description https://theia.cnes.fr/atdistrib/documents/PSC-NT-411-0362-CNES_01_00_SENTINEL-2A_L2A_Products_Description.pdf, accessed: 10/03/2020 | +| **AUX18** | Sentinel-2 MultiSpectral Instrument (MSI) user guide and spectral bands definition, https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial, accessed: 14/02/2024 | +| **AUX19** | Copernicus Atmosphere aerosol product used in MAJA for THEIA production: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts?tab=overview, accessed: 14/02/2024 https://ads-beta.atmosphere.copernicus.eu/datasets/cams-global-atmospheric-composition-forecasts?tab=overview, accessed: 09/09/2024 | +| **AUX20** | Sentinel Application Platform, Sentinel toolbox, https://step.esa.int/main/download/snap-download/, accessed: 02/2025 | +| **AUX21** | Sentinel-1 SAR Technical Guide, https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar, accessed: 07/2024 | +| **AUX22** | Copernicus Pan-European High Resolution Snow and Ice Monitoring: River and Lake Ice Extent based on Sentinel-2, 20m, 2021. E.U. Copernicus Land Monitoring Service (CLMS). Algorithm Theoretical Basis Document, version 2.6 (COSIMS-DT-063-MAG_ATBD_ICE_2.6) https://land.copernicus.eu/en/products/water-bodies/high-resolution-river-and-lake-ice-extent, accessed: 09/2024 & on request to the CLMS service desk. | +| **AUX23** | Copernicus Global Land Operations, Lake Ice Extent, Algorithm Theoretical Basis, https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/CGLOPS2_ATBD_LIE-250m-V1_I1.02.pdf, access: 2024-03-05 | + +```{=typst} +#set page(flipped: true) +#set text(size: 9pt) +``` + +| **AUX25** | CNES ALCD software: https://github.com/CNES/ALCD, access: 2024-04-11 | +|--------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **AUX26** | Scikit-learn Python module for machine learning, https://scikit-learn.org/dev/modules/generated/sklearn.metrics.classification_report.html, accessed on 11/2024 | +| **AUX27** | Copernicus Pan-European High Resolution Snow and Ice Monitoring: River and Lake Ice Extent based on Sentinel-1, 20m, 2020. E.U. Copernicus Land Monitoring Service (CLMS). Algorithm Theoretical Basis Document, version 1.3 (COSIMS-DT-126-MAG_ATBD_ICE_S1_1.3) https://www.eea.europa.eu/en/datahub/datahubitem-view/b5c68a06-5dcf-42e5-baad-94f861189f91, https://land.copernicus.eu/en/products/water-bodies/high-resolution-river-and-lake-ice-extent, accessed: 09/2024 & on request to the CLMS service desk. | +| **AUX28** | Products and services for improvement hydrographic network management in Poland https://eo4sd-eastern.eu/portfolio/product/river-ice-monitoring.html, access: 2024-03-05 | +| **AUX29** | PostGIS extension for PostgreSQL databases to support geospatial data, https://postgis.net/, accessed on 09/2024 | +| **AUX30** | GDAL library, https://gdal.org/en/latest/index.html, accessed on 11/2024 | +| **AUX31** | Let-It-Snow code Gitlab repository, https://gitlab.orfeo-toolbox.org/remote_modules/let-it-snow, accessed: 07/03/2025 | + +```{=typst} +#set page(flipped: false, paper: "a4") +#set text(size: 11pt) +``` + +also explains the rationale behind the elimination of unusable polygons from the processing and why this process requires a large amount of manual effort and cannot be fully automated. + +**Merged polygons of several rivers** + +The main issue is that splitting rivers (which are mainly represented as one polygon) cannot be performed automatically because meanders of rivers cause those small polygons to be unpredictably separated from the main river course. Therefore, it is difficult to retrieve a polygon containing only one river. + + + +**Inconsistent and/or incomplete attribute data for specific river geometry selection** + +There is no dictionary or code list which allows for the automatic selection of a single river course. The most promising attribute (CGNELIN) has many different values when the river is divided into many branches (Figure 19). According to the EU-Hydro user guide, some of the attributes (CGNELIN, ERM_ID etc.) within the dataset are described as 'Incomplete, not updated' or 'empty'. + + + +**Lack of river names** + +The name of any geographical object is one of the most valuable piece of information for any user and it could be useful for the river course selection as described above as well as accompanying any AWIC record. In the current state of EU-Hydro development, the names of the rivers should be added manually. + +**Short estuary sections of tributary rivers are merged with the main river course** + +This situation occurs when particular tributary rivers are gathered mainly as linear features only, but their mouths are represented by polygon features and are usually the part of the main river. This issue causes inaccurate calculation of statistics and to avoid this, all tributary mouths included in the main river polygon features should be eliminated manually (Figure 20). + + + + + + \ No newline at end of file diff --git a/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1-media/img-061becf2e843480e6c8eff117b39aed4.png b/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1-media/img-061becf2e843480e6c8eff117b39aed4.png new file mode 100644 index 00000000..9d242776 Binary files /dev/null and b/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1-media/img-061becf2e843480e6c8eff117b39aed4.png differ diff --git a/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1-media/img-1ec3d6e380fe7439ca0c3479dd789046.png b/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1-media/img-1ec3d6e380fe7439ca0c3479dd789046.png new file mode 100644 index 00000000..fd7560a4 Binary files /dev/null and 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files /dev/null and b/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1-media/img-f770bcedbbb18535f245fc8051a8a75b.png differ diff --git a/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1.qmd b/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1.qmd new file mode 100644 index 00000000..70bbcb12 --- /dev/null +++ b/DOCS/products/HRL_Vegetation_Layers_Comparative_Analysis_v1.qmd @@ -0,0 +1,2363 @@ +--- +title: 'Task 2: Product development support - HRL vegetated' +subtitle: 'Negotiated procedure EEA/DIS/R0/24006 Copernicus 2024/2025' +date: '2025-04-15' +category: products +--- + +# Background + +The new High-Resolution Layer (HRL) production on vegetated land cover has been ongoing since 2022. HRL Vegetated is a complex product comprising HRL Crop Types, HRL Grasslands, and HRL Forest, each with several sub-layers. In preparation for the continuation of HRL Vegetated Land Cover Characteristics (VLCC) for the reference year 2025 onwards, the European Environment Agency (EEA) foresees the potential need for product definitions and methodologies updates. This task is aimed at supporting the EEA in the evolution of the HRL VLCC products. + +# Assignment of this task and specific actions + +- **ST1: Assessment of Similarities and Differences** + + This subtask aims to explore various grassland mapping initiatives in Europe and compare them with HRL VLCC products. The goal is to identify similarities, differences, and potential improvements for future product development. + +- **ST2: Collection of User Requirements and Literature Review of Additional Forest Types** + + This subtask will support the EEA in gathering user requirements for extending forest-type classifications within the VLCC Forest dataset and conducting a literature review on current classification methodologies. + +- **ST3: Assessment of Timeseries Consistency of Forest Layers** + + This subtask focuses on assessing the stability and consistency of time series for HRL Forest layers to support indicator development. + +# Key Deliverables + +Overview of grassland datasets and their comparison, proposed forest type classifications based on user requirements and literature review, time series assessment summary and recommendations for improving consistency. + +# Assessment of similarities and differences + +## Overview of the selected initiatives mapping grassland + +The monitoring and mapping of grasslands play a crucial role in sustainable land management, biodiversity conservation, and agricultural policy implementation across Europe and beyond. Grasslands represent a significant land cover type that supports ecosystem services, carbon sequestration, and habitat provision for numerous species (Bengtsson et al., 2019; Peeters, 2009). In recent years, advancements in remote sensing technology and the availability of high-resolution satellite imagery have enabled more detailed and consistent efforts in grassland monitoring. + +European institutions and global organisations have invested in various projects and initiatives to monitor grasslands at different spatial and temporal scales. These initiatives aim to address challenges such as land-use change, grassland degradation, and the need for effective management practices. Grassland data sources, like the High-Resolution Layer Vegetated Land Cover Characteristics (HRL VLCC) Grassland and the EU Grassland Watch (EUGW) project, exemplify how remote sensing and data-driven approaches are being employed to provide accurate and up-to-date information on grassland distribution, productivity, and management. + +This chapter explores the various grassland mapping initiatives and data sources currently being utilised for grassland monitoring and assessment across Europe and globally. The goal is to offer an in-depth understanding of the available datasets, their applications, and the grassland classification, and management methodologies. Special focus is placed on the EU Grassland Watch (EUGW) project and its comparison with the HRL VLCC Grassland dataset, as these two initiatives are important to understanding the current state of grassland mapping at the European level. Focusing on qualitative comparison highlights differences in coverage, accuracy, thematic layers, and practical applications. + +In addition to these primary datasets, several other grassland monitoring initiatives contribute to a broader understanding of grassland distribution and related characteristics such as productivity or management practices. These initiatives utilise different methodologies, ranging from in-situ data collection and field surveys to remote sensing-based or even including predictive modelling, providing valuable datasets for land management, biodiversity conservation, and agricultural practices. Beyond data production, these projects also offer valuable policy insights and research recommendations. Overall, six different initiatives, products, and projects were identified, each offering a unique perspective on grassland mapping and monitoring (Table 1). + +- **EU Grassland Watch (EUGW)** provides high-resolution monitoring of grassland management, productivity, and biodiversity across Natura 2000 sites in Europe. The dataset offers yearly updates dating back to 1994, leveraging both Sentinel and pre-Sentinel satellite data to track grassland dynamics. It focuses on management events, productivity trends, habitat mapping and related biodiversity as well as land cover-based indicators. +- **LUCAS Grassland Module** offers an extensive in-situ dataset based on systematic field surveys conducted across thousands of locations in Europe. The module provides detailed information on grassland management, soil types, and species composition, making it an essential validation source for remote sensing-based classifications. First introduced in 2018 as a pilot, with a more extensive rollout in 2022, LUCAS provides point-based vector data that serves as a ground-truth dataset for European grassland studies. LUCAS data points are already used for validation and training samples in the production of HRL VLCC Grassland data. +- **Global Pasture Watch** delivers 30m-resolution raster data on a global scale, mapping annual pasture extent, livestock density, short vegetation cover, and gross primary productivity. Covering the period 2000-2022, this initiative integrates multiple Earth observation datasets to monitor the dynamics of both natural and managed pasturelands. It may serve as a valuable resource for tracking global grassland changes, particularly in response to climate change, grazing pressures, and land-use conversion. +- **The SUPER-G Project** takes a multidisciplinary approach to sustainable grassland management, integrating remote sensing, field studies, and policy research. Running from 2018 to 2023 under the Horizon 2020 framework, SUPER-G focused on developing sustainable permanent grassland systems by combining 10m-resolution Sentinel-1 and Sentinel-2 data with field-based ecological assessments. Its outputs include policy recommendations, biodiversity assessments, and tools for optimising land management practices. +- **GRASS SIGNAL** is a real-time monitoring and forecasting tool designed for agricultural applications, predicting grass yield, quality, and moisture supply over a five-day forecast period. Using Sentinel-1 and Sentinel-2 data provides insights into biomass productivity, crude protein content, nitrogen uptake, and the energy value of grasslands. GRASS SIGNAL is valuable as an aid tool to farmers and land managers in optimising grazing and forage management while contributing to precision agriculture applications. +- Lastly, the **ESDAC Soil Biomass Productivity Maps** offer a static, model-based assessment of grassland productivity across the EU. These maps, developed by the European Soil Data Centre (ESDAC), evaluate grassland fertility and productivity potential under varying soil, climatic, and topographical conditions. Based on long-term modelling, the dataset (published in 2016) provides 1km-resolution raster data, offering insights into the inherent soil productivity of European grasslands without being influenced by short-term climatic variability. + +These explorations on several mapping initiatives and grassland-related projects may help to identify gaps, opportunities, and best practices for future grassland monitoring activities, supporting the evolution of the HRL VLCC Grassland product. The table below provides an overview of key grassland mapping initiatives and data sources (excluding HRL VLCC Grassland and complex LU/LC datasets like the Global Land Cover-SHARE (GLC-SHARE)). Each source has its unique focus, coverage, and applications, making them useful for different grassland monitoring tasks. The following subchapters briefly introduce the individual initiatives (except for EUGW), including visualizing the data if available, and a more detailed comparison between the EUGW and HRL VLCC Grassland will be then provided. + +Table 1 An overview of different initiatives (except for CLMS HRL VLCC Grassland) that map grassland in Europe and globally. These initiatives cover a broad range of thematic categories, including remote sensing-based grassland mapping, field-based mapping, policy-oriented and research projects, and Al-based monitoring services. + +```{=typst} +#set page(flipped: true, paper: "a3") +#set text(size: 5pt) +``` + +| Source/Initiative/Project | Brief description | Spatial coverage | Temporal coverage | Resolution, Data type | +|-------------------------------------------------------------------------------------------------------------------------------------------------------|------------|------------|------------|------------| +| **EU Grassland Watch (EUGW)**
Source:
[https://cop4n2k.eu/](https://cop4n2k.eu/)
[https://ec.europa.eu/eu-grassland-watch/](https://ec.europa.eu/eu-grassland-watch/) | Monitors grassland management, productivity, and biodiversity within Natura 2000 sites. | Europe (only Natura 2000 sites) | yearly, 1994-present (Sentinel and pre-Sentinel era) | 10m (Sentinel era), 30m (pre-Sentinel era), raster | +| **LUCAS Grassland Module**
Source:
[https://wikis.ec.europa.eu/display/EUPKH/LUCAS+grassland+survey](https://wikis.ec.europa.eu/display/EUPKH/LUCAS+grassland+survey) | Provides indicative information on grassland management sward height and floristic composition collected via an in-situ survey. LUCAS data points are already used for validation and training samples in the production of HRL VLCC. | Europe | 2018 (pilot grassland module, 3000 sites), 2022 (full module, 20000 sites) | Point-based, vector | +| **Global Pasture Watch**
Source:
[https://landcarbonlab.org/about-global-pasture-watch/](https://landcarbonlab.org/about-global-pasture-watch/) | Provides a 30-meter global dataset mapping annually. This includes four deliverables: Pasture, livestock density map, short vegetation map, and gross primary productivity map. | Global | 2000-2022 | 30m, raster | +| **SUPER-G Project**
Source:
[https://cordis.europa.eu/project/id/774124](https://cordis.europa.eu/project/id/774124),
[https://www.super-g.eu/](https://www.super-g.eu/) | A European research initiative focused on developing sustainable permanent grassland systems and policies, combining field data, remote sensing, and modelling outputs to promote biodiversity, productivity, and ecosystem services. | Europe (case studies) | policy-oriented, has been implemented during 2018-2023 (Horizon 2020 project) | policy-oriented, field surveys, recommendations | +| **GRASS SIGNAL**
Source:
[https://business.esa.int/projects/grasssignal](https://business.esa.int/projects/grasssignal) | Service that helps to monitor grass growth. It predicts the yield and quality of grass for a five-day forecast period. | Africa, Australia, USA, UK | 2016 and onwards (considering Sentinel-2 data), weekly predictions | up to 10m (Sentinel-1 and Sentinel-2), raster | +| **ESDAC Soil Biomass Productivity (Grassland Layer)**
Source:
[https://esdac.jrc.ec.europa.eu/content/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european](https://esdac.jrc.ec.europa.eu/content/soil-biomass-productivity-maps-grasslands-and-pasture-coplands-and-forest-areas-european) | A map that provides soil biomass productivity maps, assessing grassland fertility and productivity under varying soil, climatic, and topographical conditions. | Europe (EU27) | 2016 (based on long-term soil productivity modelling) | 1km, raster (GeoTIFF) | + +```{=typst} +#set page(flipped: false, paper: "a4") +#set text(size: 11pt) +``` + +## A closer look into selected initiatives - background, data, products, methods. + +### Global Pasture Watch [^1][^2][^3] + +The Global Pasture Watch research consortium provides high-resolution (30m) annual grassland mapping products covering the period 2000-2022, with planned updates beyond 2022. This collaborative initiative is led by the Land & Carbon Lab in partnership with multiple research institutions, including the World Resources Institute, OpenGeoHub, IIASA, the German Centre for Integrative Biodiversity Research, and Cornell University. The consortium applies state-of-the-art machine learning techniques, earth observation data fusion, and crowdsourced validation to create highly accurate grassland and pasture mapping products. This chapter provides an overview of the available dataset, its methodology, key deliverables, and potential applications with CLMS HRL VLCC Grassland. + +The dataset consists of four core mapping products that integrate multi-source earth observation data and expert validation. This includes the following four key products: + +- **Pasture Class Maps** + + Represented by annual land cover classification maps at 30m resolution identifying natural/semi-natural and cultivated pastures globally. Using a probabilistic classification approach, these maps differentiate grasslands from other land cover types. + +- **Livestock Density Maps** + + Provide per-hectare estimates of livestock density, identifying hotspots of managed and unmanaged grazing areas. These maps support the monitoring of rangeland management, overgrazing, and livestock-related carbon emissions. + +- **Short Vegetation Height Maps** + + Represent annual 30m-resolution vegetation height estimates, helping to identify grassland structure and biomass availability. These maps are derived from IceSat-2 LiDAR data, offering insights into pasture productivity and suitability for grazing. Root mean square error (RMSE) ranges around 2.3m depending on the monitored vegetation type. Vegetation height is expressed as majority value rather than mean or median values. + +- **Gross Primary Productivity (GPP) Maps** + + Represented by bi-monthly carbon flux maps, estimating the amount of carbon fixed by grasslands through photosynthesis. These maps help assess grassland health, productivity, and carbon sequestration potential over time. + + + +The Global Pasture Watch dataset is derived using an integrated remote sensing and machine learning approach (see Figure 1). It combines GLAD Landsat ARD-2, MOD11A2, MCD19A2, and auxiliary datasets (e.g., accessibility, roads, water) to enhance classification accuracy. Over 2.3 million reference samples, visually interpreted from VHR imagery within the dedicated QGIS Fast Grid Inspection plugin, support model training. Two spatiotemporal Random Forest models separately classify cultivated and natural/semi-natural grasslands, ensuring robust differentiation. The models generate annual probability-based maps (2000–2022) at 30m resolution, enabling long-term monitoring of grassland dynamics. + +Furthermore, various maps can be generated using custom algorithms accessible from GitHub (see: [https://github.com/wri/global-pasture-watch/blob/main/ggc-30m/README.md](https://github.com/wri/global-pasture-watch/blob/main/ggc-30m/README.md)), and data produced by the Global Pasture Watch project can also be viewed by the Google Earth Engine app (see: [https://global-pasture-watch.projects.earthengine.app/view/ggc-30m](https://global-pasture-watch.projects.earthengine.app/view/ggc-30m); Figure 2). + + + +In summary, some aspects of Global Pasture Watch can be relevant to the potential further development of CLMS HRL VLCC Grassland data. In particular, methodologies applied to produce the Global Pasture Watch dataset may contribute to enhancing the CLMS HRL VLCC Grassland product by machine learning-based classification. HRL VLCC could benefit from similar probabilistic classification methods, for example, for detecting grassland management practices, which is one of the challenges in grassland mapping within the CLMS portfolio. This information about farming practises of management intensity is sometimes available only in national LPIS data, although it can be very useful for various research related to, for example, productivity monitoring on the pan-European level. Specifically, grazing still remains an important aspect of agricultural grassland management not addressed within the VLCC. Within Global Pasture Watch grazing is inferred from national databases. For Europe this is mainly provided by Eurostat. While this approach is suitable for products with a global scope, it does not provide means to map at higher spatial accuracy underlining the need for in-situ data on livestock density to be used as input for higher resolution mapping within Europe. + +Another interesting product provided by the Global Pasture Watch are the short vegetation height maps. This is a LIDAR derived mapping product indicating dominant sward height. While again this is very interesting global product, the spatial resolution may not be suitable for European applications especially for e.g. such as measuring scrub encroachment. Vegetation height as such remains an important aspect of grassland physiology and structure and remains excluded from the VLCC. + +### SUPER-G Project[^4][^5] + +The SUPER-G project (SUstainable PERmanent Grassland systems and policies) is a five-year European initiative (2018–2023) primarily aimed as a policy-oriented and research-driven initiative rather than a data-providing project like EU Grassland Watch or Global Pasture Watch. It focuses on developing sustainable permanent grassland systems and policies, engaging multiple stakeholders (farmers, policymakers, researchers) to promote biodiversity, ecosystem services, and climate resilience in grassland management. The overall objective of the SUPER-G project is to co-develop sustainable permanent grassland systems and policies with farmers and policymakers that will be effective in optimising productivity whilst supporting biodiversity and delivering several ecological services. The key objectives include: + +- **Co-Development** + + Engage farmers, policymakers, and other stakeholders to collaboratively develop sustainable permanent grassland systems, typology and policies to improve communication and policymaking across Europe. + +- **Ecosystem Services Optimization** + + Conduct a systematic review of grassland multifunctionality, emphasizing their role in providing ecological services like carbon sequestration, biodiversity, and climate resilience benefits. + +- **Benchmarking and Testing** + + Assess the productivity of permanent grasslands across Europe through field data collection and analysis from farm networks and experimental platforms. + +- **Policy Support** + + Develop tools and mechanisms that inform and support policy decisions related to the management of permanent grasslands. Evaluating existing policies and developing recommendations to support sustainable permanent grassland management. + +SUPER-G employs a multi-actor approach involving farmers, land managers, advisors, researchers, and policymakers. The project spans 14 countries across various European biogeographic regions, including the Mediterranean, Atlantic, Continental, Alpine, Pannonian, and Boreal zones (Figure 3). + +```{=typst} +#set page(flipped: true, paper: "a3") +#set text(size: 8pt) +``` + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Figure 3 An example of one of the experimental sites where innovative grassland management options and new technologies, such as spectral and electromagnetic sensors, were tested and evaluated. +::: + +```{=html} +
| SITE | +BIOGEOGRAPHIC REGION | +LEAD ORGAN. | +MANAGEMENT OPTION(S) / TECHNOLOGIES TESTED |
+ MEASUREMENTS | +ES | +TYPE OF INVESTIGATED AGRICULTURE |
+ LIVEST. TYPE | +
| Forage Research Station, Moravian Uplands | +Continental / Pannonian | +MENDU | +Grazing management Fertilisation Species mixes Over-seeding methods |
+ Grass yield and quality Water balance and flows |
+ Food Production Water quality Flood control Erosion Control |
+ Conventional Organic |
+ Dairy Beef |
+
| Category | +Forest Type | +
| 1. Boreal forest | +1.1 Spruce and sprucebirch boreal forest | +
| 1.2 Pine and pinebirch boreal forest | +|
| 2. Hemiboreal forest and nemoral coniferous and mixed broadleavedconiferous forest | +2.1 Hemiboreal forest | +
| 2.2 Nemoral Scots pine forest | +|
| 2.3 Nemoral spruce forest | +|
| 2.4 Nemoral Black pine forest | +|
| 2.5 Mixed Scots pinebirch forest | +|
| 2.6 Mixed Scots pinepedunculate oak forest | +|
| 3. Alpine coniferous forest | +3.1 Subalpine larch-arolla pine and dwarf pine forest | +
| 3.2 Subalpine and mountainous spruce and mountainous mixed sprucesilver fir forest | +|
| 3.3 Alpine Scots pine and Black pine forest | +|
| 4. Acidophilous oak and oakbirch forest | +4.1 Acidophilous oakwood | +
| 4.2 Oakbirch forest | +|
| 5. Mesophytic deciduous forest | +5.1 Pedunculate oak-hornbeam forest | +
| 5.2 Sessile oak-hornbeam forest | +|
| 5.3 Ashwood and oakash forest | +|
| 5.4 Mapleoak forest | +|
| 5.5 Limeoak forest | +|
| 5.6 Maplelime forest | +|
| 5.7 Lime forest | +|
| 5.8 Ravine and slope forest | +|
| 5.9 Other mesophytic deciduous forests | +|
| 6. Beech forest | +6.1 Lowland beech forest of southern Scandinavia and north central Europe | +
| 6.2 Atlantic and subatlantic lowland beech forest | +|
| 6.3 Subatlantic submountainous beech forest | +|
| 6.4 Central European submountainous beech forest | +|
| 6.5 Carpathian submountainous beech forest | +|
| 6.6 Illyrian submountainous beech forest | +|
| 6.7 Moesian submountainous beech forest | +|
| 7. Mountainous beech forest | +7.1 South western European mountainous beech forest (Cantabrians, Pyrenees, central Massif, south western Alps) | +
| 7.2 Central European mountainous beech forest | +|
| 7.3 ApennineCorsican mountainous beech forest | +|
| 7.4 Illyrian mountainous beech forest | +|
| 7.5 Carpathian mountainous beech forest | +|
| 7.6 Moesian mountainous beech forest | +|
| 7.7 Crimean mountainous beech forest | +|
| 7.8 Oriental beech and hornbeamoriental beech forest | +|
| 8. Thermophilous deciduous forest | +8.1 Downy oak forest | +
| 8.2 Turkey oak, Hungarian oak and Sessile oak forest | +|
| 8.3 Pyrenean oak forest | +|
| 8.4 Portuguese oak and Mirbeck's oak Iberian forest | +|
| 8.5 Macedonian oak forest | +|
| 8.6 Valonia oak forest | +|
| 8.7 Chestnut forest | +|
| 8.8 Other thermophilous deciduous forests | +|
| 9. Broadleaved evergreen forest | +9.1 Mediterranean evergreen oak forest | +
| 9.2 Olivecarob forest | +|
| 9.3 Palm groves | +|
| 9.4 Macaronesian laurisilva | +|
| 9.5 Other sclerophlyllous forests | +|
| 10. Coniferous forests of the Mediterranean, Anatolian and Macaronesian regions | +10.1 Mediterranean pine forest | +
| 10.2 Mediterranean and Anatolian Black pine forest | +|
| 10.3 Canarian pine forest | +|
| 10.4 Mediterranean and Anatolian Scots pine forest | +|
| 10.5 AltiMediterranean pine forest | +|
| 10.6 Mediterranean and Anatolian fir forest | +|
| 10.7 Juniper forest | +|
| 10.8 Cypress forest | +|
| 10.9 Cedar forest | +|
| 10.10 Tetraclinis articulata stands | +|
| 10.11 Mediterranean yew stands | +|
| 11. Mire and swamp forest | +11.1 Conifer dominated or mixed mire forest | +
| 11.2 Alder swamp forest | +|
| 11.3 Birch swamp forest | +|
| 11.4 Pedunculate oak swamp forest | +|
| 11.5 Aspen swamp forest | +|
| 12. Floodplain forest | +12.1 Riparian forest | +
| 12.2 Fluvial forest | +|
| 12.3 Mediterranean and Macaronesian riparian forest | +|
| 13. Non riverine alder, birch, or aspen forest | +13.1 Alder forest | +
| 13.2 Italian alder forest | +|
| 13.3 Boreal birch forest | +|
| 13.4 Southern boreal birch forest | +|
| 13,5 Aspen forest | +|
| 14. Plantations and self-sown exotic forest | +14.1 Plantations of sitenative species | +
| 14.2 Plantations of not site native species and selfsown exotic forest | +
| Corine Land Cover | +EEA European Forest Typology | +||
| Level 2 | +Level 3 | +Description | +Category | +
| 3.1 Forest | +3.1.1 Broad-leaved Forest | +Vegetation formation composed principally of trees, including shrub and bush understorey, where broad-leaved species predominate. | +4. Acidophilous oak and oak birch forest | +
| 5. Mesophytic deciduous forest | +|||
| 6. Beech forest | +|||
| 8. Thermophilous deciduous forest | +|||
| 9. Broadleaved evergreen forest | +|||
| 11. Mire and swamp forest (11.2-11.5) | +|||
| 12. Floodplain forest | +|||
| 13. Non riverine alder, birch, or aspen forest | +|||
| 3.1.2 Coniferous Forest | +Vegetation formation composed principally of trees, including shrub and bush understorey, where coniferous species predominate. | +1. Boreal forest | +|
| 2. Hemiboreal forest and nemoral coniferous and mixed broadleaved-coniferous forest | +|||
| 3. Alpine coniferous forest | +|||
| 10. Coniferous forests of the Mediterranean, Anatolian and Macronesian regions | +|||
| 11. Mire and swamp forest (11.1) | +|||
| 3.1.3 Mixed Forest | +Vegetation formation composed principally of trees, including shrub and bush understorey, where neither broad-leaved nor coniferous species predominate. | +7. Mountainous beech forest | +|
| 3.2 Shrub and/or herbaceous vegetation associations | +3.2.4 Transitional woodland/shrub | +Transitional bushy and herbaceous vegetation with occasional scattered trees. Can represent woodland degradation, forest regeneration / recolonization or natural succession | +Within other EFT classes | +
| Within other CLC classes | +14. Plantations and self-sown exotic forest | +||
| EU Ecosystem Typology | +EEA European Forest Typology | +||
| Level 1 | +Level 2 | +Description | +Category | +
| 4. Forest and woodlands | +4.1 Broadleaved deciduous forests | +Woodlands and forests dominated by summer-green non-coniferous trees that lose their leaves in winter. Includes woodland with mixed evergreen and deciduous broadleaved trees, provided that the deciduous cover exceeds that of evergreens. The proportion of conifers should not exceed 25%. | +4. Acidophilous oak and oak birch forest | +
| 5. Mesophytic deciduous forest | +|||
| 6. Beech forest | +|||
| 8. Thermophilous deciduous forest | +|||
| 11. Mire and swamp forest (11.2-11.5) | +|||
| 12. Floodplain forest | +|||
| 4.2 Coniferous forests | +Vegetation formation composed principally of trees, including shrub and bush understory, where coniferous species predominate. The proportion of deciduous trees should not exceed 25%. | +13. Non riverine alder, birch, or aspen forest | +|
| 1. Boreal forest | +|||
| 2. Hemiboreal forest and nemoral coniferous and mixed broadleaved-coniferous forest | +|||
| 3. Alpine coniferous forest | +|||
| 10. Coniferous forests of the Mediterranean, Anatolian and Macronesian regions | +|||
| 11. Mire and swamp forest (11.1) | +|||
| 4.3 Broadleaved evergreen forests | +Forests dominated by broadleaved sclerophyllous or lauriphyllous evergreen trees, or by palms. They are characteristic of the Mediterranean and warm-temperate humid zones. | +9. Broadleaved evergreen forest | +|
| 4.4 Mixed forests | +Vegetation formation composed principally of trees, including shrub and bush understorey, where neither broadleaved nor coniferous species strongly predominate (i.e. <75% deciduous and <75% coniferous trees). | +7. Mountainous beech forest | +|
| 4.5 Transitional Forest and Woodland shrub | +Transitional forests and woodland shrub. Includes vegetation that is always shrubland and areas of temporarily cleared forest (as part of forest management). | +Within other EFT classes | +|
| 4.6 Plantations | +Monoculture plantations or plantations strongly dominated by one or few species of non-European coniferous and broadleaved trees with very sparse or lacking undergrowth, e.g. eucalyptus plantations. Forest stands of single or mixed species consisting of native and/or non-native trees species that have long been established in European ecosystems and have diverse undergrowth typical for forest ecosystems should be classified as part of types 4.1 to 4.4. If not possible to distinguish plantations, these areas should be attributed to the classes 4.1 - 4.4. | +14. Plantations and self-sown exotic forest | +|
2018: Woody Vegetation Mask + Forest Mask
2021: Woody Vegetation Layer (WVL – production in progress) | +| Corine Land Cover (CLC) | 2000, 2006, 2012, 2018 | Vector (raster) product: Forest classes (311, 312, 313, 324) and other classes with significant tree cover, as 141-Green urban areas; 223-Olive groves; 222-Fruit trees and berry plantations; 243-Land principally occupied by agriculture, with significant areas of natural vegetation; 244-Agro-forestry areas | + +## Overview and evolution of main HRL Forest products + +HRL Forest layers are the main focus of this analysis. Note, despite of the name “Forest", these layers are in character more tree cover than forest related, considering terminology described in chapter 3.1. + +HRL Forest layers include three main product types: + +- **Tree Cover Density (TCD)** status product estimates the ratio of the rectangular cell area (0-100%) of the vertical projection of tree crowns to a horizontal earth's surface. The product is created from the reference year of 2018 in 10m (previously 20m) resolution, aggregated in a second step to 100m. +- **Dominant Leaf Type (DLT)** status is a classified product providing the separation of broadleaved and coniferous cover for tree covered (TCD > 0 %) areas. The product is created from the reference year of 2018 in 10m (previously 20m) resolution. Aggregated 100m DLT products were created presenting dominant leaf type classes during HRL2015 update for 2012 and 2015 reference years. From 2018 aggregated 100m resolution Broadleaved Cover Density (BCD) and Coniferous Cover Density (CCD) layers are provided. +- **Forest Type (FTY)** product is derived from primary TCD and DLT data, largely following the FAO forest definition by applying 10% tree cover density threshold and 0.5 ha Minimum Mapping Unit (MMU). Urban trees and trees under predominantly agricultural use are marked (2012 & 2015), from 2018 excluded based on the Forest Type Additional Support layer (FADSL). Aggregated 100m FTY products are created presenting dominant leaf type classes. + +The first status product of the HRL Forest time series dates back to the reference year 2012. The initial three years update period was repeated with yearly update in case of TCD and DLT products from 2018 in frames of the new HRL Vegetated Land Cover Component (VLCC) mapping (products are delivered, but not published yet), FTY status was produced only for the reference year of 2021 and change layers are still produced to represent a 3 years period, currently between 2018-2021. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 15 Overview of HRL Forest status and change layers +::: + +```{=html} +
| Ref. year | +Status | +Change | +||||
| Tree Cover Density | +Dominant Leaf Type | +Forest Type | +Tree Cover Density change | +Dominant Leaf Type change | +Forest Type change | +|
| 2012 | +TCD2012 20m TCD2012 100m |
+ DLT2012 20m DLT2012 100m |
+ FTY2012 20m FADSL2012 20m FTY2012 100m |
+ TCDC1215 100m* TCCM1215 20m |
+ DLTC1215 20m | +- | +
| 2015 | +TCD2015 20m TCD2015 100m |
+ DLT2015 20m DLT2015 100m* BCD2018 100m CCD2018 100m |
+ FTY2015 20m FADSL2015 20m FTY2015 100m |
+ |||
| 2018 | +TCD2018 10m TCD2018 100m |
+ DLT2018 10m BCD2018 100m CCD2018 100m |
+ FTY2018 10m FADSL2018 10m FTY2018 100m |
+ TCCM1518 20m | +DLTC1518 20m | +|
| 2018-2021 (VLCC) | +TCD20xx 10m TCD20xx 100m |
+ DLT20xx 10m BCD20xx 100m CCD20xx 100m |
+ FTY20yy 10m FADSL20yy 10m FTY20yy 100m |
+ TCPC1821 20m | +DLTC1821 20m | +|
| TCD&DLT (xx): 2018(revised), 2019, 2020, 2021; FTY(yy): only 2018 (revised) and 2021 | +||||||
2012 (± 1 year) - reprocessed historical HRL 2012 products | +| **Area mapped** | Countries mapped: EEA39 (current terminology EEA38+UK)
Total area mapped: 5 858 166 sqkm | +| **Unclassifiable (254) area** | 87 431 sqkm for reprocessed historical HRL 2012 products,
734 sqkm for 2015 products; | +| **Spatial resolution** | 20m for primary status products (TCD, DLT, FTY)
20m for Dominant Leaf Type Change (DLTC) product,
100m for Tree Cover Density Change (TCDC) product
100m for aggregated products | +| **Satellite input imagery used** | Primarily Sentinel-2A & Landsat-8 resampled to 20m & ESA DWH VHR 2015 (2015 products);
ESA Data Warehouse HR and VHR data (re-processing of historical 2012 products); | +| **Ancillary data used** | Previous GMES Forest 2012 data;
CLC2012 & HRL Impervious Degree 2012 & 2015 used to exclude trees in urban and agricultural context by creating FAD and FTY 2012 & 2015 layers;
Other ancillary data, as HRL Grassland 2012&2015m Water&Wetness 2012&2015 etc. | +| **Relevant novelties in methodology compared to historical (GMES) HRL data** | Primary layers DLT and TCD at 20m spatial resolution are sharing the same spatial extent for both 2015 and 2012 products;
New change products DLTC (20m) and TCDC (100m) introduced; | + +#### HRL Forest 2018 (reference year 2018)[^10] + +The HRL Forest with reference year 2018 (± 1 year) has been fully produced by a consortium of European service providers. Significant novelty was the resolution upgrade to 10m in case of primary status layers and the introduction of a set of additional expert layers (e.g. Confidence Layers). The specification of change layers was slightly updated, new Tree Cover Change Mask layer in 20m resolution was introduced to the portfolio and additionally created for previous 2012-2015 period as well. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 17 Overview of key parameters of products created in the frames of HRL Forest 2018 survey +::: + +| Parameter | Description | +|---------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------| +| **Reference years** | 2018 (± 1 year) | +| **Area mapped** | Countries mapped: EEA39 (current terminology EEA38+UK), slightly extended compared to HRL 2015 area
Total area mapped: 5 911 062 sqkm | +| **Unclassifiable (254) area** | none | +| **Spatial resolution** | 10m for primary status products (TCD, DLT, FTY)
20m for Dominant Leaf Type Change (DLTC) & Tree Cover Change Mask (TCCM),
100m for aggregated products | +| **Satellite input imagery used** | Primarily Sentinel-2A & Sentinel-2B L2A-data from the reference year 2018 in 10m | +| **Ancillary data used** | Previous HRL Forest 2015 & 2012 data;
CLC2018 & HRL Impervious Degree 2018 used to exclude trees in urban and agricultural context by creating FAD and FTY 2018 layers;
Other ancillary data close to reference year of 2018. | +| **Relevant novelties in methodology compared to historical (GMES) HRL data** | Resolution upgrade from 20m to 10m (status layers);
Introduction of 20m resolution TCPC change layer, calculation backward to 2012-2015 period as well;
Physical exclusion of urban trees and trees under predominantly agricultural use from FTY forest areas (previously these were only marked by FADSL);
Produced in large part in cloud environment (MUNDI web services), complex workflow described in Product User Manual; | + +#### HRL VLCC Forest (reference years 2018-2021)[^11] + +The HRL VLCC portfolio comprises raster layers dedicated to the themes Tree cover and Forest, Grassland and Cropland for the EEA38 countries. Significant novelties are the harmonized production of the yearly updated status layers and the harmonization with other vegetation layers included to VLCC portfolio. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 18 Overview of key parameters of products created in the frames of VLCC Forest survey +::: + +| Parameter | Description | +|---------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------| +| **Reference years** | new 2018 (revised), 2019, 2020, 2021 | +| **Area mapped** | Countries mapped: EEA38 (without UK)
Total area mapped: 5 610 863 sqkm | +| **Unclassifiable (254) area** | none | +| **Spatial resolution** | 10m for primary status products (TCD, DLT, FTY)
20m for Dominant Leaf Type Change (DLTC) & Tree Cover Presence Change (TCPC)\*,
100m for aggregated products | +| **Satellite input
imagery used\*\*** | Primarily Sentinel-2A & Sentinel-2B L2A-data in 10m resolution ? | +| **Ancillary data
used\*\*** | Previous HRL Forest 2018; ?
CLC2018 & HRL Impervious Degree 2018 used to exclude trees in urban and agricultural context by creating FAD and FTY 2018 layers; ?
Other ancillary data close to reference year of 2018. ?
CLCplus Backbone ?? | +| **Relevant novelties
in methodology
compared to
historical (GMES)
HRL data** | Substantial changes in mapping concept:
Yearly update of tree cover / forest status between 2018-2021;
New (revised) status for the reference year of 2018 (TCD, DLT, FTY);
High level technical consistency in time-series (AOI, calibration, leaf type, status vs changes)
Introduction of 1ha MMU for change patches in 20m change products (TCPC, DLTC). | + +\*New name for Tree Cover Change Mask (TCCM) produced by previous HRL Forest inventories +\*\* No exact information, product user manual not yet available. + +### Changes in the total mapped area + +The estimated total tree covered area is influenced by total area mapped by a certain survey. Additional factor is the presence of unclassifiable (254) areas in pre-2018 products due to cloud cover (limitations in the availability of satellite input imagery). The influence of all these factors is appearing in the overall statistics of the original HRL products. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 19 Overall statistics of Dominant Leaf Type (DLT) raster products (sqkm) +::: + +| DLT class | HRL DLT2012 | HRL DLT2015 | HRL DLT2018 | VLCC DLT2018 | VLCC DLT2019 | VLCC DLT2020 | VLCC DLT2021 | +|-------------------------------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------| +| 0: non-tree covered areas | 3 670 507 | 3 631 870 | 3 711 454 | 3 581 943 | 3 588 549 | 3 598 260 | 3 609 649 | +| 1: broadleaved trees | 1 217 389 | 1 340 814 | 1 268 761 | 1 099 650 | 1 098 026 | 1 095 801 | 1 093 336 | +| 2: coniferous trees | 882 839 | 884 748 | 930 847 | 976 398 | 971 416 | 963 931 | 955 007 | +| 254: unclassifiable | 87 431 | 734 | | | | | | +| TOTAL AREA | 5 858 166 | 5 858 166 | 5 911 062 | 5 657 991 | 5 657 991 | 5 657 991 | 5 657 991 | +| TREE COVER | 2 100 228 | 2 225 562 | 2 199 608 | 2 076 049 | 2 069 442 | 2 059 732 | 2 048 342 | + +The total area providing valid CLMS tree cover information shows the following evolution: + +- Most of CLMS products were produced in the 2012-2018 period for the area of 39 EEA member countries (EEA39), but the mapped areas are still showing some differences: +- **HRL Forest 2015 (reference years 2015 & 2012)**: The total area of HRL Forest products for both 2012 and 2015 reference years is exactly the same, as HRL Forest 2012 was re-processed in the frames of HRL2015 survey. On the other hand, large unclassifiable area (87 431 sqkm) is present in 2012 year products, while significantly lower amount (734 sqkm) is present in 2015 year products; +- **HRL Forest 2018**: The total mapped area is slightly larger for 2018 year survey due to refinement of land mask e.g. by considering previously unmapped islands along the coastline. +- **VLCC Forest layers (2018-2021)**: The CLMS production in the period between 2018-2021, including VLCC Forest layers and CLCplus Backbone 2021 was restricted to the EEA38 countries, without UK area. + +HRL Forest Type (FTY) layers are aimed to estimate “forest” area, by largely following the FAO forest definition. HRL FTY is a derived product, and combines tree cover information from primary HRL DLT & TCD layers, as well as from HRL Imperviousness and Corine Land Cover: + +- FTY Forest type (broadleaved vs coniferous) is originated directly from DLT tree cover 1&2; +- DLT tree covered classes (1&2) characterized by lower than 10% tree cover density (TCD <10%) are excluded from FTY forest areas; +- Areas under agricultural use and in urban context are excluded from FTY forest areas based on information derived from CLC and HRL Imperviousness; +- Minimum Mapping unit of 0.5ha is applied both for tree-covered for non-tree-covered areas in a 4-pixel connectivity mode. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 20 Overall statistics of Forest Type (FTY) raster products (sqkm) +::: + +| FTY class | HRL FTY2012 | HRL FTY2015 | HRL FTY2018 | VLCC FTY2018 | VLCC FTY2019 | VLCC FTY2020 | VLCC FTY2021 | +|-----------------------------------------------|----------------------|----------------------|----------------------|----------------------|---------------------|---------------------|----------------------| +| 0: all non-forest areas | 3 716 772 | 3 673 400 | 3 816 871 | 3 662 597 | | | 3 689 335 | +| 1: broadleaved forest | 1 168 519 | 1 300 187 | 1 159 570 | 1 015 159 | | | 1 009 473 | +| 2: coniferous forest | 885 445 | 883 845 | 934 621 | 980 235 | | | 959 184 | +| 254: unclassifiable | 87 431 | 734 | | | | | | +| TOTAL AREA | 5 858 166 | 5 858 166 | 5 911 062 | 5 657 991 | - | - | 5 657 991 | +| FOREST AREA | 2 053 963 | 2 184 033 | 2 094 191 | 1 995 394 | - | - | 1 968 657 | + +The overall statistics of Forest Type products are showing similar picture, while the estimated forest cover is slightly lower than corresponding tree cover values. + +### Evolution of total tree cover estimated for EEA38 area + +In order to exclude the effect of the variations in AOI, estimated total tree cover values were compared to areas commonly mapped by all surveys, by excluding all areas from the comparison, where the out of area (255) raster code is appearing in any of the HRL Forest products. The commonly mapped area corresponds to the area of EEA38 countries, but slightly lower than the total area mapped by VLCC survey. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 21 Overall statistics of DLT products clipped to area commonly mapped by all surveys (sqkm) +::: + +| DLT class | HRL DLT2012 | HRL DLT2015 | HRL DLT2018 | VLCC DLT2018 | VLCC DLT2019 | VLCC DLT2020 | VLCC DLT2021 | +|-------------------------------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------| +| 0: all non-tree covered areas | 3 452 112 | 3 418 330 | 3 443 636 | 3 535 642 | 3 542 247 | 3 551 957 | 3 563 347 | +| 1: broadleaved trees | 1 200 102 | 1 317 597 | 1 247 205 | 1 099 295 | 1 097 671 | 1 095 446 | 1 092 980 | +| 2: coniferous trees | 872 074 | 874 558 | 920 022 | 975 926 | 970 944 | 963 460 | 954 535 | +| 254: unclassifiable | 86 575 | 378 | | | | | | +| TOTAL AREA | 5 610 863 | 5 610 863 | 5 610 863 | 5 610 863 | 5 610 863 | 5 610 863 | 5 610 863 | +| Tree Cover | 2 072 176 | 2 192 155 | 2 167 227 | 2 075 221 | 2 068 616 | 2 058 906 | 2 047 515 | + +#### Estimation of tree covered area by various sources + +Tree cover is mapped by several CLMS surveys, providing various results by estimation the total tree cover of European area. The primary products of two of the surveys can be characterized with similar thematic specification and the same, i.e. 10m raster resolution from the reference year of 2018, namely HRL Forest and CLCplus Backbone. The list of included and excluded elements of tree covered features is very similar both for HRL Forest as well as for CLCplus Backbone woody areas, although slight differences appear in the list. Despite of the similarities in the specifications there are significant differences in the estimated extent and structure of soil tree cover. + +Thus, the area covered by trees may be estimated from: + +- TCD layers, by considering tree cover density values in various ways; +- DLT layers, by considering all raster cells classified as broadleaved or coniferous trees; +- CLCplus Backbone by considering woody classes (2: needle leaved / 3: broadleaved deciduous / 4: broadleaved evergreen) or even class 5 (low growing woody vegetation); +- Additionally, FTY layers are aimed to estimate forest cover, by considering all raster cells classified as broadleaved or coniferous forest. + +**Area estimations based on tree cover density** + +TCD maps are created by a procedure characterized by two major steps: + +- Creation of the mask (“skeleton”) of tree covered areas based a list of included / excluded elements. The area of this mask corresponds exactly to the total area of DLT map and is usually created by hybrid methods considering image classification and in-situ knowledge of tree covered features; +- Calculation of the tree cover density value (1-100%) within the mask based on EO derived vegetation indices. + +Various possible calculation methods are used to calculate the extent of tree cover based on TCD: + +- **Tree cover estimation based on TCD values directly**: By definition this calculation method would deliver the exact estimation of tree cover, where the tree covered area of an individual raster cell is calculated as the total area of the raster cell multiplied by the percentage of tree cover shown by TCD value. Note, in practice the TCD value is significantly influenced by the current state of the vegetation; +- **Tree cover estimations based on binary maps, by applying various thresholds on TCD values**: +- TCD > 1%: All elements included to Tree Cover Mask (equivalent to DLT tree covered area by definition); +- $TCD \ge 10\%$: Commonly applied value appearing in forest definition, used by production of FTY layers as well besides 0.5ha MMU and exclusions by CLC and IMD data on agricultural and urban areas; +- $TCD \ge 30\%$: Commonly applied value for practical applications, appearing e.g in CLC forest definition; +- $TCD \ge 50\%$: Corresponding to theoretical majority rule (initial assumption for the comparability with CLCplus Backbone woody classes). + + + +Considering various possibilities for the estimations of tree cover described above, the estimated total tree covered area is influenced by the major factors of: + +- The extent end structure of the binary mask of tree cover further classified in DLT / FTY products as broadleaved or coniferous - influencing all presented estimations; +- The calibration of TCD values – influencing TCD, TCD≥10%, TCD≥30% and TCD≥50% estimations. + +Both the extent and structure of tree cover mask, as well as calibration of TCD values are showing significant differences, and characterise the individual surveys. All of these factors are influencing the total estimated tree covered area presented in Figure 8: + +- Large variation of total estimated tree cover is shown in the period 2012-2018, while the time-series within VLCC survey (2018-2021) is showing stability by presenting the slow decrease of total tree cover; +- All estimations show obvious breakpoint in 2018 between HRL2018 and VLCC2018 surveys; +- Lowest tree covered area is estimated by TCD direct calculation method, while highest values are shown by DLT layers (equivalent with TCD ≥ 1%); +- Lowest total tree covered area in the time-series is shown by 2012 survey. One of the possible reasons is, that relatively large area (altogether 86 575 sqkm) could not be classified because of cloud cover, represented by value 254 (unclassifiable area) in HRL2012 data; +- The highest total tree covered area in the time-series is shown by 2015 survey. Possible reason is the significant overestimation of tree cover experienced by many visual checks; +- Local minimum is shown only by TCD direct and TCD ≥ 50% calculations in case of HRL2018; +- FTY and TCD ≥ 1% shows only few differences in 2012 and in 2015, while larger differences from 2018 onwards. The reason is, that areas under agricultural use and in urban context were only marked in 2012 and 2015, while from 2018 excluded from FTY forest areas by FADSL layers. + +### Evolution of the calibration of TCD values + +TCD-value based estimations of tree covered area are significantly influenced by the calibration of TCD values. Calibration differences between individual surveys may be illustrated well by the histogram of TCD values. The calibration differences between HRL TCD2018 and VLCC TCD2018 are highlighted to demonstrate that the distribution of TCD values is not specific to the reference year, but to the survey. + + + +While the distribution of TCD values shows significant differences between each reference year in 2012-2018 period, the shape of the histograms is almost the same for all VLCC TCD layers - the yearly time-series of VLCC TCD layers shows stability in terms of calibration as well. The question is however, how far the calibration of next survey (beyond 2021) may be harmonized with the current calibration of VLCC TCD layers. + +### Overview of the stability of leaf type classification in DLT status layers + +Besides of providing a mask for tree / forest covered areas, DLT and FTY layers provide separation of the leaf type between broadleaved vs coniferous. The stability of the classification in the time-series was analysed by checking the differences of the DLT layers. + +Following difference codes were applied by considering all possible combinations in the classification: + +0: unchanged areas with no tree cover +1: new broadleaved cover (no tree cover in first layer) +2: new coniferous cover (no tree cover in first layer) +3: loss of broadleaved cover (no tree cover in second layer) +4: loss of coniferous cover (no tree cover in second layer) +11: unchanged broadleaved cover +22: unchanged coniferous cover +120: broadleaved changed to coniferous +210: coniferous changed to broadleaved +254: unclassifiable in any of parent status layers + +In order to gain comparable results for the entire period the DLT differences were calculated for the area mapped commonly by all DLT layers. All differences were calculated in 10m raster resolution. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 22 Classification differences in DLT time-series for commonly mapped EEA38 area (sqkm) +::: + +| DIFFERENCE CODE | HRL DLT2012 VS HRL DLT2015 | HRL DLT2015 VS HRL DLT2018 | VLCC DLT2018 VS VLCC DLT2019 | VLCC DLT2019 VS VLCC DLT2020 | VLCC DLT2020 VS VLCC DLT2021 | +|------------------------------------|---------------------------------------------|------------------------------|----------------------------|------------------------------|------------------------------| +| 0 | 3 209 737 | 3 297 386 | 3 535 164 | 3 540 472 | 3 547 401 | +| 1 | 200 052 | 103 320 | 354 | 1 297 | 3 234 | +| 2 | 42 024 | 42 930 | 124 | 479 | 1 321 | +| 3 | 118 517 | 198 077 | 1 978 | 3 522 | 5 700 | +| 4 | 40 839 | 40 180 | 5 105 | 7 963 | 10 246 | +| 11 | 1 008 487 | 947 604 | 1 097 317 | 1 094 149 | 1 089 746 | +| 22 | 736 584 | 831 471 | 970 821 | 962 981 | 953 214 | +| 120 | 73 070 | 101 524 | | | | +| 210 | 94 600 | 48 371 | | | | +| 254 | 86 953 | | | | | +| SUM | 5 610 863 | 5 610 863 | 5 610 863 | 5 610 863 | 5 610 863 | +| | **HRL DLT2018 VS VLCC DLT2018** | | | | | +| 0 | 3 208 100 | | | | | +| 1 | 170 956 | | | | | +| 2 | 39 275 | | | | | +| 3 | 176 445 | | | | | +| 4 | 58 793 | | | | | +| 11 | 975 178 | | | | | +| 22 | 714 724 | | | | | +| 120 | 165 974 | | | | | +| 210 | 101 041 | | | | | +| 254 | 378 | | | | | +| SUM | 5 610 863 | | | | | + +**Key observations:** + +- In case of all change classes (new cover / loss of cover) significantly more differences are shown between HRL or HRL vs VLCC layers in 2012-2018 period, than between VLCC layers in period 2018-2021; +- Large areas were re-classified from broadleaved to coniferous and vice versa between HRL or HRL vs VLCC layers in 2012-2018 period, while no re-classification is shown between VLCC layers in period 2018-2021; +- Two different classifications (HRL vs VLCC survey) were compared to the same reference year of 2018, similar magnitude of differences is shown than between any of previous classifications; +- Unclassifiable areas appear in comparisons between HRL DLT layers 2012-2015 and 2015-2018. + +### Classification differences HRL DLT2018 vs VLCC DLT2018 layers + +The magnitude of classification differences between HRL DLT2018 vs VLCC DLT2018 layers characterizes well the level of classification uncertainty of individual surveys. Classification differences were coded the same way as in case of tree cover presence change data. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 23 Differences in estimated tree cover for the reference year of 2018 +::: + +| DIFFERENCE CLASS | HRL DLT2018 vs VLCC DLT2018 | | | +|-------------------------------------------------------------------|---------------------------------------|-------------------------------------|----------------------------------------------------------| +| | **sqkm** | **%** | **% of unchanged
tree cover** | +| 0: unchanged areas with no tree cover | 3 343 511 | 59.1% | | +| 1: new tree cover | 146 363 | 2.6% | 7.6% | +| 2: loss of tree cover | 238 325 | 4.2% | 12.4% | +| 10: unchanged areas with tree cover | 1 929 685 | 34.1% | 100.0% | +| 254: unclassifiable in any status | - | - | | +| **SUM** | **5 657 885** | **100.0%** | | + +Significant differences in estimated tree cover are shown between the two classifications performed to the same reference year of 2018. Altogether 146 363 sqkm areas (7.6% of all stable tree cover) were classified in VLCC DLT2018 as new tree cover against HRL DLT2018, while 238 325 sqkm (12.4% of all stable tree cover) were classified as loss of tree cover compared to HRL DLT2018. + + + +Aggregated tree cover differences were calculated for a 10x10km statistical grid. The distribution of tree cover differences between the two classifications shows regional variations, especially large differences are shown for Mediterranean and Scandinavian areas. + +```{=typst} +#set page(flipped: true) +#set text(size: 9pt) +``` + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 24 DLT classification uncertainties for the reference year of 2018 +::: + +| DIFFERENCE CLASS | HRL DLT2018 vs VLCC DLT2018 | | | | +|---------------------------------|--------------------|-----------------------|---------------------------------------------------------|--------------------------------------------------------------------| +| | **sqkm** | **%** | **% to unchanged
broadleaved** | **% to
unchanged
coniferous** | +| 0: unchanged areas with no tree cover | 3 343 511 | 59.1% | | | +| 1: new broadleaved cover | 103 376 | 1.8% | 10.9% | | +| 2: new coniferous cover | 42 988 | 0.8% | | 5.2% | +| 3: loss of broadleaved cover | 198 119 | 3.5% | 20.9% | | +| 4: loss of coniferous cover | 40 207 | 0.7% | | 4.8% | +| 11: unchanged broadleaved cover | 947 878 | 16.8% | 100.0% | | +| 22: unchanged coniferous cover | 831 863 | 14.7% | | 100.0% | +| 120: broadleaved changed to coniferous | 101 548 | 1.8% | 10.7% | | +| 210: coniferous changed to broadleaved | 48 397 | 0.9% | | 5.8% | +| 254: unclassifiable in any status | | 0.0% | | | +| **SUM** | **5 657 885** | **100.0%** | | | + +```{=typst} +#set page(flipped: false, paper: "a4") +#set text(size: 11pt) +``` + +Not only the presence of tree cover, but also the leaf type classification is showing differences between the two surveys. Classification differences were coded in similar way as in case of dominant leaf type change data, but all the meaningful combinations were kept. + + + +Aggregated differences of broadleaved / coniferous cover were calculated for a 10x10km statistical grid to illustrate regional variations in leaf type classification. Large amount of broadleaved cover was eliminated or re-classified to coniferous cover by VLCC DLT survey compared to HRL DLT2018. + + + +Large green areas in Scandinavia indicate new coniferous cover partly as new cover partly as re-classified from broadleaved by VLCC DLT survey compared to HRL DLT2018. + + +HRL DLT2018 +VLCC DLT2018 + +#### Visualization of classification differences by high resolution maps + +In order to gain exact statistics and be able to visualize all differences between DLT classifications DLT difference maps were calculated in 10m raster resolution between neighbouring status data, where all possible combinations were considered. The high-resolution difference map between HRL DLT2018 and VLCC DLT2018 survey results indicate especially large variations in tree cover classifications Mediterranean, Scandinavian and Alpine regions. + + +CLCplus Backbone 2018 + +The re-classification of broadleaved cover to coniferous by VLCC DLT2018 is typical for most of Scandinavian area. + + +CLCplus Backbone 2018 + +The example in Portugal illustrates well the uncertainty of the classification on low density Mediterranean tree cover. Primarily the presence of tree cover is classified differently in Mediterranean area by the two surveys, but at several regions the re-classification of broadleaved to coniferous is characteristic to southern areas as well. + + +CLCplus Backbone 2018 + +Forest areas in the mountainous Alpine relief are often misclassified. Typical differences between the two surveys are illustrated for Alpine region, the most characteristic difference is the re-classification of broadleaved to coniferous by VLCC DLT2018, but similarly typical issue is the loss of coniferous cover in the new classification. In the example the latter area was classified as low-growing woody vegetation by CLCplus Backbone. + +### Classification differences VLCC DLT2018 vs CLCplus Backbone 2018 + +CLCplus Backbone is representing a third independent classification concerning tree cover information for the European area. Overall statistics of woody classes were compared to the commonly mapped (EEA38) area. + +```{=typst} +#set page(flipped: true) +#set text(size: 9pt) +``` + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 25 Overall statistics of VLCC DLT vs CLCplus Backbone for the reference year of 2018 (sqkm) +::: + +```{=html} +
| DLT class | +VLCC DLT2018 |
+ CLCplus Backbone class | +CLCplus Backbone 2018 |
+ Difference: Backbone – DLT (sqkm) |
+ Difference: % |
+
| 0: no tree cover | +3 581 943 | +0: no tree cover (all other classes) | +3 629 182 | +47 239 | +1.3% | +
| 1: broadleaved trees | +1 099 650 | +3: broadleaved deciduous trees | +916 188 | +-65 968 | +-6.0% | +
| 4: broadleaved evergreen trees | +117 494 | +||||
| 2: coniferous trees | +976 398 | +2: needle leaved trees | +995 129 | +18 731 | +1.9% | +
| SUM | +5 657 991 | +SUM | +5 657 991 | +- | +- | +
| All tree cover (1+2) | +2 076 049 | +All tree cover (2+3+4) | +2 028 810 | +-47 237 | +2.3% | +
difference (sqkm)** | **Tree cover change
(sqkm)** | **Change / Difference %** | +| 0: unchanged areas with no tree cover | 3 416 742 | 3 518 599 | 103,0% | +| 1: new tree cover | 252 031 | 100 566 | 39,9% | +| 2: loss of tree cover | 165 154 | 61 792 | 37,4% | +| 10: unchanged areas with tree cover | 1 936 075 | 2 089 044 | 107,9% | +| 254: unclassifiable in any status | 88 164 | 88 164 | 100,0% | +| **SUM** | **5 858 166** | **5 858 166** | **100,0%** | + +\*Tree Cover Change Mask (TCCM) 2012-2015 data (20m resolution) were not provided by original HRL Forest 2015 survey (and not included to corresponding guidelines), but was provided later by HRL Forest 2018 survey. + +The Dominant Leaf type Change (DLTC) layer produced by original HRL Forest 2015 survey was an early version of that kind, is now outdated and currently no DLTC layer for the 2012-2015 period is published on CLMS website. The original DLTC1215 layer was produce by combining tree cover change information with dominant leaf type change and has included many classes, which cannot link fully to the difference classes derived by logical combinations of DLTC classes. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 27 DLT differences compared to DLT changes indicated by DLTC1215* data +::: + +```{=html} +
| DLT difference class | +DLT diff 2012-2015 |
+ DLT 2012-2015 change class | +DLT change 2012-2015 |
+
| 0: unchanged areas with no tree cover | +3 416 742 | +0: unchanged areas with no tree cover | +3 522 280 | +
| 1: new broadleaved cover | +209 205 | +1: new broadleaved cover | +83 010 | +
| 2: new coniferous cover | +42 826 | +2: new coniferous cover | +13 610 | +
| 3: loss of broadleaved cover | +122 860 | +3: loss of broadleaved cover | +38 957 | +
| 4: loss of coniferous cover | +42 294 | +4: loss of coniferous cover | +20 658 | +
| 11: unchanged broadleaved cover | +1 021 618 | +10: unchanged areas with tree cover | +2 040 828 | +
| 22: unchanged coniferous cover | +745 126 | +11: broadleaved, increased density | +16 559 | +
| 120: broadleaved changed to coniferous | +73 863 | +33: broadleaved, decreased density | +7 183 | +
| 210: coniferous changed to broadleaved | +95 469 | +22: coniferous, increased density | +4 663 | +
| 254: unclassifiable in any status | +88 164 | +44: coniferous, decreased density | +3 062 | +
| SUM | +5 858 166 | +120: broadleaved changed to coniferous | +3 062 | +
| + | + | 210: coniferous changed to broadleaved | +15 668 | +
| + | + | 254: unclassifiable in any status | +88 164 | +
| + | + | SUM | +5 858 166 | +
difference (sqkm)** | **Tree cover change
(sqkm)** | **Change / difference
%** | +| 0: unchanged areas with no tree cover | 3 413 593 | 3 619 816 | 106.0% | +| 1: new tree cover | 217 626 | 1 957 | 0.9% | +| 2: loss of tree cover | 244 492 | 17 510 | 7.2% | +| 10: unchanged areas with tree cover | 1 981 069 | 2 272 189 | 114.7% | +| 254: unclassifiable in any status | 734 | 734 | 100.0% | +| **SUM** | **5 857 514** | **5 912 205** | **100.9%** | + +Both Tree Cover Change mask (TCCM) and Dominant Leaf Type Change (DLTC) layers were provided by the HRL Forest 2018 survey in 20m resolution. With the aggregation of thematic classes, DLT difference classes may be linked to TCCM classes directly. + +HRL DLT2015 (20m resolution) and HRL DLT2018 (10m resolution) layers cannot be considered as consistent, not only because of the different spatial resolution, but statistically the difference of the layers is obviously not corresponding to the changes derived, the changes represent only 0.9% (broadleaved trees) and 7.2% (coniferous trees) of the differences. + +With the merge of thematic classes, DLT difference classes may be linked to DLTC classes directly, but as the DLTC layer does not include (by definition) all combinations of possible DLT differences, DLTC layers cannot be directly used in an backdating ("accounting") process. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 29 DLT differences compared to DLT changes indicated by DLT1518 data +::: + +```{=html} +
| DLT difference class | +DLT diff 2015- 2018 |
+ DLT 2012-2015 change class | +DLT change 2015-2018 |
+ Change / difference |
+
| 0: unchanged areas with no tree cover | +3 413 593 | +0: unchanged areas with no tree cover |
+ 3 619 816 | +106.0% | +
| 1: new broadleaved cover | +177 655 | +1: new broadleaved cover | +1 239 | +0.7% | +
| 2: new coniferous cover | +39 971 | +2: new coniferous cover | +718 | +1.8% | +
| 3: loss of broadleaved cover | +184 629 | +3: loss of broadleaved cover | +5 415 | +2.9% | +
| 4: loss of coniferous cover | +59 863 | +4: loss of coniferous cover | +12 095 | +20.2% | +
| 11: unchanged broadleaved cover | +988 726 | +10: unchanged areas with tree cover |
+ 2 271 876 | +132.7% | +
| 22: unchanged coniferous cover | +722 894 | +|||
| 120: broadleaved changed to coniferous | +167 458 | +12: potential change among dominant leaf type |
+ 313 | +0.1% | +
| 210: coniferous changed to broadleaved | +101 991 | +|||
| 254: unclassifiable in any status | +734 | +254: unclassifiable in any status |
+ 734 | +100.0% | +
| SUM | +5 857 514 | +SUM | +5 912 166 | +100.9% | +
difference (sqkm)** | **Tree cover change
(sqkm)** | **Change / difference
%** | +| 0: unchanged areas with no tree cover | 3 576 183 | 3 525 896 | 98,6% | +| 1: new tree cover | 6 815 | 2 187 | 32,1% | +| 2: loss of tree cover | 34 521 | 21 204 | 61,4% | +| 10: unchanged areas with tree cover | 2 041 527 | 2 109 759 | 103,3% | +| 254: unclassifiable in any status | - | - | - | +| **SUM** | **5 659 046** | **5 659 046** | **100,0%** | + +VLCC DLT layers were produced for each reference year in the 2018-2021 period, both change layers Tree Cover Presence Change (PCPC, new name for TCCM in previous surveys) and Dominant Leaf Type Change (DLTC) layers were provided by the VLCC Forest 2018-2021 survey in 20m resolution to cover the full 3 yearly period. + +With the merge of thematic classes, DLT difference classes may be linked to DLTC classes directly, but as the DLTC layer does not include (by definition) all combinations of possible DLT differences, DLTC layers cannot be directly used in a backdating (“accounting") process. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 31 DLT differences compared to DLT changes indicated by DLT1821 data +::: + +```{=html} +
| DLT difference class | +DLT diff 2015-2018 |
+ DLT 2012-2015 change class | +DLT change 2015-2018 |
+ Change / difference |
+
| 0: unchanged areas with no tree cover | +3 576 183 | +0: unchanged areas with no tree cover | +3 525 896 | +98.6% | +
| 1: new broadleaved cover | +4 888 | +1: new broadleaved cover | +1 660 | +34.0% | +
| 2: new coniferous cover | +1 927 | +2: new coniferous cover | +528 | +27.4% | +
| 3: loss of broadleaved cover | +11 203 | +3: loss of broadleaved cover | +4 301 | +38.4% | +
| 4: loss of coniferous cover | +23 319 | +4: loss of coniferous cover | +16 903 | +72.5% | +
| 11: unchanged broadleaved cover | +1 088 448 | +10: unchanged areas with tree cover | +2 109 759 | +103.3% | +
| 22: unchanged coniferous cover | +953 080 | +|||
| 120: broadleaved changed to coniferous | +- | +12: potential change among dominant leaf type | +- | +- | +
| 210: coniferous changed to broadleaved | +- | +|||
| 254: unclassifiable in any status | +- | +254: unclassifiable in any status | +- | +- | +
| SUM | +5 659 064 | +SUM | +5 659 064 | +100.0% | +
| Patch size | +New tree cover | +Loss of tree cover | +||
| sqkm | +% | +sqkm | +% | +|
| Smaller than 0.1ha | +2 044 | +30.0% | +3 343 | +9.7% | +
| 0.1ha – 1ha | +2 243 | +32.9% | +7 638 | +22.1% | +
| Greater or equal than 1ha | +2 528 | +37.1% | +23 540 | +68.2% | +
| All changes | +6 815 | +100.0% | +34 521 | +100.0% | +
| Stratum | +No. of all samples | +No. of samples evaluated | +DLT2018 | +DLT2021 | +Validity of change (no. of samples) | +||
| Correct | +Correct | +Real change |
+ Partial change |
+ No change |
+ |||
| New tree cover | +100 | +98 | +93% | +91% | +74% | +7% | +18% | +
| Loss of tree cover | +100 | +91 | +89% | +87% | +74% | +8% | +19% | +
| Total: | +200 | +189 | +91% | +89% | +74% | +7% | +19% | +
| Product features: | +|
| Digital thematic map. Thematic classes based on CORINE LC nomenclature and GUS Legend. |
+ |
| Input data sources | +|
| Earth Observation (EO) data with 2.5 m spatial resolution multispectral or pan-sharpened (multispectral merged with panchromatic) data. Multispectral data includes near-infrared band. Topographic Maps at a scale of 1: 50 000 or larger. COTS navigation data for the road network (methodology applied will be defined). Areas of Interest for Urban Atlas Mapping are determined by DG Regional Policy. Sealing layer based on FTS specifications for degree of sealing for level 3 classes 1.1.1 and 1.1.2 and level 4 classes 1.1.2.1, 1.1.2.2, 1.1.2.3 and 1.1.2.4. All input data should be described by metadata according to the INSPIRE metadata profile specifications and guidelines. |
+ |
| Ancillary data optional for all classes | +|
| COTS navigation data: points of interest, land use, land cover, water areas. Google Earth (only for interpretation, not for delineation). Local city maps. |
+ |
| Ancillary data required for certain classes | +|
| Local zoning data (e.g. cadastral data). Field check (on-site visit). Very high resolution imagery (better than 1 m ground resolution, e.g. aerial photographs). |
+ |
| Geometric resolution (Scale) | +|
| 1:10 000; MinMU = 0.25 ha | +|
| Geographic projection / Reference system | +|
| As per user request but uniform within project area. | +|
| Positional accuracy | +|
| ± 5 m | +|
| Thematic accuracy (in %) | +|
| Minimum overall accuracy for level 1 class 1 "Artificial surfaces”: 85%. Minimum overall accuracy (all classes): 80%. Methodology for quality control has to be performed according to RD[1]. The minimum overall accuracy for level 1 class 1 "Artificial surfaces" must include both omission and commission errors with other classes within the larger urban zone (LUZ). |
+ |
| Update frequency | +|
| t.b.d. | +|
| Base data topicality | +|
| t.b.d | +|
| Delivery format | +|
| Topologically correct GIS file. Single part features. |
+ |
| Data type | +|
| Vector | +
| CORINE Class(es) [Level I, No.] |
+ Level(s) provided |
+ MinMU | +Thematic Accuracy |
+ Positional Pixel Accuracy |
+ |
| M1.1 Urban Atlas | +1 | +I-IV | +0.25 ha | +>= 85% | +<± 5 m | +
| + | 2 - 5 | ++ | 1 ha | +>= 80% | +<± 5 m | +
| Overall Accuracy |
+ >= 80% | ++ | |||
| + GSELand M1.1 Urban Atlas + | +|||
| Urban Atlas No. |
+ Vector Data Code |
+ Nomenclature | +Additional Information |
+
| GSELUA_yy | ++ | + | |
| 1 | ++ | Artificial surfaces | ++ |
| 1.1 | +Urban Fabric | +||
| 1.1.1 | +11100 | +Continuous Urban Fabric (S.L. > 80%) | +FTS¹ required | +
| 1.1.2 | +11200 | +Discontinuous Urban Fabric (S.L. 10% - 80%) |
+ + |
| 1.1.2.1 | +11210 | +Discontinuous Dense Urban Fabric (S.L. 50% - 80%) |
+ FTS required | +
| 1.1.2.2 | +11220 | +Discontinuous Medium Density Urban Fabric (S.L. 30% - 50%) |
+ FTS required | +
| 1.1.2.3 | +11230 | +Discontinuous Low Density Urban Fabric (S.L. 10% - 30%) |
+ FTS required | +
| 1.1.2.4 | +11240 | +Discontinuous Very Low Density Urban Fabric (S.L. < 10%) |
+ FTS required | +
| 1.1.3 | +11300 | +Isolated structures | ++ |
| 1.2 | ++ | Industrial, commercial, public, military, private and transport units |
+ + |
| 1.2.1 | +12100 | +Industrial, commercial, public, military and private units |
+ zoning data / field check recommended |
+
| 1.2.2 | +12200 | +Road and rail network and associated land |
+ COTS² navigation data required |
+
| 1.2.2.1 | +12210 | +Fast transit roads and associated land |
+ COTS navigation data required |
+
| 1.2.2.2 | +12220 | +Other roads and associated land | +COTS navigation data required |
+
| 1.2.2.3 | +12230 | +Railways and associated land | +COTS navigation data required |
+
| 1.2.3 | +12300 | +Port areas | +zoning data / field check recommended |
+
| 1.2.4 | +12400 | +Airports | +zoning data / field check recommended |
+
| 1.3 | ++ | Mine, dump and construction sites | ++ |
| 1.3.1 | +13100 | +Mineral extraction and dump sites | ++ |
| 1.3.3 | +13300 | +Construction sites | ++ |
| 1.3.4 | +13400 | +Land without current use | ++ |
| 1.4 | ++ | Artificial non-agricultural vegetated areas |
+ + |
| 1.4.1 | +14100 | +Green urban areas | ++ |
| 1.4.2 | +14200 | +Sports and leisure facilities | ++ |
| 2 | +20000 | +Agricultural areas, semi-natural areas and wetlands |
+ 1 ha MMU | +
| 3 | +30000 | +Forests | +1 ha MMU | +
| 5 | +50000 | +Water | +1 ha MMU | +
| + | Net2Class | +|||
| FRC | +1 | +2 | +3 | +4 | +
| 0 | +Manual | +|||
| 1 | +Manual | +|||
| 2 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 3 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 4 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 5 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 6 and above | +Mapping decided on city by city basis | +|||
| + | Name | +Issue | +Date | +Reference | +
| RD[1] | +C5-Service Validation Protocol | +1.00 | +14/05/2008 | +ITD-0421-RP-0003-C5 | +
| RD[2] | +Call for Tenders No ENTR/08/029 - Specifications |
+ 2.00 | +07/05/2008 | +Call for Tenders No ENTR/08/029 - Specs |
+
| RD[3] | +Mapping Guide for a European Urban Atlas |
+ 1.02 | +08/05/2008 | +ITD-0421-GSELand-TN-01 | +
| RD[4] | +Call for Tenders No EEA/IDM/RO/16/007 - Specifications |
+ 1.0 | +08/08/2016 | +Call for Tenders No EEA/IDM/RO/16/007 - Specs |
+
| RD[5] | +Urban Atlas 2012 Validation Report |
+ 1.2 | +09/06/2017 | +UA2012 Validation Report | +
| RD[6] | +Urban Atlas 2012 Extension to Western Balkan and Turkey Validation report |
+ 1.5 | +05/11/2018 | +UA2012 Extension Validation report |
+
| RD[7] | +Urban Atlas 2006 Mapping Guide |
+ 2.0 | ++ | UA2006 Mapping Guide | +
| RD[8] | +Urban Atlas 2012 Mapping Guide |
+ 5.0 | ++ | UA2012 Mapping Guide | +
| Title | Urban Atlas LU/LC |
| Abstract | Very high-resolution land use and land cover dataset of Functional Urban Areas |
| INSPIRE themes | Land cover Land use |
| Geographic description | EU, EFTA and West Balkan countries plus United Kingdom and Turkey |
| Temporal description | 2006 / 2012 / 2018 |
| Purpose | Provide the baseline of land use/land cover data on Functional Urban Areas extracted from VHR and other available imagery (and combined with in-situ data) to allow the study of the characteristics of these areas in comparison with major urban areas in the EU and EFTA countries. |
| Minimum Mapping Unit | 0.25 ha in urban areas (level 1) 1 ha in rural areas (level 2-5) |
| Minimum mapping width | 10 m between two objects for distinct mapping |
| Nomenclature | As provided in the Chapter 4.4. table 3 (containing 27 classes) |
| Projection | ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) |
| Delivery formats | Shapefile (2006), EsriFileGDB (2012), GeoPackage (2018) |
| Metadata | INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 |
| Positional accuracy | According to geo-location accuracy of satellite imagery delivered by ESA |
| Overall classification accuracy | ≥ 85 % in urban classes (class 1) ≥ 80 % in rural classes (classes 2 to 5) ≥ 80 % overall accuracy |
| Title | Street Tree Layer |
| Abstract | Very high-resolution tree cover dataset of inside nomenclature level 1 areas. |
| INSPIRE themes | Land cover |
| Geographic description | EU, EFTA and West Balkan countries plus United Kingdom and Turkey |
| Temporal description | 2012 (partial coverage) / 2018 |
| Purpose | Mapping of contiguous rows or patches of trees covering inside the nomenclature level 1 areas. |
| Minimum Mapping Unit | 500 m² |
| Minimum mapping width | 10 m |
| Nomenclature | Tree (STL=1) or Nodata (99) |
| Projection | ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) |
| Delivery formats | Shapefile (2012), GeoPackage (2018) |
| Metadata | INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 |
| Positional accuracy | According to geo-location accuracy of satellite imagery delivered by ESA |
| Overall classification accuracy | > 80 % in urban classes |
| Title | Digital Height Model |
| Abstract | Very High-Resolution layer (grid, building or block footprint) containing building height in the core urban area |
| INSPIRE themes | Buildings |
| Geographic description | EU, EFTA and West Balkan countries plus United Kingdom and Turkey - core urban areas of capital cities only |
| Temporal description | 2012 |
| Purpose | Provide very high-resolution information (grid, building or block footprint) containing building height in the core urban area of capital cities in order to obtain a better insight into measuring urban density |
| Minimum Mapping Unit | 10 m x 10 m or better |
| Minimum mapping width | N.A. |
| Nomenclature | N.A. |
| Projection | ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) |
| Delivery formats | Raster GeoTIFF 16-bit |
| Metadata | INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 |
| Horizontal accuracy | Half a pixel |
| Vertical accuracy | 3 m |
| Overall classification accuracy | N.A. |
| CODE_yyyyy | +
| 11210* | +
| CODE | +→ | +Legend code | +
| yyyyy | +→ | +Reference year (e.g. 2006, 2012 or 2018) | +
| CORINE Classes [Lev. I, No.] |
+ Levels provided | +MMU | +|
| Urban | +1 | +I - IV | +0.25 ha | +
| Rural | +2 - 5 | +I - II | +1 ha | +
| Street Tree Layer | +- | +- | +500 m2 | +
| CORINE Classes [Lev. I, No.] |
+ Levels provided | +Thematic Accuracy |
+ Positional Pixel Accuracy |
+ |
| Urban | +1 | +I - IV | +>= 85% | +<± 5 m | +
| Rural | +2 - 5 | +I - II | +>= 80% | +<± 5 m | +
| Overall Accuracy |
+ >= 80% | +|||
| UA No. | +Code | +Nomenclature | +Additional Information |
+
| 1 | ++ | Artificial surfaces | ++ |
| 1.1 | ++ | Urban Fabric | ++ |
| 1.1.1 | +11100 | +Continuous urban fabric (S.L. > 80%) | +HRL IMD required | +
| 1.1.2 | ++ | Discontinuous Urban Fabric (S.L. 10% - 80%) | ++ |
| 1.1.2.1 | +11210 | +Discontinuous dense urban fabric (S.L. 50% - 80%) |
+HRL IMD required | +
| 1.1.2.2 | +11220 | +Discontinuous medium density urban fabric (S.L. 30% - 50%) |
+HRL IMD required | +
| 1.1.2.3 | +11230 | +Discontinuous low density urban fabric (S.L. 10% - 30%) |
+HRL IMD required | +
| 1.1.2.4 | +11240 | +Discontinuous very low density urban fabric (S.L. < 10%) |
+HRL IMD required | +
| 1.1.3 | +11300 | +Isolated structures | ++ |
| 1.2 | ++ | Industrial, commercial, public, military, private and transport units | ++ |
| 1.2.1 | +12100 | +Industrial, commercial, public, military and private units |
+zoning data / field check recommended |
+
| 1.2.2 | ++ | Road and rail network and associated land | +COTS or OSM data required | +
| 1.2.2.1 | +12210 | +Fast transit roads and associated land | +COTS or OSM data required | +
| 1.2.2.2 | +12220 | +Other roads and associated land | +COTS or OSM data required | +
| 1.2.2.3 | +12230 | +Railways and associated land | +COTS or OSM data required | +
| 1.2.3 | +12300 | +Port areas | +zoning data / field check recommended |
+
| 1.2.4 | +12400 | +Airports | +zoning data / field check recommended |
+
| 1.3 | ++ | Mine, dump and construction sites | ++ |
| 1.3.1 | +13100 | +Mineral extraction and dump sites | ++ |
| 1.3.3 | +13300 | +Construction sites | ++ |
| 1.3.4 | +13400 | +Land without current use | ++ |
| 1.4 | ++ | Artificial non-agricultural vegetated areas | ++ |
| 1.4.1 | +14100 | +Green urban areas | ++ |
| 1.4.2 | +14200 | +Sports and leisure facilities | ++ |
| 2 | ++ | Agricultural areas | +1 ha MMU | +
| 2.1 | +21000 | +Arable land (annual crops) | ++ |
| 2.2 | +22000 | +Permanent crops | ++ |
| 2.3 | +23000 | +Pastures | ++ |
| 2.4 | +24000 | +Complex and mixed cultivation | ++ |
| 3 | ++ | Natural and (semi-)natural areas | +1 ha MMU | +
| 3.1 | +31000 | +Forests | ++ |
| 3.2 | +32000 | +Herbaceous vegetation associations | ++ |
| 3.3 | +33000 | +Open spaces with little or no vegetation | ++ |
| 4 | +40000 | +Wetlands | +1 ha MMU | +
| 5 | +50000 | +Water | +1 ha MMU | +
| 9.1 | +91000 | +No data (Clouds and shadows) | ++ |
| 9.2 | +92000 | +No data (Missing imagery) | ++ |
| FRC (COTS) | +Type (OSM) | +Full name | +
| 0 | +motorway | +Motorway, Freeway or another Major Road | +
| 1 | +trunk | +Major Road less important than a Motorway | +
| 2 | +trunk_link | +Other Major Road | +
| 3 | +primary | +Secondary Road | +
| 4 | +secondary | +Local Connecting Road | +
| 5 | +secondary_link | +Local Road of high importance | +
| 6 | +tertiary | +Local Road | +
| 7 | +residential, service, unclassified | +Local Road of minor importance | +
| 8 | +track, path, steps, cycleway | +Other Road | +
| Net2Class | +Importance Level | +
| 0 | +First class (Highest) | +
| 1 | +Second class | +
| 2 | +Third class | +
| 3 | +Fourth class | +
| 4 | +Fifth class | +
| 5 | +Sixth class | +
| 6 | +Seventh class (Lowest) | +
| + | Net2Class | +|||
| 1 | +2 | +3 | +4 | +|
| FRC | ++ | |||
| 0 | +Manual | +|||
| 1 | +Manual | +|||
| 2 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 3 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 4 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 5 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 6 and above | +Mapping decided on city by city basis | +|||
| Product features | |
| Digital thematic map. Thematic classes based on CORINE LC nomenclature and Copernicus Urban Service Legend | |
| Input data sources | |
| Earth Observation (EO) data with 2 to 4 m spatial resolution multispectral or pan-sharpened (multispectral merged with panchromatic) data. Multispectral data includes near-infrared band. 10m resolution Sentinel-2 EO data for the rural 2012-2018 change detections. Topographic Maps at a scale of 1: 50 000 or larger. Navigation data (COTS or OSM) for the road network. Areas of Interest for Urban Atlas (UA) Mapping are determined by DG REGIO. Soil Sealing / Imperviousness Degree (IMD) based on HRL Imperviousness specifications for degree of sealed soils (IMD 0-100%) for level 3 classes 1.1.1 and 1.1.2 and level 4 classes 1.1.2.1, 1.1.2.2, 1.1.2.3 and 1.1.2.4. All input data should be described by metadata according to the INSPIRE metadata profile specifications and guidelines. | |
| Ancillary data optional for all classes | |
| Navigation data (COTS or OSM): points of interest, land use, land cover, water areas. Google Earth or other relevant available database (only for interpretation, not for delineation). Local city maps | |
| Ancillary data optional for all classes | |
| Local zoning data (e.g. cadastral data). Field check (on-site visit). Very high resolution imagery (better than 1 m ground resolution, e.g. aerial photographs). | |
| Geometric resolution (Scale) | |
| 1:10 000 | |
| Geographic projection / Reference System | |
| ETRS_1989_LAEA (for UA2012) | |
| Positional accuracy | |
| ± 5 m | |
| Thematic accuracy (in %) | |
| Minimum Overall Accuracy (OA) for level 1 class 1 "Artificial surfaces: 85% Minimum OA (Rural surfaces and Overall): 80%. Methodology for quality control has to be performed according to RD[1]. The minimum OA for level 1 class 1 "Artificial surfaces" must include both omission and commission errors with other classes within the Functional Urban Areas (FUA). | |
| Update frequency | |
| Every 6 years | |
| Base data topicality | |
| Not specified | |
| Delivery format | |
| Topologically correct GIS files. Single part features. | |
| Data type | |
| Vector |
| Version | +Date | +Comments and main updates | +
| V1 | 08/05/2008 | Document TD-0421-GSELand-TN-01 from the ESRIN/Contract No. 19407/05/I-LG (GSE Land Information Services) |
| V2 | 2011 | Urban Atlas 2006 Mapping Guide with European Commission Template |
| V3 | 03/04/2016 | - Update for the Urban Atlas 2012 component into the former template - Insertion of attribute tables description for Urban Atlas 2006 revised, Urban Atlas 2012, Changes 2006-2012, Urban Atlas 2006-2012 - New unique projection: ETRS_1989_LAEA - Exception of MinMW for the Roads: 6 m |
| V4.1 | 03/05/2016 | - Urban Atlas 2012 Mapping Guide with European Commission Template - Insertion of Section 4.6: Description of mapping units for the Urban Atlas Street Tree Layer |
| V4.2 | 04/05/2016 | - Minor corrections |
| V4.3 | 24/06/2016 | - Insertion of new “no data” classes: 91000 No data (Clouds and shadows) / 92000 No data (Missing imagery) |
| V4.4 | 05/07/2016 | - Minor corrections |
| V4.5 | 27/07/2016 | - Insertion of Section 4.7 dedicated on the change layer including the specific MMU for this layer - Insertion of the Table of Content at the beginning of the document |
| V4.6 | 03/08/2016 | - Removal of the confusing sentence in section 4.2.9. about Minimum mapping units: "Maximum mapping width (MaxMW) between 2 objects for mapping together 10 m" - MaxMW was also removed from the list of abbreviations |
| V4.7 | 28/10/2016 | - Insertion of report information and Change records |
| V4.8 | 02/12/2016 | - Insertion of a versioning on the cover page and on the footer - New formulation for the sentences of the priority rules, section 4.2.10 - Insertion of a figure for the Street Tree Layer minimum mapping unit, section 4.8 - Modification of the STL no data code from 91000/92000 to 99, section 5.4.5. |
| V5.0 | 24/02/2017 | - Paging corrections |
| V6.0 | 06/06/2019 | - Some minor precisions in classes description. - Updates to include the 2018 exercise. |
| V6.1 | 28/02/2020 | - Update of Reference Documents - Update on section 4.1 Product description (addition of DHM product) - Update on section 4.2 General Guidelines (redesign of section to include latest changes on change layer, STL product) - Update on section 5.4 product types and field descriptions |
| V6.2 | 21/09/2021 | - Add of a section about the digital height model layer (DHM) |
| V6.3 | 06/01/2023 | - Clarifications on descriptions for classes 1.1.3. and 1.4.1. |
| Document Author | +Tanja Gasber¹, Christian Gruber¹, Adam Pasik¹, Florence Marti², Matthieu Denisselle² ¹GeoVille GmbH, ²Magellium (lead). |
+
| Project Owner | +Joanna Przystawska | +
| Project Manager | +Florence Marti | +
| Document Code | +HR-WSI-DT-067-MAG_PUM_WATER | +
| Document Version | +1.3 | +
| Distribution | +Public | +
| Date | +23/12/2025 | +
| File identifier (layer/ metadata) | +Description | +Format | +Data type | +
| WCD | +Describes the water presence frequency in a hydrological year and is expressed in days as follows: → 0: no water → 1-366 day with water detected → 65485: cloud or cloud shadow → 65533: sea water → 65535: no data |
+ GeoTIFF | +uint16 | +
| WCD-QA | +Quality layer providing a basic assessment of WCD quality: → 0: high quality → 1: medium quality → 2: low quality → 3: minimal quality → 205: cloud or cloud shadow → 253: sea water → 255: no data |
+ GeoTIFF | +uint8 | +
| MTD | +Product metadata (INSPIRE compatible) | +XML | +string | +
| Class | +Explanation | +Examples | +
| Dry | +Always dry or mostly dry with minor instances of water (i.e. <25%) | +
+
|
+
| Permanent water | +Always water. The highest ratio of the water / total instances (>85%) are classified as permanent water surfaces. |
+
+
|
+
| Temporary water | +Temporary water surfaces. Alteration of dry and water Temporary water surfaces will have a ratio between >25% to 85% (water / total instances) |
+
+
|
+
| Sea water | ++ | + |
| Elements to be included in the HR Water Layer 2021 | +Elements to be excluded from the HR Water Layer 2021 | +
+
|
+
+
|
+
| File identifier (layer/metadata) | +Description | +Format | +Data type | +
| WL | +Classified layer, differentiating the classes of permanent water, temporary water, and dry areas as well as sea water, non-classifiable pixels, and outside area throughout the entire observation period 2016-2021 as follows: → 0: dry → 1: permanent water → 2: temporary water → 205: cloud or cloud shadow → 253: sea water → 255: no data |
+ GeoTIFF | +uint8 | +
| WPI (delivered in 10m, EPSG:3035 version only) | +Shows the occurrence of water areas throughout the entire observation period 2016-2021 as follows: → 0: no water → 1-100: water presence → 205: cloud or cloud shadow → 253: sea water → 255: no data |
+ GeoTIFF | +uint8 | +
| WCL (delivered in 10m, EPSG:3035 version only) | +Quality layer providing an assessment of WL quality: → 0-100: classification confidence → 205: cloud or cloud shadow → 253: sea water → 255: no data |
+ GeoTIFF | +uint8 | +
| File identifier (layer/metadata) | +Description | +Format | +Data type | +
| WLRA: RASWaM | +Water masks showing the seasonal (2009-2015) water / dry occurrences in binary values as follows: → 0: no water/dry → 1: water → 255: no data |
+ ZARR files with Geotiffs | +uint8 | +
| RAMWaM | +Water masks showing the monthly (2016-2021) water / dry occurrences in binary values as follows: → 0: no water/dry → 1: water → 255: no data |
+ + | + |
+
+
+
+
+
+
+
+
+
+```
+Figure 3-1 Evolution of HRL Forest and Grassland towards the three product groups HRL Tree Cover & Forests, HRL Grasslands, HRL Croplands.
+
+# Review of User Requirements
+
+In the frame of the Horizon 2020 (H2020) project ECoLaSS a survey [5] of key stakeholders has been performed in order to evaluate the user requirements towards the evolution of existing and future **Copernicus** products. This survey made also use of the results from the Nextspace User Study [6] and revealed that HRL users like European institutions, service industry, research and academia, national agencies, regional administrations, NGOs or private users would in general appreciate:
+
+- High thematic quality/meaningful and application-oriented product definitions;
+- Sufficient spatial and timely resolution concerning both, status layer and change layer;
+- Short update cycles;
+- Change monitoring;
+- Free and open access;
+- High technical quality;
+- Standardized and comparable nomenclature;
+- Transparent and scientific workflows and state-of-the-art methodology;
+- Detailed documentation of these workflow and the respective methodology;
+- Consistency of the Pan-European products enabling synergistic use of all products;
+- Streamlining the pan-European product with global ones;
+- Availability of historic data and compatibility of time series;
+- Open access to the original Copernicus Sentinel data;
+- Sophisticated product presentation and visualisation possibilities in an online viewer on the Copernicus platform;
+- IPCC -compliant land-use categories..
+
+While many of these requirements had already been satisfied with previous HRL reference years some could only be implemented within the current update:
+
+- A long-standing thematic gap in the European CLMS portfolio concerning the monitoring of agriculture has been addressed though new products on crop types and agricultural activities. This also improves the separation between grassland and cropland and the IPCC conformity;
+- Yearly update cycle for status layer;
+- Grassland use intensity (or the dynamics of intensification/ extensification) is partially addressed through a new product on Grassland mowing.
+
+Further requirements that remain to be considered for future updates are for example:
+
+- More fine-grained differentiation among species-rich (extensively used) and separation from species-poor (intensively used) and managed grassland;
+- Tree-species compositions and shifts between extensive and intensive management;
+- Increased timeliness of availability of the products: The mid-term goal is a product provision at latest 12 months after the end of the reference year.
+
+# Product structure - What are the High Resolution Layers?
+
+The **High Resolution Layers (HRL)** vegetated land cover characteristics portfolio consists of **Tree Cover & Forests, Grasslands and Croplands** products (Figure 5-1), which together cover most of what is defined as the Biotic component of the EAGLE Land Cover Components[^1]. More specifically, the mapping is focused on surfaces with a vegetation cover above 30%; an exception to this is tree cover where the objective is to map tree cover with a continuous range of 1-100% **Tree Cover Density (TCD)**, i.e. also below 30%, as far as detectable from 10-metre resolution satellite imagery. This definition is also in line with the Sparsely Vegetated class in the **CLC+ Backbone Raster**[^2] and considers that detection / classification of vegetation below this threshold is typically more error-prone. The definition also aims at largely avoiding overlaps with the non-vegetated land cover characteristics such as **HRL Imperviousness**, which is focused on areas with less than 10% vegetation cover during any time of the year, for a reference period of 3 year.
+
+Some overlaps between the three product groups are allowed by definition, for example areas with tree crops (i.e. olive, fruit and nut trees) which are included in both the Tree Cover & Forests and the Croplands products. Furthermore, specific vegetations types are not included in the HRL-
+
+[^1]: HRL Forest
+
+ Production 2012
+
+ Reference year: 2012 ± 1 year + target accuracy: + 90% overall accuracy for status layers + |
+
+ Production 2015
+
+ Reference year: 2015 ± 1 year + target accuracy: + Minimum 90% user's/producer's accuracy for TCD and DLT + |
+
+ Production 2018
+
+ Reference year: 2018 ± 1 year + target accuracy: + Minimum 90% user's/producer's accuracy for TCD, DLT and TCPC + |
+
|
+ Legend: + TCD: Tree Cover Density + FTY: Forest Type + DLT: Dominant Leaf Type + TCDC: Tree Cover Density Change + DLTC: Dominant Leaft Type Change + |
+
+ TCCM: Tree Cover Change Mask + BCD: Broadleaved Cover Density + CCD: Coniferous Cover Density + * discontinued + ** continued as Tree Cover Presence Change + |
+
↓
+ From 2018 onward replaced by
yearly updates of HRL Tree Cover & Forests
+ yearly updates of HRL Tree Cover & Forests
HRL Grassland
+
+ Production 2015
+
+ Reference year: 2015 ± 1 year + target accuracy: + Min. 85% OA of GRA status layer + per biogeographical regions + |
+
+ Production 2018
+
+ Reference year: 2018 ± 1 year + target accuracy: + Min. 85% OA for GRA status layer & min. + 80% OA for change layer per + biogeographical regions + |
+
|
+ Legend: + GRA: Grassland + GRAC: Grassland Change + |
+
↓
+ From 2017 onward replaced by
yearly updates of HRL Grasslands
+ yearly updates of HRL Grasslands
↓
+ From 2017 onward yearly updates of HRL Croplands
+| Elements included in the tree covered area (if tree cover can be detected from the 10-metre imagery) |
+ Elements excluded from tree covered area (if no tree cover can be detected from the 10-metre imagery) |
+
+
|
+
+
|
+
| Name of layer | +Acronym | +Abbreviation | +Data format | +Metadata | +
| Tree Cover Density (10 m) | +TCD | +TCD_S2018_R10m TCD_S2019_R10m ... TCD_S2023_R10m |
+ Tiles of Cloud-Optimized GeoTIFF aligned with the 100km LAEA grid and with embedded colormaps, as well as separate colour legends in the formats \*.qml, \*.sld and \*.lyr | +XML metadata files according to INSPIRE metadata standards and GDAL-style Permanent Auxiliary Metadata (PAM)\*.aux.xml including statistics and Raster Attribute Table | +
| Tree Cover Density (100 m) | +TCD | +TCD_S2018_R100m TCD_S2019_R100m ... TCD_S2023_R100m |
+ ||
| Dominant Leaf Type (10 m) | +DLT | +DLT_S2018_R10m DLT_S2019_R10m ... DLT_S2023_R10m |
+ ||
| Tree Cover Presence Change (20 m) | +TCPC | +TCPC_C2018-2021_R20m | +||
| Dominant Leaf Type Change (20 m) | +DLTC | +DLTC_C2018-2021_R20m | +||
| Forest Type (10 m) | +FTY | +FTY_S2018_R10m FTY_S2021_R10m |
+ ||
| Forest Type (100 m) | +FTY | +FTY_S2018_R100m FTY_S2021_R100m |
+ ||
| Forest Additional Support Layer (10 m) | +FADSL | +FADSL_S2018_R10m FADSL_S2021_R10m |
+ ||
| Broadleaved Cover Density (100 m) | +BCD | +BCD_S2018_R100m BCD_S2019_R100m ... BCD_S2023_R100m |
+ ||
| Coniferous Cover Density (100 m) | +CCD | +CCD_S2018_R100m CCD_S2019_R100m ... CCD_S2023_R100m |
+ ||
| Tree Cover Density Confidence Layer | +TCDCL | +TCDCL_S2018_R10m TCDCL_S2019_R10m ... TCDCL_S2023_R10m |
+ ||
| Dominant Leaf Type Confidence Layer (10 m) | +DLTCL | +DLTCL_S2018_R10m DLTCL_S2019_R10m ... DLTCL_S2023_R10m |
+ ||
| Tree Cover Presence Change Confidence Layer (20 m) | +TCPCCL | +TCPCCL_C2018-2021_R20m | +
| Name of layer | +Acronym | +Spatial coverage | +Coordinate reference system (WKT) | +
| Tree Cover Density | +TCD | +5.751.002 km² (covering the full EEA-38) | +
+
+PROJCS["ETRS89-extended / LAEA Europe",
+ GEOGCS["ETRS89",
+ DATUM ["European_Terrestrial_Reference_System_1989",
+ SPHEROID ["GRS 1980",6378137,298.257222101,
+ AUTHORITY["EPSG","7019"]],
+ AUTHORITY["EPSG","6258"]],
+ PRIMEM["Greenwich", 0,
+ AUTHORITY["EPSG","8901"]],
+ UNIT["degree", 0.0174532925199433,
+ AUTHORITY["EPSG","9122"]],
+ AUTHORITY["EPSG","4258"]],
+ PROJECTION ["Lambert_Azimuthal_Equal_Area"],
+ PARAMETER ["latitude_of_center",52],
+ PARAMETER ["longitude_of_center", 10],
+ PARAMETER ["false_easting",4321000],
+ PARAMETER["false_northing",3210000],
+ UNIT["metre", 1,
+ AUTHORITY["EPSG","9001"]],
+ AXIS["Northing", NORTH],
+ AXIS["Easting", EAST],
+ AUTHORITY["EPSG","3035"]]
+
+```
+
+ + Except for French DOMs where the following CRS are used: + YT: EPSG 32738 + RE: EPSG 32740 + MQ: EPSG 32620 + GP: EPSG 32620 + GF: EPSG 32620 + |
+
| Dominant Leaf Type | DLT | ||
| Tree Cover Presence Change | TCPC | ||
| Dominant Leaf Type Change | DLTC | ||
| Forest Type | FTY | ||
| Forest Additional Support Layer | FADSL | ||
| Broadleaved Cover Density | BCD | ||
| Coniferous Cover Density | CCD | ||
| Tree Cover Density Confidence Layer | TCDCL | ||
| Dominant Leaf Type Confidence Layer | DLTCL | ||
| Tree Cover Presence Change Confidence Layer | TCPCCL |
| Name of layer | +Acronym | +Classified feature | +Class coding | +
| Tree Cover Density | +TCD | +Tree cover; tree cover density from 1-100%. According to the vertical projection of tree crowns to a horizontal earth's surface as assessed by means of VHR satellite imagery with sub-metre resolution | +0: all non-tree covered areas 1-100: tree cover density in % 255: outside area |
+
| Dominant Leaf Type | +DLT | +Dominant leaf type: broadleaved or coniferous. | +0: all non-tree covered areas 1: broadleaved trees 2: coniferous trees 255: outside area |
+
| Tree Cover Presence Change | +TCPC | +Increase or decrease of tree cover extent. | +0: unchanged areas with no tree cover 1: new tree cover 2: loss of tree cover 10: unchanged areas with tree cover 255: outside area |
+
| Dominant Leaf Type Change | +DLTC | +Various possible leaf type changes between two reference years. | +0: unchanged areas with no tree cover 1: new broadleaved cover 2: new coniferous cover 3: loss of broadleaved cover 4: loss of coniferous cover 10: unchanged areas with tree cover 255: outside area |
+
| Forest Type | +FTY | +Forest Type: broadleaved or coniferous, largely following the FAO forest definition: >TCD 10%; >0.5 ha MMU; Urban trees and trees under predominantly agricultural use are |
+ 0: all non-forest areas 1: broadleaved forest 2: coniferous forest 255: outside area |
+
| Forest Additional Support Layer | +FADSL | +Trees in urban context, trees predominantly used for agricultural practices. | +0: all non-tree covered areas, and tree cover without urban context or agricultural use 3: trees predominantly used for agricultural practices - broadleaved (from CLC2018) 4: trees in urban context - broadleaved and coniferous (from IMD2018) 5: trees in urban context - broadleaved and coniferous (from CLC2018) 255: outside area |
+
| Broadleaved Cover Density | +BCD | +Aggregated density of broadleaved trees. Percentage of broadleaved pixels in the DLT for the respective reference year | +0: all non-broadleaved covered areas 1-100: broadleaved cover density in % 255: outside area |
+
| Coniferous Cover Density | +CCD | +Aggregated density of coniferous trees. Percentage of coniferous pixels in the DLT for the respective reference year | +0: all non-coniferous covered areas 1-100: coniferous cover density in % 255: outside area |
+
| Tree Cover Density Confidence Layer | +TCDCL | +Standard deviation computed from the total variance as defined in [1] | +0-100: standard deviation of TCD estimate 253: all non-tree covered areas 255: outside area |
+
| Dominant Leaf Type Confidence Layer | +DLTCL | +Probability margin (i.e. difference of probabilities for highest and second highest ranked leaf type class) | +0-100: classification confidence 253: all non-tree covered areas 255: outside area |
+
| Tree Cover Presence Change Confidence Layer | +TCPCCL | +Change in tree cover probability from 2018 to 2021. Higher absolute values signal higher confidence. | +0-100: change confidence 253: all non-tree covered areas 255: outside area |
+
+
+
+
+
+
+
+
+
+
+
+
+```
+
+```{=html}
+Table 8-2: HRL TCD validation results at EU27 level
+| TCD 2018 - EU27 Weighted | |||||
| Product | +Reference | +Total | +User acc. | +CI95% | +|
| TCD <30% | +TCD ≥30% | +||||
| TCD <30% | +3632.966 | +48.047 | +3681.012 | +98.69% | +0.19% | +
| TCD ≥30% | +122.137 | +2907.069 | +3029.206 | +95.97% | +0.33% | +
| Total | +3755.103 | +2955.116 | +6710.218 | ++ | + |
| Prod. Acc. | +96.75% | +98.37% | ++ | + | + |
| CI 95% | +0.30% | +0.21% | ++ | + | + |
| TCD 2021 - EU27 Weighted | |||||
| Product | +Reference | +Total | +User acc. | +CI95% | +|
| TCD <30% | +TCD ≥30% | +||||
| TCD <30% | +3648.468 | +60.333 | +3708.800 | +98.37% | +0.21% | +
| TCD ≥30% | +133.621 | +2867.798 | +3001.418 | +95.55% | +0.34% | +
| Total | +3782.088 | +2928.130 | +6710.218 | ++ | + |
| Prod. Acc. | +96.47% | +97.94% | ++ | + | + |
| CI 95% | +0.31% | +0.24% | ++ | + | + |
| TCD 2022 - EU27 Weighted | |||||
| Product | +Reference | +Total | +User acc. | +CI95% | +|
| TCD <30% | +TCD ≥30% | +||||
| TCD <30% | +4369.710 | +132.183 | +4501.893 | +97.06% | +0.28% | +
| TCD ≥30% | +154.245 | +2845.181 | +2999.427 | +94.86% | +0.37% | +
| Total | +4523.956 | +2977.364 | +7501.320 | ++ | + |
| Prod. Acc. | +96.59% | +95.56% | ++ | + | + |
| CI 95% | +0.30% | +0.34% | ++ | + | + |
+
+
+
+
+
+
+
+
+
+
+
+```
+
+### Dominant Leaf Type
+
+For the reference years 2018, 2021 and 2022, the **Dominant Leaf Type (DLT)** layers prove to accurately depict the tree coverage over the EU27 and EEA38 countries and their discrimination between broadleaved trees or coniferous trees. The thematic accuracies systematically reach the 90% target (considering the margin error depicted by the confidence interval) with the exception of the User's accuracy for broadleaved class at EEA38 level (still above 89%). Confusions matrices for the coverage of the EU27 and the EEA39 are presented in Table 8-4 and Table 8-5. The higher commission errors for the broadleaved class are dominated by two factors:
+
+- At the borders of tree cover canopies to other vegetations types the DLT tends to overestimate the extent of the tree cover. This is closely related to the spatial uncertainties of the input data (see 7.4).
+- The mixed spectral signal at such borders typically resembles broadleaved rather than coniferous trees.
+
+An illustration of the issue is provided in Figure 8-5. While some of the commission errors have been reduced for the production of the reference years 2022 and 2023 the significantly better user's accuracies for 2022 (Table 8-5) might be partially caused by the response design focusing on a plausibility analysis only (section 8.3)
+
+
+
+```{=html}
+Table 8-3: HRL TCD validation results at EEA38 Level
+| TCD 2018 - EEA38 Weighted | |||||
| Product | +Reference | +Total | +User acc. | +CI95% | +|
| TCD <30% | +TCD ≥30% | +||||
| TCD <30% | +5879.209 | +180.076 | +6059.285 | +97.03% | +0.28% | +
| TCD ≥30% | +147.293 | +3563.293 | +3710.586 | +96.03% | +0.33% | +
| Total | +6026.502 | +3743.369 | +9769.870 | ++ | + |
| Prod. Acc. | +97.56% | +95.19% | ++ | + | + |
| CI 95% | +0.26% | +0.36% | ++ | + | + |
| TCD 2021 - EEA38 Weighted | |||||
| Product | +Reference | +Total | +User acc. | +CI95% | +|
| TCD <30% | +TCD ≥30% | +||||
| TCD <30% | +5897.438 | +194.569 | +6092.006 | +96.81% | +0.29% | +
| TCD ≥30% | +152.818 | +3525.046 | +3677.864 | +95.84% | +0.33% | +
| Total | +6050.256 | +3719.614 | +9769.870 | ++ | + |
| Prod. Acc. | +97.47% | +94.77% | ++ | + | + |
| CI 95% | +0.26% | +0.37% | ++ | + | + |
| TCD 2022 - EEA38 Weighted | |||||
| Product | +Reference | +Total | +User acc. | +CI95% | +|
| TCD <30% | +TCD ≥30% | +||||
| TCD <30% | +5989.833 | +225.207 | +6215.040 | +96.38% | +0.31% | +
| TCD ≥30% | +155.430 | +3640.400 | +3795.830 | +95.91% | +0.33% | +
| Total | +6145.263 | +3865.607 | +10010.870 | ++ | + |
| Prod. Acc. | +97.47% | +94.17% | ++ | + | + |
| CI 95% | +0.26% | +0.39% | ++ | + | + |
+
+
+
+
+
+
+
+
+
+
+
+```
+
+```{=html}
+Table 8-4: DLT validation results at EU27 level
+| DLT 2018 - EU27 Weighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| No trees | +Broadleaved | +Coniferous | +Total | +|||
| No trees | +3985.259 | +39.052 | +10.000 | +4034.312 | +98.78% | +0.18% | +
| Broadleaved | +114.051 | +1188.796 | +17.029 | +1319.876 | +90.07% | +0.50% | +
| Coniferous | +39.046 | +6.007 | +1307.978 | +1353.031 | +96.67% | +0.30% | +
| Total | +4138.356 | +1233.856 | +1335.007 | +6707.218 | ++ | + |
| Prod. Acc. | +96.30% | +96.35% | +97.98% | ++ | + | + |
| CI 95% | +0.32% | +0.31% | +0.24% | ++ | + | + |
| DLT 2021 - EU27 Weighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| No trees | +Broadleaved | +Coniferous | +Total | +|||
| No trees | +4016.994 | +49.142 | +11.239 | +4077.376 | +98.52% | +0.20% | +
| Broadleaved | +110.046 | +1180.800 | +18.000 | +1308.846 | +90.22% | +0.50% | +
| Coniferous | +47.178 | +3.009 | +1270.809 | +1320.997 | +96.20% | +0.32% | +
| Total | +4174.219 | +1232.952 | +1300.048 | +6707.218 | ++ | + |
| Prod. Acc. | +96.23% | +95.77% | +97.75% | ++ | + | + |
| CI 95% | +0.32% | +0.34% | +0.25% | ++ | + | + |
| DLT 2022 - EU27 Weighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| No trees | +Broadleaved | +Coniferous | +Total | +|||
| No trees | +4039.374 | +38.070 | +16.022 | +4093.466 | +98.68% | +0.19% | +
| Broadleaved | +54.017 | +1215.836 | +37.007 | +1306.860 | +93.03% | +0.43% | +
| Coniferous | +18.017 | +10.002 | +1279.974 | +1307.994 | +97.86% | +0.24% | +
| Total | +4111.409 | +1263.908 | +1333.003 | +6708.320 | ++ | + |
| Prod. Acc. | +98.25% | +96.20% | +96.02% | ++ | + | + |
| CI 95% | +0.22% | +0.32% | +0.33% | ++ | + | + |
+
+
+
+
+
+
+
+
+
+
+
+```
+
+### Change layers
+
+The validation of the **Tree Cover Presence Change (TCPC)** and **Dominant Leaf Type Change (DLTC)** suggest excellent User's accuracies and with nearly no false positives. The Producer's accuracies for **Tree Cover Losses** are somewhat lower but still between 83% and 87%. The Producer's accuracies of gains are evaluated somewhat lower with potential omissions of more than 50%. On the one hand, this appears plausible considering the generally subtler signals from gradual gains over time spans of three years. On the other hand, considering the general difficulty to sample for change omissions this result should be interpreted with caution:
+
+Additional sampling (see section 8.2) was performed in the frame of change layers thematic assessment and the weighting factor linked to it have a significant impact on accuracy figures. While the additional sampling allows to get further samples within change area, the weighting factor mandatory to counterbalance the sampling effort between commission strata (i.e. change areas as labelled by the layer) and omission strata (i.e. stable areas as labelled by the layer). Due to the limited areas concerned by change, the weighting factor give an insignificant weight to additional samples within the change areas.
+
+An error linked with a sample belonging to the omission strata (weight factor = 1) impacts the accuracy figures differently than an error linked with a sample belonging to the commission strata (weight factor around 0.002). Weighted and unweighted confusion matrices are presented in Table 8-6 and Table 8-7 to allow a better understanding of the results. While the weighted results present statistically corrected figures, it is currently not obvious whether they actually reflect the layer's quality. For example, at EEA38 level, the “New coniferous” class of the **DLTC** layer presents a producer accuracy of only 22%, whereas only 4 samples out of 51 indicate an omission of gains in coniferous tree cover. It is therefore important to also consider the unweighted matrices which provide a complementary overview of the quality.
+
+```{=html}
+Table 8-5: DLT validation results at EEA38 Level
+| DLT 2018 - EEA38 Weighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| No trees | +Broadleaved | +Coniferous | +Total | +|||
| No trees | +5364.541 | +76.064 | +35.020 | +5475.624 | +97.97% | +0.24% | +
| Broadleaved | +137.098 | +1458.973 | +48.085 | +1644.156 | +88.74% | +0.53% | +
| Coniferous | +53.178 | +13.025 | +1555.887 | +1622.090 | +95.92% | +0.33% | +
| Total | +5554.817 | +1548.061 | +1638.992 | +8741.870 | ++ | + |
| Prod. Acc. | +96.57% | +94.25% | +94.93% | ++ | + | + |
| CI 95% | +0.30% | +0.39% | +0.37% | ++ | + | + |
| DLT 2021 - EEA38 Weighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| No trees | +Broadleaved | +Coniferous | +Total | +|||
| No trees | +5398.229 | +90.204 | +37.395 | +5525.827 | +97.69% | +0.25% | +
| Broadleaved | +123.086 | +1463.861 | +45.015 | +1631.961 | +89.70% | +0.51% | +
| Coniferous | +50.232 | +10.012 | +1524.838 | +1585.082 | +96.20% | +0.32% | +
| Total | +5571.546 | +1564.077 | +1607.247 | +8742.870 | ++ | + |
| Prod. Acc. | +96.89% | +93.59% | +94.87% | ++ | + | + |
| CI 95% | +0.29% | +0.41% | +0.37% | ++ | + | + |
| DLT 2022 - EEA38 Weighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| No trees | +Broadleaved | +Coniferous | +Total | +|||
| No trees | +5475.738 | +66.075 | +28.017 | +5569.830 | +98.31% | +0.22% | +
| Broadleaved | +55.076 | +1688.878 | +11.007 | +1754.961 | +96.23% | +0.32% | +
| Coniferous | +17.050 | +4.000 | +1544.015 | +1565.064 | +98.66% | +0.19% | +
| Total | +5547.864 | +1758.953 | +1583.039 | +8889.856 | ++ | + |
| Prod. Acc. | +98.70% | +96.02% | +97.53% | ++ | + | + |
| CI 95% | +0.19% | +0.33% | +0.26% | ++ | + | + |
+
+
+
+
+
+
+
+
+
+
+
+
+```
+
+```{=html}
+Table 8-6: HRL Tree Cover & Forests Change layers validation results at EU27 Level
+| TCPC1821 - EU27 Weighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| Stable | Gain | Loss | Total | +|||
| Stable | 6665.2 | 4.0 | 5.0 | 6674.2 | 99.9% | 0.08% |
| Gain | 0.1 | 3.0 | 3.0 | 98.3% | 0.80% | |
| Loss | 1.0 | 0.0 | 31.9 | 32.9 | 96.9% | 1.08% |
| Total | 6666.3 | 7.0 | 36.9 | 6710.2 | ||
| Prod. Acc. | 100.0% | 42.5% | 86.5% | |||
| CI95% | 0.03% | 3.06% | 2.12% | |||
| TCPC1821 - EU27 Unweighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| Stable | Gain | Loss | Total | +|||
| Stable | 8143 | 5 | 5 | 8153 | 99.9% | 0.07% |
| Gain | 7 | 150 | 157 | 95.5% | 1.28% | |
| Loss | 4 | 2 | 1388 | 1394 | 99.6% | 0.41% |
| Total | 8154 | 157 | 1393 | 9704 | ||
| Prod. Acc. | 99.9% | 95.5% | 99.6% | |||
| CI95% | 0.08% | 1.28% | 0.37% | |||
| DLTC1821 - EU27 Weighted | ||||||||
| Product | +Reference | +User acc. | CI95% | +|||||
| Stable | New Broad. | New Conif. | Loss Broad. | Loss Conif. | Total | +|||
| Stable | 6663.2 | 3.0 | 1.0 | 1.0 | 4.0 | 6672.2 | 99.9% | 0.08% |
| New Broad. | 0.1 | 2.7 | 0.0 | 2.7 | 97.3% | 1.00% | ||
| New Conif. | 0.0 | 0.3 | 0.3 | 96.1% | 1.21% | |||
| Loss Broad. | 0.0 | 0.0 | 5.7 | 1.1 | 6.9 | 83.7% | 2.29% | |
| Loss Conif. | 1.0 | 0.0 | 0.1 | 25.0 | 26.1 | 95.8% | 1.15% | |
| Total | 6664.3 | 5.7 | 1.3 | 6.8 | 30.1 | 6708.2 | ||
| Prod. Acc. | 100.0% | 46.7% | 21.7% | 84.0% | 83.1% | |||
| CI95% | 0.03% | 3.09% | 2.56% | 2.27% | 2.32% | |||
| DLTC1821 - EU27 Unweighted | ||||||||
| Product | +Reference | +User acc. | CI95% | +|||||
| Stable | New Broad. | New Conif. | Loss Broad. | Loss Conif. | Total | +|||
| Stable | 8141 | 4 | 1 | 1 | 4 | 8151 | 99.9% | 0.07% |
| New Broad. | 7 | 100 | 3 | 110 | 90.9% | 1.78% | ||
| New Conif. | 3 | 44 | 47 | 93.6% | 1.52% | |||
| Loss Broad. | 3 | 1 | 244 | 13.0 | 261 | 93.5% | 1.53% | |
| Loss Conif. | 1 | 1 | 13 | 1118 | 1133 | 98.7% | 0.71% | |
| Total | 8152 | 109 | 48 | 258 | 1135 | 9702 | ||
| Prod. Acc. | 99.9% | 91.7% | 91.7% | 94.6% | 98.5% | |||
| CI95% | 0.08% | 1.71% | 1.71% | 1.40% | 0.75% | |||
+
+
+
+
+
+
+
+
+
+
+
+
+```
+
+# Terms of use and product technical support
+
+## Terms of use
+
+The product(s) described in this document is/are created in the frame of the Copernicus programme of the European Union by the European Environment Agency (product custodian) and is/are owned by the European Union. The product(s) can be used following Copernicus full free and open data policy, which allows the use of the product(s) also for any commercial purpose. Derived products created by end users from the product(s) described in this document are owned by the end users, who have all intellectual rights to the derived products.
+
+## Citation
+
+In cases of re-dissemination of the product(s) described in this document or when the product(s) is/are used to create a derived product it is required to provide a reference to the source. A template is provided below:
+
+> "© European Union, Copernicus Land Monitoring Service Table 8-7: HRL Tree Cover & Forests Change layers validation results at EEA38 level
+| TCPC1821 - EEA38 Weighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| Stable | Gain | Loss | Total | +|||
| Stable | 9713.4 | 5.0 | 7.0 | 9725.4 | 99.9% | 0.06% |
| Gain | 0.1 | 4.1 | 4.1 | 98.6% | 0.20% | |
| Loss | 1.0 | 0.0 | 39.3 | 40.3 | 97.4% | 0.28% |
| Total | 9714.5 | 9.1 | 46.3 | 9769.9 | ||
| Prod. Acc. | 100.0% | 44.8% | 84.9% | |||
| CI95% | 0.02% | 0.86% | 0.62% | |||
| TCPC1821 - EEA38 Unweighted | ||||||
| Product | +Reference | +User acc. | +CI95% | +|||
| Stable | Gain | Loss | Total | +|||
| Stable | 11674.0 | 6.0 | 7.0 | 11687.0 | 99.9% | 0.06% |
| Gain | 9.0 | 186.0 | 195.0 | 95.4% | 0.36% | |
| Loss | 9.0 | 2.0 | 1849.0 | 1860.0 | 99.4% | 0.13% |
| Total | 11692.0 | 194.0 | 1856.0 | 13742.0 | ||
| Prod. Acc. | 99.8% | 95.9% | 99.6% | |||
| CI95% | 0.07% | 0.35% | 0.11% | |||
| DLTC1821 - EEA38 Weighted | ||||||||
| Product | +Reference | +User acc. | CI95 % | +|||||
| Stable | New Broad. | New Conif. | Loss Broad. | Loss Conif. | Total | +|||
| Stable | 8695.4 | 3.0 | 1.0 | 1.0 | 4.0 | 8704.4 | 99.9% | 0.06% |
| New Broad. | 0.1 | 3.7 | 0.0 | 3.8 | 98.0% | 0.24% | ||
| New Conif. | 0.0 | 0.0 | 0.3 | 0.3 | 95.3% | 0.37% | ||
| Loss Broad. | 0.0 | 0.0 | 8.0 | 1.1 | 9.1 | 87.4% | 0.58% | |
| Loss Conif. | 1.0 | 0.0 | 0.1 | 30.1 | 31.2 | 96.5% | 0.32% | |
| Total | 8696.5 | 6.8 | 1.3 | 9.1 | 35.2 | 8748.9 | ||
| Prod. Acc. | 100% | 55.3% | 22.3% | 87.9% | 85.5% | |||
| CI95% | 0.02% | 0.86% | 0.72% | 0.57% | 0.61% | |||
| DLTC1821- EEA38 Unweighted | ||||||||
| Product | +Reference | +User acc. | CI95 % | +|||||
| Stable | New Broad. | New Conif. | Loss Broad. | Loss Conif. | Total | +|||
| Stable | 10656 | 4 | 1 | 1 | 4 | 10666 | 99.9% | 0.05% |
| New Broad. | 8 | 133 | 3 | 144 | 92.4% | 0.46% | ||
| New Conif. | 1 | 3 | 47 | 51 | 92.2% | 0.47% | ||
| Loss Broad. | 8 | 1 | 328 | 21 | 358 | 91.6% | 0.48% | |
| Loss Conif. | 1 | 1 | 13 | 1487 | 1502 | 99.0% | 0.17% | |
| Total | 10674 | 142 | 51 | 342 | 1512 | 12721 | ||
| Prod. Acc. | 99.8% | 93.7% | 92.2% | 95.9% | 98.3% | |||
| CI95% | 0.07% | 0.42% | 0.47% | 0.34% | 0.22% | |||
| Class Code | Class Name | Red | Green | Blue | |
| 0 | all non-tree covered areas | 240 | 240 | 240 | |
| 1 | 1% tree cover density | 253 | 255 | 115 | |
| 2-49 | 2% to 49% tree cover density | colour shades in between | |||
| 50 | 50% tree cover density | 76 | 230 | 0 | |
| 51-99 | 51% to 99% tree cover density | colour shades in between | |||
| 100 | 100% tree cover density | 28 | 92 | 36 | |
| 255 | outside area | 0 | 0 | 0 | |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | all non-tree covered areas | 240 | 240 | 240 | |
| 1 | broadleaved trees | 70 | 158 | 74 | |
| 2 | coniferous trees | 28 | 92 | 36 | |
| 255 | outside area | 0 | 0 | 0 |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | all non-forest areas | 240 | 240 | 240 | |
| 1 | broadleaved forest | 70 | 158 | 74 | |
| 2 | coniferous forest | 28 | 92 | 36 | |
| 3 | mixed zones (only for aggregated 100m layer) | 76 | 133 | 67 | |
| 255 | outside area | 0 | 0 | 0 |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | all non-tree covered areas, and tree cover without urban context or agricultural use | 240 | 240 | 240 | |
| 3 | trees predominantly used for agricultural practices – broadleaved (from CLC2018) | 204 | 173 | 71 | |
| 4 | trees in urban context – broadleaved and coniferous (from IMD 2018) | 255 | 85 | 0 | |
| 5 | trees in urban context – broadleaved and coniferous (from CLC 2018) | 168 | 56 | 0 | |
| 255 | outside area | 0 | 0 | 0 |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | unchanged areas with no tree cover | 255 | 255 | 255 | |
| 1 | new broadleaved cover | 20 | 255 | 20 | |
| 2 | new coniferous cover | 0 | 150 | 0 | |
| 3 | loss of broadleaved cover | 255 | 0 | 0 | |
| 4 | loss of coniferous cover | 255 | 128 | 0 | |
| 10 | unchanged areas with tree cover | 191 | 191 | 191 | |
| 255 | outside area | 0 | 0 | 0 |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | unchanged areas with no tree cover | 255 | 255 | 255 | |
| 1 | new tree cover | 20 | 255 | 20 | |
| 2 | loss of tree cover | 255 | 0 | 0 | |
| 10 | unchanged areas with tree cover | 191 | 191 | 191 | |
| 255 | outside area | 0 | 0 | 0 |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | all non-broadleaved covered areas | 240 | 240 | 240 | |
| 1 | 1% broadleaved cover density | 253 | 255 | 115 | |
| 2-49 | 2-49% broadleaved cover density | colour shades in between | |||
| 50 | 50% broadleaved cover density | 76 | 230 | 0 | |
| 51-99 | 51-99% broadleaved cover density | colour shades in between | |||
| 100 | 100% broadleaved cover density | 28 | 92 | 36 | |
| 255 | outside area | 0 | 0 | 0 | |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | all non-coniferous covered areas | 240 | 240 | 240 | |
| 1 | 1% coniferous cover density | 253 | 255 | 115 | |
| 2-49 | 2-49% coniferous cover density | colour shades in between | |||
| 50 | 50% coniferous cover density | 76 | 230 | 0 | |
| 51-99 | 51-99% coniferous cover density | colour shades in between | |||
| 100 | 100% coniferous cover density | 28 | 92 | 36 | |
| 255 | outside area | 0 | 0 | 0 | |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | 0% prediction interval | 8 | 99 | 0 | |
| 1-49 | 1-49% prediction interval | colour shades in between | |||
| 50 | 50% prediction interval | 255 | 255 | 0 | |
| 51-99 | 51-99% prediction interval | colour shades in between | |||
| 100 | 100% prediction interval | 255 | 0 | 0 | |
| 253 | all non-tree covered areas | 240 | 240 | 240 | |
| 255 | outside area | 0 | 0 | 0 | |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | 0% classification confidence | 255 | 0 | 0 | |
| 1-49 | 1-49% classification confidence | colour shades in between | |||
| 50 | 50% classification confidence | 255 | 255 | 0 | |
| 51-99 | 51-99% classification confidence | colour shades in between | |||
| 100 | 100% classification confidence | 8 | 99 | 0 | |
| 253 | all non-tree covered areas | 240 | 240 | 240 | |
| 255 | outside area | 0 | 0 | 0 | |
| Class Code | Class Name | Red | Green | Blue | |
| 0 | 0% change confidence | 255 | 0 | 0 | |
| 1-49 | 1-49% change confidence | colour shades in between | |||
| 50 | 50% change confidence | 255 | 255 | 0 | |
| 51-99 | 51-99% change confidence | colour shades in between | |||
| 100 | 100% change confidence | 8 | 99 | 0 | |
| 253 | all non-changed areas | 240 | 240 | 240 | |
| 255 | outside area | 0 | 0 | 0 | |
| Coastal Zones Land Cover and Land Use status map 2012 | +||||
| Product Title / Content | +Product Short Name | +|||
| Coastal Zones Land Cover and Land Use status map 2012 | +CZ_LCLU_2012 | +|||
| Product Definition | +||||
| The LCLU status map provides a detailed Land Cover and Land Use map of the coastal land zone within EEA39, for a specific reference year, based on VHR satellite imagery and coastal zones nomenclature. | +||||
| Input Data Sources | +||||
|
+ 1) Coastal Zones AOI (adapted buffer of EU-Hydro dataset) + 2)Image data: +
+ CLC 2012/2018; Urban Atlas 2012/2018; GIO HR Layers; DWH_MG2_CORE_01 Coverage 2 (RapidEye, 5m); National orthophoto WMS, Google Earth, Bing Maps; Numerous additional reference and in-situ data sources. + |
+ ||||
| Methodology | +||||
| Mixture of automatic classification routines and visual interpretation of VHR satellite data implementing tailored Coastal Zones nomenclature. | +||||
| Geographic Coverage | +||||
| Customized 10 km landwards buffer Coastal land zone and 25 km seawards buffer including territorial waters within EEA39. | +||||
| Projection | +||||
| ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) | +||||
| Temporal Reference | +||||
| Reference year 2012 (products of the reference year 2012 are produced based on 2011-2014 reference data) | +||||
| Geometric Resolution / Equivalent Scale | +Nomenclature | +|||
| 1:10,000 | +71 thematic classes | +|||
| Minimum Mapping Unit | +Minimum Mapping Length | +Minimum Mapping Width | +||
| 0.5 ha | +N/A | +10 m | +||
| ≥ 80% for class specific user and produced accuracies + ≥ 85 % overall accuracy + Positional accuracy: RMSE ≤ 5m |
+ ||||
| Coastal Zones Land Cover and Land Use status map 2018 | +||||
| Product Title / Content | +Product Short Name | +|||
| Coastal Zones Land Cover and Land Use status map 2018 | +CZ_LCLU_2018 | +|||
| Product Definition | +||||
| The LCLU status map provides a detailed Land Cover and Land Use map of the coastal land zone within EEA39, for a specific reference year, based on VHR satellite imagery and coastal zones nomenclature. | +||||
| Input Data Sources | +||||
|
+ 1)Coastal Zones AOI (adapted buffer of EU-Hydro dataset) + 2)Image data: +
+ CLC 2012/2018; Urban Atlas 2012/2018; GIO HR Layers; National orthophoto WMS, Google Earth, Bing Maps; Numerous additional reference and in-situ data sources. + |
+ ||||
| Methodology | +||||
| Produced by merging LCLU status 2012 and LCLU change map in a GIS environment. | +||||
| Geographic Coverage | +||||
| Customized 10 km landwards buffer Coastal land zone and 25 km seawards buffer including territorial waters within EEA39. | +||||
| Projection | +||||
| ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) | +||||
| Temporal Reference | +||||
| Reference year 2018 (products of the reference year 2018 are produced based on 2017-2019 reference data) | +||||
| Geometric Resolution / Equivalent Scale | +Nomenclature | +|||
| 1:10,000 | +71 thematic classes | +|||
| Minimum Mapping Unit | +Minimum Mapping Length | +Minimum Mapping Width | +||
| 0.5 ha | +N/A | +10 m | +||
| Thematic/Positional Product Accuracy | +||||
| ≥ 80% for class specific user and produced accuracies + ≥ 85 % overall accuracy + Positional accuracy: RMSE ≤ 5m |
+ ||||
| Coastal Zones Land Cover and Land Use change map 2012-2018 | +||||
| Product Title / Content | +Product Short Name | +|||
| Coastal Zones Land Cover and Land Use change map 2012-2018 | +CZ_LCLU change_2012_2018 |
+ |||
| Product Definition | +||||
| The LCLU change map provides a detailed Land Cover and Land Use change map of the coastal land zone within EEA39, between two specific reference years, based on VHR satellite imagery and coastal zones nomenclature. | +||||
| Input Data Sources | +||||
|
+ 1)Coastal Zones AOI (adapted buffer of EU-Hydro dataset) + 2) Image data: +
+ CLC 2012/2018; Urban Atlas 2012/2018; GIO HR Layers; National orthophoto WMS, Google Earth, Bing Maps; Numerous additional reference and in-situ data sources. + |
+ ||||
| Methodology | +||||
| Mixture of automatic classification routines and visual interpretation of VHR satellite data implementing tailored Coastal Zones nomenclature. | +||||
| Geographic Coverage | +||||
| Customized 10 km landwards buffer Coastal land zone and 25 km seawards buffer including territorial waters within EEA39. | +||||
| Projection | +||||
| ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) | +||||
| Temporal Reference | +||||
| Based on very high resolution (VHR) satellite imagery of the reference years 2012 and 2018 | +||||
| Geometric Resolution / Equivalent Scale | +Nomenclature | +|||
| 1:10,000 | +71 thematic classes | +|||
| Minimum Mapping Unit | +Minimum Mapping Length | +Minimum Mapping Width | +||
| 0.5 ha | +N/A | +10 m | +||
| Thematic/Positional Product Accuracy | +||||
| ≥ 80 % for class specific user and produced accuracies + ≥ 85 % overall accuracy + Positional accuracy: RMSE ≤ 5m |
+ ||||
| Level 1 | +Level 2 | +Level 3 | +Level 4 | +Level 5 | +Ecosystem types level 2 (MAES) |
+
| 1 Urban | +1.1 Urban fabric, industrial, commercial, public, military and private units | +1.1.1 Urban fabric (predominantly public and private units) | +1.1.1.1 Continuous urban fabric (IMD ≥80%) | ++ | Urban | +
| 1.1.1.2 Dense urban fabric (IMD ≥30-80%) | ++ | ||||
| 1.1.1.3 Low density fabric (IMD <30%) | ++ | ||||
| 1.1.2 Industrial, commercial, public and military units | +1.1.2.1 Industrial, commercial, public and military units (other) | ++ | |||
| 1.1.2.2 Nuclear energy plants and associated land | ++ | ||||
| 1.2.3 Port areas and associated land | +1.2.3.1 Cargo port | ++ | |||
| 1.2.3.2 Passenger port | ++ | ||||
| 1.2 Transport infrastructure | +1.2.3.3 Fishing port | ++ | |||
| 1.2.3.4 Naval port | ++ | ||||
| 1.2.3.5 Marinas | ++ | ||||
| 1.2.1 Road networks and associated land | +1.2.3.6 Local multi-functional harbours | ++ | |||
| 1.2.2 Railways and associated land | +1.2.3.7 Shipyards | ++ | |||
| + | 1.2.4 Airports and associated land | ++ | + | ||
| + | + | ||||
| 1.3 Mineral extraction, dump and construction sites, land without current use | +1.3.1 Mineral extraction, dump and construction sites | +1.3.1.1 Mineral extraction sites | ++ | ||
| 1.3.1.2 Dump sites | ++ | ||||
| 1.3.1.3 Construction sites | ++ | ||||
| 1.3.2 Land without current use | ++ | + | |||
| 1.4 Green urban, sports and leisure facilities | ++ | + | + | ||
| 2 Cropland | +2.1 Arable land | +2.1.1 Arable irrigated and non-irrigated land | ++ | + | Cropland | +
| 2.1.2 Greenhouses | ++ | + | |||
| 2.2 Permanent crops | +2.2.1 Vineyards, fruit trees and berry plantations | ++ | + | ||
| 2.2.2 Olive groves | ++ | + | |||
| 2.3 Heterogeneous agricultural area | +2.3.1 Annual crops associated with permanent crops | ++ | + | ||
| 2.3.2 Complex cultivation patterns | ++ | + | |||
| 2.3.3 Land principally occupied by agriculture with significant areas of natural vegetation | ++ | + | |||
| 2.3.4 Agro-forestry | ++ | + | |||
| + | |||||
| + | |||||
| + | |||||
| 3 Woodland and forest | +3.1 Broadleaved forest | +3.1.1 Natural & semi-natural broadleaved forest | ++ | + | Woodland and forest |
+
| 3.1.2 Highly artificial broadleaved plantations | ++ | + | |||
| 3.2 Coniferous forest | +3.2.1 Natural & semi-natural coniferous forest | ++ | + | ||
| 3.2.2 Highly artificial coniferous plantations | ++ | + | |||
| 3.3 Mixed forest | +3.3.1 Natural & semi-natural mixed forest | ++ | + | ||
| 3.3.2 Highly artificial mixed plantations | ++ | + | |||
| 3.4 Transitional woodland and scrub | ++ | + | + | ||
| 3.5 Lines of trees and scrub | ++ | + | + | ||
| 3.6 Damaged forest | ++ | + | + | ||
| 4 Grassland | +4.1 Managed grassland | ++ | + | + | Grassland | +
| 4.2 Natural & semi-natural grassland | +4.2.1 Semi-natural grassland | ++ | + | ||
| 5 Heathland and scrub | +alpine natural grassland | ++ | + | Heathland and shrub | +|
| 5.1 Heathland and moorland | ++ | + | + | ||
| 5.2 Alpine scrub land | ++ | + | + | ||
| 6 Open spaces with little or no vegetation |
+ 5.3 Sclerophyllous scrubs | ++ | + | + | Sparsely vegetated land |
+
| 6.1 Sparsely vegetated areas | +6.1.1 Sparse vegetation on sands | ++ | + | ||
| 6.1.2 Sparse vegetation on rocks | ++ | + | |||
| 6.2 Beaches, dunes, river banks | +6.2.1 Beaches and dunes | +6.2.1.1 Beaches | +6.2.1.1.1 Sandy beaches | +||
| 6.2.1.1.2 Shingle beaches | +|||||
| 6.2.1.2 Dunes | ++ | ||||
| 6.2.2 River banks | ++ | + | |||
| 6.3 Bare rocks, burnt areas, glaciers and perpetual snow | +6.3.1 Bare rocks, outcrops, cliffs | +6.3.1.1 Bare rocks and outcrops | ++ | ||
| 6.3.1.2 Coastal cliffs | ++ | ||||
| 6.3.2 Burnt areas (except burnt forest) | ++ | + | |||
| 6.3.3 Glaciers and perpetual snow | ++ | + | + | ||
| + | + | + | + | ||
| 7 Wetland | +7.1 Inland wetlands | +7.1.1 Inland marshes | ++ | + | Wetlands | +
| 7.1.2 Peat bogs | +7.1.2.1 Exploited peat bogs | ++ | Marine inlets and transitional waters |
+ ||
| 7.1.2.2 Unexploited peat bogs | ++ | ||||
| 7.2 Coastal wetlands | +7.2.1 Salt marshes | ++ | + | ||
| 7.2.2 Salines | ++ | + | |||
| 8 Water | +7.2.3 Intertidal flats | ++ | + | Rivers and lakes |
+ |
| 8.1 Water courses | +8.1.1 Natural & semi-natural water courses | ++ | + | ||
| 8.1.2 Highly modified water courses and canals | ++ | + | |||
| 8.1.3 Seasonally connected water courses (oxbows) | ++ | + | |||
| 8.2 Lakes and reservoirs | +8.2.1 Natural lakes | ++ | + | ||
| 8.2.2 Reservoirs | ++ | + | |||
| 8.2.3 Aquaculture ponds | ++ | + | |||
| 8.2.4 Standing water bodies of extractive industrial sites | ++ | Marine inlets and transitional waters |
+ |||
| 8.3 Transitional waters | +8.3.1 Lagoons | ++ | + | ||
| 8.3.2 Estuaries | ++ | + | |||
| 8.3.3 Marine inlets and fjords | ++ | + | Open ocean Coastal |
+ ||
| 8.4 Sea and ocean | +8.4.1 Open sea | ++ | + | ||
| 8.4.2 Coastal waters | ++ | + | |||
| Class code | +Class names | +Status map 2012 | +Status layer 2018 | +Change layer 2012-2018 | +||||||
| Counts | +Area (km²) | +Area (%) | +Counts | +Area (km²) | +Area (%) | +Area gain (km²) | +Area loss (km²) | +Diff (km²) | +||
| 11110 | +Continuous urban fabric (IM,D ≥ 80%) | +27762 | +3246,65 | +0,15% | +27873 | +3258,51 | +0,15% | +13,42 | +1,55 | +11,86 | +
| 11120 | +Dense urban fabric (IM,D ≥ 30-80%) | +161366 | +18827,99 | +0,84% | +161707 | +19084,97 | +0,86% | +264,94 | +7,96 | +256,98 | +
| 11130 | +Low density urban fabric (IM,D < 30%) | +553987 | +13767,87 | +0,62% | +556971 | +13943,46 | +0,63% | +182,48 | +6,90 | +175,58 | +
| 11210 | +Industrial, commercial, public and military units (other) | +376863 | +11314,42 | +0,51% | +385050 | +11862,07 | +0,53% | +586,14 | +38,49 | +547,64 | +
| 11220 | +Nuclear energy plants and associated land | +41 | +16,72 | +>0,01% | +50 | +18,74 | +>0,01% | +2,02 | ++ | 2,02 | +
| 12100 | +Road networks and associated land | +7736 | +4695,29 | +0,21% | +7817 | +4789,97 | +0,21% | +97,68 | +3,00 | +94,68 | +
| 12200 | +Railways and associated land | +3092 | +705,27 | +0,03% | +3141 | +713,83 | +0,03% | +9,11 | +0,55 | +8,55 | +
| 12310 | +Cargo port | +1794 | +546,25 | +0,02% | +1811 | +571,25 | +0,03% | +27,31 | +2,32 | +24,99 | +
| 12320 | +Passenger port | +504 | +23,34 | +>0,01% | +513 | +24,94 | +>0,01% | +1,62 | +0,02 | +1,59 | +
| 12330 | +Fishing port | +1480 | +29,51 | +>0,01% | +1518 | +30,56 | +>0,01% | +1,08 | +0,02 | +1,05 | +
| 12340 | +Naval port | +116 | +31,88 | +>0,01% | +116 | +32,23 | +>0,01% | +0,34 | ++ | 0,34 | +
| 12350 | +Marinas | +3506 | +145,88 | +0,01% | +3559 | +149,34 | +0,01% | +3,62 | +0,16 | +3,46 | +
| 12360 | +Local multi-functional harbours | +1285 | +35,54 | +>0,01% | +1293 | +35,98 | +>0,01% | +0,60 | +0,16 | +0,44 | +
| 12370 | +Shipyards | +402 | +40,54 | +>0,01% | +409 | +41,80 | +>0,01% | +1,32 | +0,06 | +1,27 | +
| 12400 | +Airports and associated land | +751 | +908,50 | +0,04% | +759 | +912,90 | +0,04% | +8,56 | +4,17 | +4,39 | +
| 13110 | +Mineral extraction sites | +14767 | +1449,72 | +0,07% | +14881 | +1510,91 | +0,07% | +164,41 | +103,23 | +61,19 | +
| 13120 | +Dump sites | +2013 | +117,60 | +0,01% | +2055 | +117,57 | +0,01% | +7,81 | +7,84 | +-0,03 | +
| 13130 | +Construction sites | +14015 | +602,97 | +0,03% | +10930 | +540,62 | +0,02% | +393,42 | +455,76 | +-62,34 | +
| 13200 | +Land without current use | +30742 | +840,84 | +0,04% | +29031 | +788,19 | +0,04% | +82,13 | +134,78 | +-52,65 | +
| 14000 | +Green urban, sports and leisure facilities | +101860 | +5827,75 | +0,26% | +103091 | +5896,32 | +0,26% | +100,40 | +31,83 | +68,57 | +
| 21100 | +Arable irrigated and non-irrigated land | +335945 | +122755,66 | +5,51% | +338372 | +122217,06 | +5,48% | +353,05 | +891,65 | +-538,61 | +
| 21200 | +Greenhouses | +28913 | +1549,81 | +0,07% | +30257 | +1703,81 | +0,08% | +202,90 | +48,90 | +154,00 | +
| 22100 | +Vineyards, fruit trees and berry plantations | +111145 | +15598,94 | +0,70% | +112828 | +15728,69 | +0,71% | +298,29 | +168,55 | +129,75 | +
| 22200 | +Olive groves | +108815 | +15297,06 | +0,69% | +109494 | +15283,00 | +0,69% | +41,30 | +55,35 | +-14,05 | +
| 23100 | +Annual crops associated with permanent crops | +4444 | +480,82 | +0,02% | +4466 | +479,17 | +0,02% | +1,30 | +2,94 | +-1,65 | +
| 23200 | +Complex cultivation patterns | +38258 | +3494,51 | +0,16% | +38368 | +3487,52 | +0,16% | +4,94 | +11,93 | +-6,99 | +
| 23300 | +Land principally occupied by agriculture with significant areas of natural vegetation | +23949 | +2854,33 | +0,13% | +24042 | +2851,10 | +0,13% | +4,55 | +7,78 | +-3,23 | +
| 23400 | +Agro-forestry (Mediterranean Areas) | +2743 | +1048,79 | +0,05% | +2747 | +1044,44 | +0,05% | +2,30 | +6,66 | +-4,35 | +
| 31100 | +Natural & semi-natural broadleaved forest | +426043 | +74640,42 | +3,35% | +428992 | +74527,39 | +3,34% | +470,67 | +583,71 | +-113,04 | +
| 31200 | +Highly artificial broadleaved plantations | +15290 | +6172,99 | +0,28% | +15525 | +6118,50 | +0,27% | +55,74 | +110,24 | +-54,50 | +
| 32100 | +Natural & semi-natural coniferous forest | +244012 | +76659,29 | +3,44% | +253200 | +74109,04 | +3,32% | +1299,62 | +3849,86 | +-2550,25 | +
| 32200 | +Highly artificial coniferous plantations | +4441 | +586,41 | +0,03% | +4806 | +661,02 | +0,03% | +109,87 | +35,26 | +74,61 | +
| 33100 | +Natural & semi-natural mixed forest | +101368 | +19043,31 | +0,85% | +102973 | +18897,99 | +0,85% | +273,64 | +418,96 | +-145,32 | +
| 33200 | +Highly artificial mixed plantations | +393 | +44,07 | +>0,01% | +453 | +46,92 | +>0,01% | +5,32 | +2,47 | +2,85 | +
| 34000 | +Transitional woodland and scrub | +171587 | +12240,05 | +0,55% | +194055 | +14594,22 | +0,65% | +4659,75 | +2305,58 | +2354,17 | +
| 35000 | +Lines of trees and scrub | +11844 | +241,67 | +0,01% | +11866 | +245,75 | +0,01% | +4,91 | +0,83 | +4,08 | +
| 36000 | +Damaged forest | +266 | +53,84 | +>0,01% | +668 | +325,50 | +0,01% | +322,92 | +51,25 | +271,67 | +
| 41000 | +Managed grassland | +276578 | +56337,34 | +2,53% | +277341 | +56130,94 | +2,52% | +84,91 | +291,31 | +-206,40 | +
| 42100 | +Semi-natural grassland | +362745 | +35404,02 | +1,59% | +364138 | +35253,64 | +1,58% | +366,50 | +516,88 | +-150,38 | +
| 42200 | +Alpine and sub-alpine natural grassland | +56 | +2,41 | +>0,01% | +72 | +2,86 | +>0,01% | +0,61 | +0,16 | +0,45 | +
| 51000 | +Heathland and moorland | +132860 | +44469,98 | +1,99% | +133002 | +44395,65 | +1,99% | +20,90 | +95,24 | +-74,33 | +
| 52000 | +Alpine scrub land | +1024 | +55,05 | +>0,01% | +1026 | +55,37 | +>0,01% | +0,38 | +0,07 | +0,32 | +
| 53000 | +Sclerophyllous scrubs | +110991 | +38964,49 | +1,75% | +111758 | +38558,07 | +1,73% | +124,68 | +531,10 | +-406,42 | +
| 61100 | +Sparse vegetation on sands | +7061 | +926,12 | +0,04% | +7142 | +926,09 | +0,04% | +6,07 | +6,10 | +-0,03 | +
| 61200 | +Sparse vegetation on rocks | +211328 | +44344,88 | +1,99% | +211545 | +44378,01 | +1,99% | +62,73 | +29,60 | +33,14 | +
| 62111 | +Sandy beach | +11675 | +1118,98 | +0,05% | +11708 | +1131,68 | +0,05% | +21,40 | +8,71 | +12,69 | +
| 62112 | +Shingle beach | +2477 | +106,09 | +>0,01% | +2470 | +106,33 | +>0,01% | +0,89 | +0,64 | +0,24 | +
| 62120 | +Dunes | +1458 | +527,47 | +0,02% | +1464 | +527,25 | +0,02% | +0,55 | +0,76 | +-0,21 | +
| 62200 | +River banks | +11843 | +973,52 | +0,04% | +11968 | +953,47 | +0,04% | +33,24 | +53,29 | +-20,05 | +
| 63110 | +Bare rocks and outcrops | +31194 | +15086,22 | +0,68% | +31260 | +15092,21 | +0,68% | +9,93 | +3,95 | +5,99 | +
| 63120 | +Coastal cliffs | +30377 | +1409,62 | +0,06% | +30355 | +1406,98 | +0,06% | +0,26 | +2,90 | +-2,64 | +
| 63200 | +Burnt areas (except burnt forest) | +448 | +129,50 | +0,01% | +540 | +183,74 | +0,01% | +177,52 | +123,28 | +54,24 | +
| 63300 | +Glaciers and perpetual snow | +3133 | +1148,63 | +0,05% | +3133 | +1142,30 | +0,05% | +0,22 | +6,54 | +-6,33 | +
| 71100 | +Inland marshes | +40724 | +5698,52 | +0,26% | +40835 | +5702,56 | +0,26% | +22,04 | +18,00 | +4,04 | +
| 71210 | +Exploited peat bog | +3119 | +1183,07 | +0,05% | +3142 | +1186,63 | +0,05% | +6,09 | +2,53 | +3,56 | +
| 71220 | +Unexploited peat bog | +63443 | +18937,96 | +0,85% | +63489 | +18921,81 | +0,85% | +0,78 | +16,93 | +-16,15 | +
| 72100 | +Salt marshes | +32063 | +5228,60 | +0,23% | +32109 | +5221,32 | +0,23% | +9,27 | +16,55 | +-7,28 | +
| 72200 | +Salines | +1087 | +707,59 | +0,03% | +1093 | +710,03 | +0,03% | +6,64 | +4,20 | +2,44 | +
| 72300 | +Intertidal flats | +7112 | +12142,34 | +0,54% | +7123 | +12148,68 | +0,54% | +9,81 | +3,48 | +6,33 | +
| 81100 | +Natural & semi-natural water courses | +19219 | +3428,76 | +0,15% | +19274 | +3463,89 | +0,16% | +59,76 | +24,63 | +35,13 | +
| 81200 | +Highly modified water courses and canals | +7553 | +722,79 | +0,03% | +7668 | +739,05 | +0,03% | +18,03 | +1,77 | +16,26 | +
| 81300 | +Seasonally connected water courses (oxbows) | +475 | +27,91 | +>0,01% | +490 | +28,42 | +>0,01% | +0,62 | +0,11 | +0,50 | +
| 82100 | +Natural lakes | +107399 | +12479,63 | +0,56% | +107576 | +12501,30 | +0,56% | +31,10 | +9,43 | +21,67 | +
| 82200 | +Reservoirs | +7200 | +168,08 | +0,01% | +7586 | +187,84 | +0,01% | +22,02 | +2,26 | +19,76 | +
| 82300 | +Aquaculture ponds | +681 | +126,33 | +0,01% | +696 | +127,31 | +0,01% | +1,33 | +0,35 | +0,98 | +
| 82400 | +Standing water bodies of extractive industrial sites | +1589 | +63,50 | +>0,01% | +1752 | +72,41 | +>0,01% | +13,26 | +4,35 | +8,94 | +
| 83100 | +Lagoons | +2959 | +5619,55 | +0,25% | +2966 | +5617,08 | +0,25% | +3,01 | +5,48 | +-2,48 | +
| 83200 | +Estuaries | +597 | +3367,10 | +0,15% | +601 | +3360,57 | +0,15% | +0,41 | +6,94 | +-6,53 | +
| 83300 | +Marine inlets and fjords | +701 | +28829,98 | +1,29% | +711 | +28806,84 | +1,29% | +0,15 | +23,28 | +-23,14 | +
| 84100 | +Open sea | +250 | +1267898,72 | +56,87% | +249 | +1267898,70 | +56,87% | ++ | 0,02 | +-0,02 | +
| 84200 | +Coastal waters | +803 | +205906,74 | +9,24% | +812 | +205893,70 | +9,24% | +28,46 | +41,50 | +-13,04 | +
| SUM | +4.425.760 | +2.229.478,03 | +100% | +4.482.781 | +2.229.478,03 | +100% | +11203,05 | +11203,05 | ++ | |
| Type | +Description | +Adopted solution | +
| Border errors between input data | +Errors resulting of different classes of the input data on the same locations (different mapping or different granularity of nomenclature) | +
+
|
+
| Thematic errors | +Errors of wrong classification of the input datasets | +
+ Errors are corrected if: +
|
+
| Geometric errors | +Geometric/topological errors which violate the CZ specifications (MMU, MMW, spikes, etc.): | +
+ Errors due the pre-processing of input data to transform them into input data for the skeleton are corrected
+
|
+
| + | Status 2012 | +Status 2018 | +||
| Overall Accuracy | +Confidence Interval | +Overall Accuracy | +Confidence Interval | +|
| Level 5 plausibility validation |
+ 97.90% | +0.0015 | +97.70% | +0.0016 | +
| Level 1 blind validation |
+ 98.27% | +0.0013 | +98.24% | +0.0013 | +
| + | Change 2012-2018 | +|
| + | Producer's Accuracy | +User's Accuracy | +
| Changes | +92.935% | +93.252% | +
| Non-Changes | +97.851% | +99.723% | +
| Class | +2012 | +2018 | +||
| Producer's Accuracy | +User's Accuracy | +Producer's Accuracy | +User's Accuracy | +|
| 1 | +97.39% | +98.66% | +96.69% | +98.60% | +
| 2 | +97.67% | +98.20% | +97.09% | +98.12% | +
| 3 | +98.68% | +98.18% | +98.08% | +98.65% | +
| 4 | +94.91% | +96.96% | +92.08% | +96.34% | +
| 5 | +91.20% | +96.76% | +88.17% | +95.96% | +
| 6 | +94.70% | +98.15% | +91.72% | +98.32% | +
| 7 | +97.08% | +98.41% | +87.75% | +98.37% | +
| 8 | +98.31% | +99.50% | +93.56% | +99.48% | +
| 2012 | +2018 | +||||
| Class | +Producer's Accuracy |
+ User's Accuracy |
+ Class | +Producer's Accuracy |
+ User's Accuracy |
+
| 11110 | 99.88% | 90.52% | +35000 | 99.13% | 94.24% | +
| 11120 | 91.65% | 98.06% | +36000 | 88.55% | 83.84% | +
| 11130 | 86.69% | 98.11% | +41000 | 97.28% | 98.14% | +
| 11210 | 96.52% | 97.97% | +42100 | 91.62% | 94.35% | +
| 11220 | 76.39% | 90.50% | +42200 | 94.50% | 26.34% | +
| 12100 | 98.51% | 98.38% | +51000 | 99.11% | 97.36% | +
| 12200 | 83.05% | 99.70% | +52000 | 97.61% | 86.68% | +
| 12310 | 95.37% | 92.24% | +53000 | 99.95% | 96.14% | +
| 12320 | 87.31% | 91.04% | +61100 | 98.83% | 86.55% | +
| 12330 | 94.45% | 90.59% | +61200 | 96.02% | 97.97% | +
| 12340 | 96.77% | 91.76% | +62111 | 97.44% | 98.48% | +
| 12350 | 94.81% | 87.86% | +62112 | 83.82% | 88.57% | +
| 12360 | 67.10% | 77.36% | +62120 | 51.18% | 98.70% | +
| 12370 | 97.45% | 85.24% | +62200 | 95.88% | 92.25% | +
| 12400 | 100.00% | 99.40% | +63110 | 99.75% | 98.08% | +
| 13110 | 91.48% | 96.65% | +63120 | 98.02% | 97.29% | +
| 13120 | 93.97% | 76.19% | +63200 | 91.21% | 93.87% | +
| 13130 | 92.59% | 95.20% | +63300 | 96.51% | 97.88% | +
| 13200 | 95.60% | 89.43% | +71100 | 92.93% | 95.03% | +
| 14000 | 70.95% | 96.48% | +71210 | 92.43% | 96.36% | +
| 21100 | 63.11% | 99.18% | +71220 | 98.77% | 97.92% | +
| 21200 | 93.99% | 94.89% | +72100 | 90.64% | 98.05% | +
| 22100 | 96.16% | 96.73% | +72200 | 98.09% | 99.54% | +
| 22200 | 95.06% | 96.42% | +72300 | 88.56% | 97.19% | +
| 23100 | 93.15% | 83.38% | +81100 | 96.86% | 98.45% | +
| 23200 | 92.86% | 84.03% | +81200 | 97.56% | 98.90% | +
| 23300 | 90.38% | 86.37% | +81300 | 100.00% | 98.40% | +
| 23400 | 83.30% | 89.88% | +82100 | 99.65% | 99.42% | +
| 31100 | 99.38% | 97.94% | +82200 | 68.75% | 89.53% | +
| 31200 | 95.80% | 99.04% | +82300 | 86.41% | 98.02% | +
| 32100 | 98.38% | 99.44% | +82400 | 92.29% | 90.28% | +
| 32200 | 94.81% | 71.68% | +83100 | 93.36% | 97.51% | +
| 33100 | 98.15% | 98.68% | +83200 | 97.61% | 98.13% | +
| 33200 | 92.90% | 38.10% | +83300 | 95.77% | 95.99% | +
| 34000 | 90.92% | 94.71% | +84100 | 100.00% | 100.00% | +
| + | 84200 | 96.88% | 99.65% | +||
| 2018 | +_User's Accuracy |
+ ||||
| Class | Accuracy | + | 96.21% | 90.57% | 90.34% | +
| 89.70% | 98.23% | 96.90% | +79.96% | 94.00% | 98.11% | +
| 95.74% | 97.72% | 94.67% | +97.99% | 91.49% | 93.69% | +
| 100.00% | 90.50% | 67.32% | +27.65% | 95.65% | 98.90% | +
| 89.76% | 97.20% | 100.00% | +99.71% | 100.00% | 81.55% | +
| 90.29% | 92.33% | 91.07% | +95.71% | 96.48% | 90.56% | +
| 98.22% | 87.38% | 81.71% | +91.12% | 76.68% | 97.83% | +
| 67.70% | 91.76% | 88.92% | +97.87% | 95.54% | 94.04% | +
| 78.55% | 88.48% | 94.77% | +77.42% | 89.13% | 98.75% | +
| 98.35% | 85.56% | 90.40% | +90.06% | 89.63% | 99.40% | +
| 93.45% | 97.87% | 92.82% | +93.68% | 69.65% | 97.26% | +
| 68.78% | 73.83% | 100.00% | +77.57% | 87.20% | 88.64% | +
| 89.32% | 97.73% | 90.41% | +88.04% | 79.74% | 94.98% | +
| 91.71% | 96.63% | 78.65% | +96.20% | 95.93% | 98.90% | +
| 86.73% | 97.91% | 94.38% | +97.48% | 91.36% | 98.20% | +
| 92.23% | 96.59% | 99.12% | +99.54% | 88.29% | 96.06% | +
| 79.61% | 97.20% | 93.58% | +83.72% | 96.74% | 98.24% | +
| 74.17% | 83.94% | 86.81% | +98.85% | 90.74% | 86.28% | +
| 91.27% | 98.35% | 97.37% | +89.80% | 82.27% | 99.15% | +
| 97.18% | 97.62% | 91.49% | +89.93% | 88.12% | 98.99% | +
| 97.61% | 98.10% | 97.63% | +98.84% | 87.55% | 90.47% | +
| 80.16% | 86.46% | 96.91% | +97.52% | 97.41% | 98.47% | +
| 85.44% | 98.19% | 90.84% | +38.64% | 98.89% | 95.98% | +
| 90.80% | 94.74% | 99.95% | +100.00% | 96.48% | 99.64% | +
| Country | +Status | +Country | +Status | +
| Albania | +not eligible for Copernicus funding |
+ Kosovo (under the UN Security Council Resolution 1244/99) | +not eligible for Copernicus funding |
+
| Austria | +✓ | +Latvia | +will not submit offer | +
| Belgium | +✓ | +Liechtenstein | +✓ covered by Austria | +
| Bosnia and Herzegovina | +not eligible for Copernicus funding |
+ Lithuania | +✓ | +
| Bulgaria | +✓ | +Luxemburg | +will not submit offer | +
| Croatia | +✓ | +Malta | +✓ | +
| Cyprus | +no reply yet | +Montenegro | +not eligible for Copernicus funding |
+
| Czech Republic | +✓ | +The Netherlands | +✓ | +
| Denmark | +will not submit offer | +Norway | +✓ | +
| Estonia | +✓ | +Poland | +✓ | +
| Finland | +✓ | +Portugal | +✓ | +
| Former Yugoslav Republic of Macedonia | +not eligible for Copernicus funding |
+ Romania | +✓ | +
| France | +✓ | +Serbia | +not eligible for Copernicus funding |
+
| Germany | +✓ | +Slovakia | +✓ | +
| Greece | +✓ | +Slovenia | +✓ | +
| Hungary | +✓ | +Spain | +✓ | +
| Iceland | +✓ | +Sweden | +✓ | +
| Ireland | +✓ | +Switzerland | +not eligible for Copernicus funding |
+
| Italy | +✓ | +Turkey | +not eligible for Copernicus funding; will provide national funding |
+
| + | + | United Kingdom | +✓ | +
| Sentinel-2 Multispectral Imager (MSI) | +|||
| Swath width (km) | +290 | +||
| Number of bands | +13 (altogether) | ++ | + |
| 4 | +in VIS | ++ | |
| 6 | +in NIR | ++ | |
| 3 | +in SWIR | ++ | |
| Ground sampling distance (m) |
+ 10 | +bands 2,3,4 (VIS) and band 8 (NIR) | +|
| 20 | +bands 5,6,7,8a (NIR) and bands 11,12 (SWIR) | +||
| 60 | +band 1 (VIS), band 9 (NIR) and band 10 (SWIR) | +||
| Bit depth (recording) | +12 | +||
| Repeat cycle at the Equator (days) |
+ 10 (with 1 satellite) 5 (with 2 satellites) |
+ ||
| Data access | +free, full and open access | +||
| Delivered resolution (m) |
+ 10 / 20 / 60 (depending on band) | +||
[μm]
Sentinel-2 MSI | Landsat-7 ETM | IRS (Resource-sat) LISS-III | SPOT-4
HRV | Remark | +|--------------|-------------------------------------------------------------|----------------------------------|----------------------------------|----------------------------------|-----------------------| +| **1** | 0.433-0.453 | | | | VIS band. Main use: atmospheric correction (aerosols) | +| **2** | 0.458-0.523 | 0.45-0.52
(TM1) | | | VIS: blue band | +| **3** | 0.543-0.578 | 0.53-0.61
(TM2) | 0.52-0.59
(MS1) | 0.50-0.59
(XI1) | VIS: green band | +| **4** | 0.650-0.681 | 0.63-0.69
(TM3) | 0.62-0.68
(MS2) | 0.61-0.68
(XI2) | VIS: red band | +| **5** | 0.698-0.713 | | | | NIR: vegetation red edge band | +| **6** | 0.733-0.748 | | | | NIR: vegetation red edge band | +| **7** | 0.773-0.793 | | | | NIR: vegetation red edge band | +| **8** | 0.735-0.950 | 0.75-0.90
(TM4) | 0.77-0.86
(MS3) | 0.78-0.89
(XI3) | NIR band | +| **8a** | 0.855-0.875 | | | | NIR: vegetation red edge band | +| **9** | 0.935-0.955 | | | | NIR band. Main use: atmospheric correction (water vapor) | +| **10** | 1.365-1.395 | | | | SWIR band. Main use: atmospheric correction (cirrus clouds) | +| **11** | 1.565-1.655 | 1.55-1.75
(TM5) | 1.55-1.70
(MS4) | 1.58-1.70
(XI4) | SWIR band | +| **12** | 2.100-2.280 | 2.09-2.35
(TM7) | | | SWIR band | +| Colour | Sentinel-2 | +| :--- | :--- | +| Red (R) | band 8 (NIR) | +| Green (G) | band 11 (SWIR) | +| Blue (B) | band 4 (red) | + +```{=typst} +#set page(flipped: false, paper: "a4") +#set text(size: 11pt) +``` +### Sentinel-2 data access and product types + +Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community; data access mechanisms have been tailored to address the different requirements of the various use typologies. Starting in 2014, the Sentinel missions become Copernicus Contributing Missions (CCMs), enlarging significantly the overall operational Earth Observation capability to support fulfilling the needs of the Copernicus Services [19]. +The Sentinel-2 User Products always refer to a given **Datatake**. Datatake definition refers to a continuous acquisition of an image from one Sentinel-2 satellite. The maximum length of an imaging Datatake is 15000 km (continuous observation from e.g. Northern Russia to Southern Africa). +Within a given Datatake, a portion of sensed image downlinked during a pass to a given receiving station is termed **Datastrip**. If a particular orbit is acquired by more than one receiving station, a Datatake is composed of one or more Datastrips. +Sentinel-2 User Products are provided as a compilation along a single orbit of elementary **Granules** of fixed size. In this respect, the product granularity corresponds to the minimum indivisible partition of one Sentinel-2 User Product. For Level-0, 1A and 1B products (Tables 11 and 12), these Granules are sub-images in MSI sensor reference frame of a given number of lines along-track and detector separated. +All Granules intersecting/touching the Region of Interest of the user are provided into the final User Product. For ortho-rectified products (Level-1C, Table 12), the Granules are called **Tiles**. A Tile consists of 100km x 100km sized ortho-images in cartographic reference frame UTM/WGS84 (Universal Transverse Mercator / World Geodetic System 1984) projection. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 11 Sentinel-2 products: Level 0 [17], [20] +::: + +| | | +|------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **Level-0** | Contains raw data after restoration of the chronological data sequence at full space/time resolution. Level-0 product contains all the information required to generate the Level-1 (and upper) products. | One Level-0 product refers always to one Datatake; it can cover the full Datatake or its extract. It may refer to one or several Datastrips from the same Datatake. | +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 12 Sentinel-2 products: Level 1 [17], [20] +::: + +```{=html} +
| Level-1A | +Corresponds to the systematic processing steps that must be applied before any further processing. It includes:
+
|
+
+ One Level-1A/B/C product:
+
|
+
| Level-1B | +Radiometrically corrected and geo-refined product obtained by performing corrections on the Level-1A data and refining its geometric model. + The radiometric corrections are applied but the geo-refinement model is only appended to the metadata and not applied to the product. Corrections include: +
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| Level-1C | +Geo-coded top-of atmosphere (TOA) reflectance with a sub-pixel multi-spectral and multi-date registration. + Ortho-image product, i.e. a map projection of the acquired image using a DEM to correct ground geometric distortions. + Note that the reflectance meaningful values go from “1” to “65535” as “0” is reserved for the NO_DATA. + A cloud, land and water mask is associated to the product. + L1C products are resampled with a constant GSD (Ground Sampling Distance) of 10m, 20m and 60m according to the native resolution of the different spectral bands. + |
+ |
| + | Visual product | +Full product | +
| Sentinel 2A/B |
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| Landsat 8 | +
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| Workflow step | +Activity | +Who is doing this? |
+ Timing | +
| 1 | +After closure of extended window, pre-selection and documentation of imagery by SP | +SP (service provider) | +Up to 4 weeks after closure of extended windows (with first deliveries starting 3rd October) | +
| 2 | +Approval or rejection of pre-selected S2 image tiles/LS8 scenes, possibly the selection of further S2 image tiles/LS8 scenes for the two coverages, and possibly selection of additional S2 image tiles. Based on shapefile, natural colour quicklooks and detailed instructions provided by SP | +CLC national teams | +Up to 2 weeks (total of 6 weeks after end of extended window, taking into account first delivery date) | +
| 3 | +Production of imagery and provision for FTP download | +SP | +Up to 4 weeks (total of up to 10 weeks after end of extended window, taking into account first delivery date) | +
| 4: Only in exceptional cases (in case country teams discover problems with images that were not visible in the quicklooks, but that require additional imagery) | +Propose further S2 image tiles/LS8 scenes for the two coverages and/or further additional S2 image tiles. | +CLC national teams | +Up to 2 weeks (total of 12 weeks after end of extended window) | +
| 5: Only in exceptional cases | +Only in accordance with the SP and EEA: Production of imagery and provision for FTP download | +SP | +Up to 4 weeks (total of up to 16 weeks after end of extended window) | +
A₂₀₁₂ ≥ 25 | Lch
Ach≥ 5 | L₂₀₁₈
A₂₀₁₈ ≥ 25 | Short explanation | Remark | +|--------------|------------------------------|---------------------------------------------------------------------|------------------------------|----------------------------|----------------------------| +| **A** | 1 | 1 | 1 | Simple change | Occurs the most frequently | +| **B** | 1 | 0 | 1 | Small change in existing polygon | Occurs frequently; **not interpreted** -> max. 5 ha error in CLC2018 | +| **C** | 1 | 1 | 0 | Disappearance of polygon | Seldom occurs | +| **D** | 1 | 0 | 0 | Disappearance of polygon with small change | Occurs very seldom, **not interpreted** -> max. 5 ha error in CLC2018 | +| **E** | 0 | 1 | 1 | Emerging of new polygon | **T** is used to avoid > 5 ha <25 ha error in CLC2018 | +| **F** | 0 | 0 | 1 | Emerging of new polygon with small change | **T** is used to avoid > 20 ha < 25 ha error in CLC2018 | +| **G** | 0 | 1 | 0 | Change only | Occurs frequently | +| **H** | 0 | 0 | 0 | Small change only | **Not interpreted** | + +```{=typst} +#set page(flipped: false, paper: "a4") +#set text(size: 11pt) +``` +Hereafter we give guidance on the way of handling each of the above types, illustrating them with examples. Of course, no universal recipe can be given for any of the cases. Thus, the following examples are schematic (they show a simplified reality) and do not list all possible combinations of codes and sizes. However, any change case falls under one of these theoretical types. The examples do not deal thoroughly with questions of generalisation, as these are well described in the CLC nomenclature document [5]. For figure legend see Ch. 4.3.1. + +#### A. Simple change: a polygon > 25 ha in CLC2012 grows or decreases with a change > 5 ha resulting a polygon > 25 ha in CLC2018 + +Being the most frequently occurring change type, changes > 5 ha connected to an existing (> 25 ha) CLC2012 polygon are always mapped (Figs. 9 & 10). + +IMAGE / CLC2012 IMAGE2018 CLC-CHANGE CLC2018 + + +Figure 9 Simple change (growth): A settlement (112) > 25 ha grows with > 5 ha, occupying arable land (211) + + +Figure 10 Simple change (shrinkage): A fruit orchard (222) > 25 ha decreases with > 5 ha, while area of arable land (211) is increasing. The resulting 222 polygon is still > 25 ha in 2018. + +Following their delineation, change polygons must be given a code₂₀₁₂ and a code₂₀₁₈ representing the processes having occurred to the given patch in reality (see explanation at „real change” at Ch. 4.3.1). + +#### B. Small change in existing polygon: < 5 ha change in polygon > 25 ha + +No change polygons < 5 ha should be mapped except if they are elementary changes of a complex change > 5 ha (Ch. 4.3.1. and Fig. 8). + +Remark: 10% exaggeration in size is allowed (i.e. 4.5 ha new industry is better to enlarge to 5 ha in order to keep it in CLC-Change). + +#### C. Disappearing polygon: a polygon decreases to <25 ha with a change > 5 ha + +If due to a change > 5 ha the size of a polygon decreases under 25 ha, it will disappear in CLC2018 because of generalisation, while the change polygon remains in CLC-Change. Only the part that has really changed must be delineated during change mapping (Figs. 11 and 12). + + +Figure 11 Disappearing polygon, case-1: Most of the area of a park (141) is built up so that the park’s size actually decreases under 25 ha. Consequently, what is left of it is generalized into the settlement (112) in CLC2018. + + +Figure 12 Disappearing polygon, case-2: Significant (> 5 ha, but < 25 ha) part of a vineyard (221) is occupied by new industry (121). A change polygon coded 221-121 is delineated in CLC-Change database. The area left from the vineyard is < 25 ha. Consequently, in CLC2018 the remaining vineyard and the new industry is generalized into arable land (211) and urban fabric (112), respectively. + +#### D. Polygon disappearing with small change: a polygon decreases to < 25 ha with a change < 5 ha + +In a few cases, existing polygons decrease to a size < 25 ha with a change < 5 ha. As change is < 5 ha, the changed patch should not be delineated. This causes a minor (< 5 ha) mistake in CLC2018. + +Remark: 10% exaggeration in size is allowed (i.e. 4.5 ha new residential area is better to enlarge to 5 ha in order to keep it in CLC-Change). + +#### E. New polygon: a polygons grows > 25 ha with a change > 5 ha + +The simplest case of this type is the emerging of a new patch > 25 ha (Fig. 13). + + +Figure 13 New polygon: A > 25 ha new fishpond (512) is established on former pasture (231). + +If a patch that existed in 2012, but used to be < 25 ha (thus not mapped in CLC2012) grows with a change > 5-ha so that it exceeds the 25-ha limit in 2018, a so-called „technical change" polygon must also be applied. Besides delineating the real change (grown part of the polygon), the non-changed (originally existing) part must be delineated as well, with identical code₂₀₁₂ and code₂₀₁₈ and an additional attribute marking it as technical change. Using up the two types of change polygons, the patch will be included in CLC2018 automatically, whereas the technical change polygon will be deleted later from the final CLC-Change database (Fig. 13). (For more information on technical changes see its definition at Ch. 4.3.1). + + +Figure 14 New polygon with Technical change, case-1: A 20 ha forest clearcut (324) grows with 8 ha. As a result, the clearcut's area exceeds 25 ha. Two change polygons must be delineated: an 8-ha real change (311-324) and a 20 ha technical change (324-324). The technical change will be deleted from final version of CLC-Change, while the corresponding change polygons will make up a 324 polygon in CLC2018. + +In order to avoid inaccuracies being introduced into CLC2018, the same method is applied also in cases when the real change is > 25 ha so that it would make up a new polygon itself in CLC2018. This case too, a real change polygon must be drawn over the changed ("new") part and a technical change polygon must be drawn above the non-changed ("already existing") part if > 5 ha (Fig. 15). + + +Figure 15 New polygon with Technical change, case-2: A 7 ha fishpond (512) grows with 30 ha. Although the change is > 25 ha, so the polygon would be enough to form new polygon in CLC2018, in order not to miss the 7-ha part, a technical change polygon (512-512) must also be delineated. This will be deleted from final version of CLC-Change, while CLC2018 will contain a correct 37 ha water body (512) polygon. + +A special case of this type (combined with type C) is the code change of a polygon (Fig. 16). + + +Figure 16 Code change: A new industrial unit (121) is built on a > 25 ha pasture (231), totally occupying its area. With a change 231-121 the pasture disappears, while a new industry emerges. + +#### F. New polygon with small change: a polygon grows > 25 ha with a change < 5 ha + +In the few cases when polygon grows over 25 ha with a real change < 5 ha, the real change should be added to the technical change polygon as well. Without using technical change, we would introduce a major (between 20 and 25 ha) mistake into CLC2018 Fig. 17). Using technical change the newly appearing polygon will be included in CLC2018, while the change database will not contain any polygon here (no real change > 5 ha). + +Remark: 10% exaggeration in size is allowed (i.e. 4.5 ha new sport and recreation area is better to enlarge to 5 ha to keep it in CLC-Change; it leads to case E). + + +Figure 17 New polygon with small change: A 22 ha sport facility (142) grows with 3 ha, thus just reaching the 25 ha MMU. As changed part is < 5 ha, no real change polygon must be delineated. The polygon should however appear in CLC2018, so a 25-ha technical change (142-142) polygon must be drawn. + +#### G. Only change: changes in a non-existing polygon + +This type includes cases when the change polygon is not connected to a valid polygon neither in CLC2012 nor in CLC2018, while valid (> 5 ha) change occurred. This type of change also must be coded according to their real change process (Figs. 18 and 19). + +Figure 18 Changes in non-existing polygons, case-1: A new small industrial unit (121) > 5 ha is built on former arable land (211), while a small patch of fruit orchard + +(222) > 5 ha disappears because of being turned into arable land (211). Both patches must be delineated as changes (211-121 and 222-211), as being > 5 ha. No new polygons emerge in CLC2018, as corresponding change polygons are generalised. + + +Figure 19 Changes in non-existing polygons, case-2: A 13 ha sport facility (142) expands with 6 ha, while in the neighbourhood 9 ha of a 15-ha dumpsite (132) is recultivated by being turned into grassland (231). Both changed areas are >5 ha thus resulting a valid polygon in CLC-Change database. However, none of them result a > 25 ha polygon in 2018. + +#### H. Small change (< 5 ha) in not existing polygon (< 25 ha) + +As polygons in all three databases are smaller than their respective area limits, this case should not be dealt with. + +### Treating changes in by-definition heterogeneous classes – changes on landscape level + +CLC nomenclature includes some land cover classes that by definition represent heterogeneous landscapes, thus certain polygons are made up a mosaic of smaller homogenous patches, most of them < 25 ha. This means a shift from a dominantly feature-level mapping generally applied by CLC to a landscape-level approach for classes especially: 242, 243 and 313.⁹ If individual land cover changes occur within polygons of these classes in a way that they altogether change the characteristics of the area on a landscape level, then change polygons should be delineated on a landscape level, too. Let us take for example a 243 polygon, being mostly agricultural landscape with mosaic of small (< 25 ha) patches of semi natural features: forest, bushes, wetlands and / or natural grassland. If a few of the bushes are cut and turned into arable land, the main character of the polygon does not change, it is still an agricultural landscape with significant amount of natural features. This case changes must be mapped individually as 324-211, thus they will represent real changes in the CLC-Change database, whereas in CLC2018 the 243 polygon will be left unchanged as new 211 patches will be generalized into 243 (Fig. 20). + +Figure 20 Changes in heterogeneous class 243 (no landscape level change): In a + +heterogeneous landscape (243) a few patches of semi-natural vegetation (324) are turned into arable land (211). As still significant area of natural vegetation is left, the character of the polygon does not change, it is still best characterised with code 243. Change polygons delineated must represent the real process (324-211). Due to generalisation, the 243 polygon will be left unchanged in CLC2018. + +It might happen however, that due to an economic / social impact (say change in EU subsidisation system) or for some natural phenomena all or most of the natural patches are turned into arable land, turning the whole landscape's character into agricultural. The area is not a mosaic of natural patches and agricultural land any more, but mostly arable land. This case the change happened on the landscape level, so the change polygon will include the whole area, its code pair being 243-211. It is only in these cases that the delineation of individual changes can be replaced by landscape-level change mapping (Figs. 21 and 22). + +*** +⁹ Heterogeneous classes are not to be confused with general rule of CLC mapping i.e. all classes might have some portion with different land cover. E.g. Discontinuous urban fabric (112) might include < 25 ha parks, water bodies, industry etc. + +*** + + +Figure 21 Changes in heterogeneous class 243 (landscape level change): In a heterogeneous landscape (243) most of patches of semi-natural vegetation (324, 321) are turned into arable land (211). As the area of natural vegetation left is not significant, the character of the whole area has changed. A 243-211 change polygon must be delineated. + + +Figure 22 Changes in heterogeneous class 243 (landscape level change on part of the area only): In a part of a heterogeneous landscape (243) most of patches of natural vegetation (324, 321) are turned into arable land (211). The delineated change polygon (243-211) must cover only the changed part of the landscape. + +Processes showing to the opposite direction (from homogeneous to heterogeneous landscape) should be treated similarly (Figs. 23 and 24). + + +Figure 23 A homogeneous landscape turned into heterogeneous landscape: In an area dominantly occupied by orchards (222), a significant part of the plantations is cut and turned into arable land and pasture. The landscape becomes heterogeneous agricultural landscape (242); orchards do not dominate it any more. + +IMAGE / CLC2012 IMAGE 2018 CLC CHANGE CLC2018 + +Figure 24 A homogeneous landscape partially turned into heterogeneous landscape: If fruit tree plantations (222) are kept in a part of the same area, only the altered part should be delineated as change (222-242). + +Similar approach should be applied for all three by-definition heterogeneous classes: i.e. 313, 242, 243. + +## CLC2018 Support Package (InterChange software) + +The CLC2012 Support Package, developed by ETC-SIA has been successfully used by more than 20 national teams in implementing CLC2012. It is a set of standalone applications developed with Embarcadero Delphi XE2 and TatukGIS Developer Kernel. Thus, user does not need to purchase, install and tune any other software to carry out CLC change mapping [24]. The CLC2012 Support Package, being a specialized, task oriented software tool, significantly facilitated updating, change detection and mapping, quality control and correction of CLC databases by means of computer-assisted visual photointerpretation [4]. + +The tool has been upgraded for the CLC2018 project, called now CLC2018 Support Package [31]. Main novelties of the tool are as follows: + +- supports the use of Sentinel-2 imagery, +- versions for both 32-bit and 64-bit operation systems, +- enhanced error checking function, +- new, more efficient editing tool ("re-shape") in the revision window, +- various convenience functions. + +Like its predecessors, the CLC2018 Support Package consists of three modules: + +1. **InterChange** for interpreting land cover changes: Provides a tool for the revision of CLC2012 land cover database and supports the interpretation of land cover changes in order to create the CLC-Change₂₀₁₂₋₂₀₁₈ database. The program provides a convenient and easy-to-use interface for editing polygons in CLC2012 and CLC-Change₂₀₁₂₋₂₀₁₈ databases, for viewing and modification of polygon data and for finding and correction (revision) of errors generated during interpretation and editing. +2. **InterCheck** for checking CLC databases: Serves the checking of revised CLC2012 (or CLC2018) and CLC-Change₂₀₁₂₋₂₀₁₈ data. InterCheck program has been prepared primarily for supporting the CLC Technical Team, although national central teams might apply it as a tool for checking of the completed CLC2018 and CLC-Change₂₀₁₂₋₂₀₁₈ databases. Many file formats are supported, not only those that has been prepared with InterChange. +3. **LUCAS Photo Viewer**: Program displays the main data and field photos of the LUCAS2012 and LUCAS2015¹⁰) sampling point selected in InterChange or InterCheck program. + +### User registration + +CLC2018 Support Package is available for all participating national teams **free of charge**. The submission of a **registration form** is the sole requirement of using the software. This (besides keeping record of users) enables developer to contact users in case an update of the software package is released. The registration form and the Support Package can be downloaded from: https://forum.eionet.europa.eu/nrc_land_covers/library/copernicus-2014-2020/pan-european-component/corine-land-cover-clc-2018/clc2018-support-package + +Like before, detailed help, and printable user's guide (in English) as well as user support are inherent parts of the package. ETC-ULS provides helpdesk service, similarly to the CLC2012 exercise. Helpdesk contact: lehoczki.robert@bfkh.gov.hu + +## Alternative solutions for CLC2018 + +During the implementation of the CLC2012 project some countries applied procedures different from visual photo-interpretation for deriving CORINE Land Cover data. These solutions aimed to reduce human work-load and combined national GIS datasets, satellite image processing (IP) technology, on-screen digitization (visual photo-interpretation) and GIS-based generalisation. Most of these methods aimed to produce CLC status layer, but they were also successful in facilitating / partly solving the change mapping job. + +Applying these GIS/IP based alternative change mapping solutions are encouraged, if results are compatible (in terms of technical features and accuracy) with the standard method. + +In case of change mapping major issues of non-compliance were as follows (as experienced during CLC2006 and CLC2012 verifications): + +- Changes mapped are often not "real" changes, i.e. they do not represent a change that occurred in reality; +- Changes are topologically incorrect (e.g. overlap with other changes) or geometrically incorrect (e.g. narrow or sliver polygons); +- Change outlines do not match boundaries of the CLC parent layer (i.e. CLC2006 in CLC2012 project). + +These can be avoided by: + +- First of all: not taking uncritically the changes derived from ancillary databases, but considering them as potential changes, which are to be visually checked and approved by interpreters or used as background information for interpreters in manual delineation of changes. This is especially true for changes of built-up areas, heterogeneous agricultural classes and non-forested natural classes. Forestry changes are easier to be automatically detected; they however also require at least partial visual control (especially forest growth); + +*** +¹⁰ Because of the deadline of the submission of national CLC2018 results Q3 2018, LUCAS2018 data will be available too late to be applied in CLC2018. + +*** + +- Making sure that source databases are timely, i.e. not outdated, but especially not fore dated (databases of buildings or spatial planning do often contain features that are planned to be raised, but in reality, are still not existing). + +Taking CLC2012 database as a geometrical basis of change mapping is a necessary requirement. In the exceptional case when CLC2018 is produced first, and CLC-Change₂₀₁₂₋₂₀₁₈ are derived by backdating, geometrical and thematic compliance with new status layer (CLC2018) is still necessary. + +# Ancillary data + +In-situ data in Copernicus programme by definition comprise all non-space-born data with a geographic dimension. Major use of in situ data in CLC project is to complement the satellite data in the course of production and to verify or validate results provided from space-born data. + +The photointerpreter should be aware that primary source of information is IMAGE2012 and IMAGE2018, which are considered reference data concerning both date and thematic content. Recommended in-situ data include: + +- Up-to date topographic maps (preferably at scale 1:25.000 / 1:50.000) to be used during interpretation, mapping and validation process; +- Orthophotos, taken optimally in 2012 and in 2017 (especially if topographic maps are out of date). Orthophotos are to be used in those cases only, which cannot be understood by interpreting satellite images. +- Thematic maps (built-up, vegetation, forestry, hydrology, snow and ice, etc.); +- Other ancillary data (e.g. LPIS, which has an utmost importance in precise mapping agriculture classes and their changes) for identification/interpretation and verification of land-cover mapping; +- LUCAS 2012 field survey data coordinated by Eurostat, including landscape photographs from visited points, covering EU27 [25]. LUCAS data can be displayed and analysed to support change mapping under CLC2018 [3]. +- Results of HRLs produced by using IMAGE2012 and IMAGE2018 data (especially degree of imperviousness and tree cover density / forest type) encouraged to be used as supporting information to derive CLC2018. +- Ancillary data complementing the above, and being useful in CLC change mapping are Google Earth (GE) imagery (or equivalent): provides VHR image data, supporting the interpretation where no ortho-photos are available. Often multi-year time series are provided, which are very useful in understanding the evolution of the area. Major uses are: mapping fast-growing changes (e.g. constructions, mining, clear-cu, etc); identification of plantations (both fruit and forest), agroforestry (dehesa /montado) and scattered holiday cottages and their changes; use of crowd-sourced field photos (Panoramio) attached to these images. Interpreter however should always a) be aware of GE image dates; b) treat GE data with a due precaution as non-valid image dates might occur; and c) Panoramio field photos are often misplaced. + +# Production of CLC2018 database + +In the CLC2018 project the CORINE Land Cover database is updated by the „change mapping first” approach (Ch. 4.2). During change mapping, discovered errors (thematic as well as geometric) of CLC2012 have to be corrected (Ch. 4.2.2.1), providing a revised CLC2012 dataset (or a layer of technical changes if revision is not to be done according to national project plan). + +CLC2018 database will be produced by adding CLC2012rev and CLC-Change₂₀₁₂₋₂₀₁₈ in a GIS, like happened in CLC2012 project. + +The two major prerequisites of producing CLC2018 out of CLC2012rev and CLC-Change₂₀₁₂₋₂₀₁₈ data by GIS is that + +1. CLC-Change₂₀₁₂₋₂₀₁₈ outlines are geometrically based on CLC2012rev outlines. +2. Both CLC2012rev and CLC-Change₂₀₁₂₋₂₀₁₈ are topologically correct databases (no holes, no overlaps, no multi-part polygons, no dissolve errors). + +If any of these conditions are not fulfilled, GIS operation will produce false result and slivers, therefore will give an incorrect CLC2018. + +Integrating CLC2012rev and CLC-Change₂₀₁₂₋₂₀₁₈ in order to produce CLC2018 should rely on the equation: + +$$CLC2018 = CLC2012_{rev} (+) CLC-Change_{2012-2018}$$ + +Where (+) means the following operation: CLC2012rev (revised CLC2012) and CLC-Change₂₀₁₂₋₂₀₁₈ databases are intersected, then CLC-Change polygons' code₂₀₁₂ is replaced by code₂₀₁₈, and finally neighbours with similar code are unified (Fig. 25). Small (<25 ha) polygons are generalized according to a priority table (Fig. 26). As an option, polygons slightly below the 25-ha limit (e.g. 23,5 ha) can be manually enlarged by a photo-interpreter (Fig. 27). The CLC Technical Team provides an ArcGIS toolbox written for the "intelligent" data integration, like in CLC2006 and CLC2012 projects [25]. + + +Figure 25 Increase of a settlement / decrease of arable land by 30 ha. As the change is >25 ha, the integration of CLC2012rev and CLC-Change₂₀₁₂₋₂₀₁₈ is straightforward and can be done automatically. The exact mathematical relation between the three databases (CLC2012rev, CLC-Change₂₀₁₂₋₂₀₁₈, CLC2018) is fulfilled. + + +Figure 26 10 ha of new forest plantation on former arable land. As the change is <25 ha, the integration of CLC2012 and CLC-Change₂₀₁₂₋₂₀₁₈ is not straightforward and generalisation is needed. Using the priority table, this case can be solved automatically. The forest plantation area will be added to the area of the forest polygon. The exact mathematical relation between the three databases (CLC2012, CLC-Change₂₀₁₂₋₂₀₁₈, CLC2018) is not fulfilled. + + +Figure 27 New industry (23.5 ha) on arable land. With an automatic process, this would have not appeared in CLC2018. However, with a slight exaggeration to reach the 25-ha limit, this object will be part of CLC2018. The exact mathematical relation between the three databases (CLC2012, CLC-Change₂₀₁₂₋₂₀₁₈, CLC2018) is not fulfilled. The generalization toolbox makes possible the setting up of a threshold above which polygons are manually generalized, allowing the above described manual or automated exaggeration. + +# Metadata + +Like in the CLC2012 project, two levels of metadata are produced in CLC2018 project. + +## Working unit-level metadata + +The purpose of the working-unit level documentation is to make note on all steps of production of the CLC-change database. National teams are responsible for preparing working unit-level documentation for their CLC databases, for internal use within CLC projects. The templates for CLC2012 and an example of a filled-in form can be found in Eionet Forum https://forum.eionet.europa.eu/nrc_land_covers/library/copernicus-2014-2020/pan-european-component/corine-land-cover-clc-2018/technical-guidelines/metadata/working-unit-level-documentation + +## Country-level metadata + +Country-level metadata mostly serve the users by informing them about the main parameters of the product. Country-level metadata are to be produced by the national teams for CLC-Change₂₀₁₂₋₂₀₁₈, CLC2018 and if applicable, for the revised CLC2012 databases. + +Since country level metadata for CLC products include information common to all countries, a template XML file specific for CLC has been prepared¹¹ and can be found in Eionet Forum at: https://forum.eionet.europa.eu/nrc_land_covers/library/copernicus-2014-2020/pan-european-component/corine-land-cover-clc-2018/technical-guidelines/metadata/country-level-metadata + +Country-level metadata can be edited with the INSPIRE metadata editor and saved in a new XML file. Since the direct editing of an XML file is rather complicated, for metadata editing we recommend using the INSPIRE metadata editor available at http://inspire-geoportal.ec.europa.eu/editor/ General user guide can be found at [28]. + +Be aware that, currently, only INSPIRE core metadata elements can be entered and edited with this tool (not possible to add theme specific metadata). For this reason, other XML editors can also be used. + +An example of a pdf file with metadata for country level CLC can be found in on Eionet Forum. It can be considered as an example of what can be written in the country specific XML file. Titles like abstract or resource constraints should be maintained. As for lineage, we give an example used for CLC Luxembourg. + +*** +¹¹Country-level metadata for CLC2012 products is INSPIRE compatible and applies EEA's requirements which follow the “Guidelines for creators of metadata related to spatial datasets" found at: http://taskman.eionet.europa.eu/projects/sdi/wiki/Cataloguemetadata_guidelines + +*** + +# Training and verification + +Training and verification for CLC2018 are implemented by the CLC Technical Team under the guidance of the EEA, similarly to previous CLCs. + +## Training + +Since the CLC2006 project training of a National Team was organised only on the request of the national team. As the methodology of CLC2018 is the same as that of CLC2006 and CLC2012, training will be held only in exceptional cases on the request of the country in the questionnaire circulated in late 2016. The list of CLC2018 training courses foreseen (based on CLC2018 country survey) are presented in Table 17. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 17 Planned CLC2018 training courses (status 12/10/17) +::: + +| Country | Reason of training | Remark | +|-------------------------------------------|-----------------------------------------|--------------------------------------------------------------------------------------------------------------------| +| Albania | new team | A longer than usual training is foreseen to improve basic skills in photointerpretation | +| Cyprus | possibly new team | to be confirmed | +| Estonia | new team | The responsible national Environment Agency will provide photointerpreters | +| France | new team | Training for subcontractor needed | +| Greece | partly new team | new photointerpreters, but experienced QC team | +| Kosovo | possibly new team | to be confirmed | +| Lithuania | new team | | +| Portugal | new team, new method | new team of photo interpreters; partly new technology | +| Slovenia | new team, new method | Consultation on semi-automated use of national data | +| Switzerland | new team | Photointerpreters are selected on national tender or the direct assignment of the work. Should be checked if the working team is really new and the training course is needed. | +## Verification + +Like in previous CLC projects, the CLC Technical Team will usually verify the revised parent status layer and the new CLC-Change layer (CLC2012rev and CLC-Change₂₀₁₂₋₂₀₁₈ databases). The reason to verify CLC2012rev is that according to the standard methodology CLC2018 is based on CLC2012, so producing a good quality CLC2018 requires as good as possible quality CLC2012. + +In case of few countries where the CLC2018 database is directly produced, CLC2018 and CLC-Change₂₀₁₂₋₂₀₁₈ databases will be verified. + +The aim of the verification is two-fold: + +- to inform the EEA and the National Authority of the Member state about the work progress; +- to assist the country in producing a high-quality CLC update, which is harmonised in Europe. + +Like in CLC2012, in CLC2018 two verification actions per country are planned Tables 18, 19): + +1st verification (usually remote verification, i.e. not visiting the country, data sent to technical Team) is due when the first few working units are interpreted (e.g. 10-30% of the country). The main purpose of this action is to reveal problems in the early phase of implementation. Countries new in CLC, or having less-experienced CLC team might be visited. In countries working with regional teams (Italy and Spain) all regions need to send separately a sample for first verification. The sample size is standardised, but sample location is determined by national team. + +2nd verification (in majority of cases remote verification) is due when around 75% of the country area is interpreted. The main purpose of this action is to check the database close to completion and suggest improvements if needed. The sample size is standardised, and sample location(s) are determined by CLC Technical Team. Like in 1st verification regions in Italy and Spain are checked separately. However, countries / regions having area below 20.000 km² will not requested to send a new sample for 2nd verification. In case of poor result, re-checking of the area of 1st verification might be requested. + +It is the purpose to check altogether usually 10% of the country area (taking into consideration the 1st verification as well). The proposed standard size of the verification working unit (VWU) is about 50 km x 50 km area within a S2 image frame. The number of samples to be checked in 2nd verification depends on the size of the country/region. (Table 18). + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 18 General scheme of verification in CLC2018 +::: + +```{=html} +
| + | 1st verification | +2nd verification | +Data to provide; data format | +Remark | +
| sample selection by | +national team | +CLC2018 Technical Team | +revised CLC2012 (or CLC2018¹²) and CLC-Change₂₀₁₂₋₂₀₁₈ in shapefile format |
+ samples should be completed, i.e. full area interpreted | +
| sample size to verify | +a single area, about 50 km x 50 km in size within a Sentinel-2 tile (or Landsat 8 image) | +about 50 km x 50 km areas within Sentinel-2 tiles (or Landsat 8 image). Number of areas depends on size of the country / region (see Table 19). | +||
| IMAGE2012 to provide | +area(s) covering the sample interpretation(s) | +IRS / SPOT / RapidEye data covering the sample area; in GeoTiff, LAN format | +all used IMAGE2012 data have to be sent | +|
| IMAGE2018 | +ID (file name) of S2 (or Landsat-8) images used in deriving CLC data should be provided for each sample | ++ | images to be sent by NT only if not the centrally provided IMAGE2018 is used | +|
| Orthophotos and topographic maps | ++ | recommended if available; WMS access welcome | +||
VWUs, 1st
verification | remark, 2nd
verification | number of
VWUs, 2nd
verification | +|-----------------------------------------|--------------|------------------|-----------------------------------------|-------------------------------------------|-------------------------------------------| +| Liechtenstein | 160 | with Austria | 0 | | 0 | +| Malta | 316 | | 1 | recheck if needed | 0 | +| Luxembourg | 2 586 | | 1 | recheck if needed | 0 | +| Cyprus | 9 251 | | 1 | recheck if needed | 0 | +| Kosovo | 10 908 | | 1 | recheck if needed | 0 | +| Montenegro | 13 812 | | 1 | recheck if needed | 0 | +| Slovenia | 20 273 | | 1 | | 1 | +| Macedonia | 25 713 | | 1 | | 1 | +| Albania | 28 748 | | 1 | | 1 | +| Belgium | 30 510 | | 1 | | 1 | +| Switzerland | 41 290 | | 1 | | 1 | +| Netherlands | 41 526 | | 1 | | 1 | +| Denmark | 43 094 | | 1 | | 1 | +| Estonia | 45 226 | | 1 | | 1 | +| Slovakia | 48 845 | | 1 | | 1 | +| Bosnia and
Herzegovina | 51 129 | | 1 | | 1 | +| Croatia | 56 542 | | 1 | | 2 | +| Latvia | 64 589 | | 1 | | 2 | +| Lithuania | 65 200 | | 1 | | 2 | +| Ireland | 70 280 | | 1 | | 2 | +| Serbia | 77 453 | | 1 | | 2 | +| Czech Republic | 78 866 | | 1 | | 2 | +| Austria | 83 858 | | 1 | | 3 | +| Portugal | 91 568 | | 1 | | 3 | +| Hungary | 93 030 | | 1 | | 3 | +| Iceland | 103 000 | | 1 | | 3 | +| Bulgaria | 110 910 | | 1 | | 4 | +| Greece | 131 940 | | 1 | | 5 | +| Romania | 238 392 | | 1 | | 9 | +| United Kingdom| 244 820 | | 1 | | 9 | +| Italy | 301 318 | regions | 20 | partly only recheck | 16 | +| Poland | 312 685 | | 1 | | 12 | +| Norway | 323 802 | | 1 | | 12 | +| Finland | 338 145 | | 1 | | 13 | +| Germany | 357 050 | | 1 | | 13 | +| Sweden | 449 964 | | 1 | | 17 | +| Spain | 505 992 | regions | 19 | partly only recheck | 22 | +| France | 551 695 | | 1 | | 21 | +| Turkey | 783 562 | | 1 | | 30 | +| **Total:** | **5 848 048** | | **75** | | **217** | + +```{=typst} +#set page(flipped: false, paper: "a4") +#set text(size: 11pt) +``` +The results of the verification will be expressed in qualitative terms as before, i.e. no quantitative accuracy assessment will be provided: + +**A** (accepted) means: only minor problems were found; + +**CA** (conditionally accepted) means: there are more problems but relatively easy to correct; following corrections the working unit is accepted; + +**R** (rejected) means: there are many mistakes in the database (incorrect application of the nomenclature, omitted changes, false changes, etc.), which takes considerable work to correct. Each verification will be accompanied with a verification report and GIS file including the remarks. + +Table 19 includes the verification plan for CLC2018. + +All participating countries (except Liechtenstein, which is verified together with Austria)) and all regions in Italy and Spain are expected to send one verification working unit (VWU)¹³ (about 50 km x 50 km in size) for 1st verification. The VWU should be selected from inside the respective S2 tile (or Landsat-8 image) in order to minimize the number of IMAGE2018images used in verification. + +*** +¹³ The size of the verification working unit (50 km x 50 km) and its location is not necessarily coinciding with the size and location of production working unit + +*** + +# Final quality control and delivery + +## Delivery procedure + +Delivery of national CLC2018 products from National Team (NT) to the EEA is part of the CLC2018 planning in countries and follows the agreed CLC2018 projects schedule. Any foreseen alteration of the delivery schedule shall be indicated to the CLC Technical Team in advance, so these can be accommodated appropriately in the project plan. + +National data can be considered as 'ready for delivery' after the following steps are fulfilled: + +1. Last verification mission of CLC Technical Team took place and Verification Mission Report has been issued; +2. Recommendations specified in the Verification Reports have been integrated into the data by the NT; +3. Technical quality of deliverables has been checked internally by NT and screened using online tool (see Ch. 9.1.1) to conform to all specifications as defined in the CLC2018 Technical Guidelines. + +The following deliveries [delivery file name] are expected from the countries (xx means the two character-long ISO code of the country): + +- **CLC-Changes (2012-2018)** - [`CHA18_xx`] +- **CLC2018** - [`CLC18_xx`] +- **CLC2012 revised** – [`CLC12_xx`] +- **Metadata** as specified in Chapter 7 + +In addition, ESRI Geodatabase format introduced during CLC2006 is now considered as primary delivery format. ESRI Geodatabase, an object-oriented geographic database that provides framework for managing geographic data and their topological relations is considered as proprietary, but robust standard (both file and personal geodatabase is supported). For other file formats please consult with CLC Technical Team (contact: tomas.soukup@gisat.cz). + +### Online quality screening + +When deliveries are ready to be uploaded into CDR folders dedicated for national CLC2018 deliveries, the NT performs final quality check using the CLC QC Tool - online technical quality screening service. Conformity to the CLC specifications as defined in these Technical Guidelines can be checked there and results are provided visually as well as reported via dedicated reports and errors correction supporting GIS files in automatic manner. The tool supports national teams in their DIY compliance checking in order to assure conformity of the final deliveries prior to upload to EEA CDR in a standard, transparent and more effective way. This shall streamline final data acceptance and provision of the DBTA Report - the Database Technical Acceptance Report. + +CLC QC Tool integrates all formal, technical and topological checks as defined in these Technical Guidelines, which can be done in fully automatic way. This standard set of checks and their centralized implementation shall assure that all checks are done in a standard and transparent way and also that no checks are skipped or omitted. Based on the experience from delivery and acceptance task in the previous CLC updates, the aim of the tool is to support: + +- streamlining of the delivery process; +- assurance of technical consistency and semantic correctness of each individual national CLC database produced by participating countries; +- smooth and fast integration of data into the seamless European CLC database + +CLC technical quality screening service is available as web service via web address http://clcqc.gisat.cz (Fig. 28). All functionalities are run purely through web client and therefore doesn't impose any change to national team technical procedures, internal workflow or software environment setup. Eligible national team users will be notified with access information. For detailed information about the CLC QC Tool functionalities please consult documentation ([27]) and the online CLCQC Tool Help (http://clcqc.gisat.cz/help/AboutCLCQCTool.html). For any additional questions related to CLC2018 technical quality screening service please contact tomas.soukup@gisat.cz. + + +Figure 28 CLC QC Tool home page - http://clcqc.gisat.cz + +### DBTA Report + +When online checked deliveries are uploaded into the CDR, the central technical team proceed with final acceptance. As mentioned, the DBTA report contains summary of data and metadata conformity checks as specified in the CLC2018 Technical Guidelines, and is therefore used either as + +**A** final data acceptance confirmation – to confirm that all delivery parts have been accepted by ETC/ULS without problems, listing all exceptions if present + +or + +**B** – a part of 'the request for improvement' from the ETC/ULS documenting inconsistencies found and guiding the NT in data improvement. In this case, the DBTA report draft is accompanied with supporting information and GIS files for such guidance and sent to National Team. Nevertheless, number of these iterations shall be minimized if not avoided at all by introducing the online technical quality screening service. Nevertheless, in case of some non-conformity still found in data, the NT will be notified with request to proceed with improvement of datasets, new online checking and new submission into the EEA Central Data Repository (CDR). + +### Final delivery + +The final data delivery process is very simple. All deliveries shall be uploaded into the EEA Central Data Repository (CDR). In order to deliver data, you have to log in with your EIONET account and password in the relevant folder for your country in the Reportnet Central Data Repository (see list below). You should then carry out the following steps: + +- Create the delivery envelope +- Activate the task +- Upload your files from your system to CDR +- Verify that the delivery is complete +- Release the envelope (Files which should not be available to the public can be locked) +- Finish + +All final accepted deliveries (the DBTA report is issued) shall be uploaded into the EEA Central Data Repository (CDR) subfolder named "final" together with the DBTA report. National CLC2018 coordinators should liaise with National Focal Points on the delivery process to the EEA. If you need any assistance during the delivery process, do not hesitate to contact EIONET helpdesk at helpdesk@eionet.europa.eu. + +The CDR folders for national CLC2018 deliveries: +AL http://cdr.eionet.europa.eu/al/eea/clc +AT http://cdr.eionet.europa.eu/at/eea/clc +BA http://cdr.eionet.europa.eu/ba/eea/clc +BE http://cdr.eionet.europa.eu/be/eea/clc +BG http://cdr.eionet.europa.eu/bg/eea/clc +CH http://cdr.eionet.europa.eu/ch/eea/clc +CY http://cdr.eionet.europa.eu/cy/eea/clc +CZ http://cdr.eionet.europa.eu/cz/eea/clc +DE http://cdr.eionet.europa.eu/de/eea/clc +DK http://cdr.eionet.europa.eu/dk/eea/clc +EE http://cdr.eionet.europa.eu/ee/eea/clc +ES http://cdr.eionet.europa.eu/es/eea/clc +FI http://cdr.eionet.europa.eu/fi/eea/clc +FR http://cdr.eionet.europa.eu/fr/eea/clc +GR http://cdr.eionet.europa.eu/gr/eea/clc +HR http://cdr.eionet.europa.eu/hr/eea/clc +HU http://cdr.eionet.europa.eu/hu/eea/clc +IE http://cdr.eionet.europa.eu/ie/eea/clc +IS http://cdr.eionet.europa.eu/is/eea/clc +IT http://cdr.eionet.europa.eu/it/eea/clc +LI http://cdr.eionet.europa.eu/li/eea/clc +LT http://cdr.eionet.europa.eu/lt/eea/clc +LU http://cdr.eionet.europa.eu/lu/eea/clc +LV http://cdr.eionet.europa.eu/lv/eea/clc +ME http://cdr.eionet.europa.eu/me/eea/clc +MK http://cdr.eionet.europa.eu/mk/eea/clc +MT http://cdr.eionet.europa.eu/mt/eea/clc +NL http://cdr.eionet.europa.eu/nl/eea/clc +PL http://cdr.eionet.europa.eu/pl/eea/clc +PT http://cdr.eionet.europa.eu/pt/eea/clc +NO http://cdr.eionet.europa.eu/no/eea/clc +RO http://cdr.eionet.europa.eu/ro/eea/clc +RS http://cdr.eionet.europa.eu/rs/eea/clc +SE http://cdr.eionet.europa.eu/se/eea/clc +SI http://cdr.eionet.europa.eu/si/eea/clc +SK http://cdr.eionet.europa.eu/sk/eea/clc +TR http://cdr.eionet.europa.eu/tr/eea/clc +UK http://cdr.eionet.europa.eu/gb/eea/clc +XK http://cdr.eionet.europa.eu/xk/eea/clc + +## Comparison to 2012 delivery procedure + +As seen above the delivery workflow for CLC2018 update remains the same as in the case of CLC2012, where technical quality screening service have been already introduced. The workflow assures technical consistency of each national CLC delivery from countries in shorter time and enable subsequent smooth and fast integration of data into the seamless European CLC2018 products. + +# References + +[1] Heymann Y, C Steenmans, G Croissille., M Bossard, 1994. CORINE Land Cover. Technical Guide. EUR12585 Luxembourg, Office for Official Publications of the EC. + +[2] Büttner G, J Feranec, G Jaffrain, 2002: CORINE Land Cover Update 2000, Technical Guidelines, EEA Technical Report No. 89. + +[3] Büttner G, B Kosztra, 2007. CLC2006 Technical Guidelines. EEA, Technical Report 17/2007. + +[4] Büttner Gy, B.Kosztra, 2014. Addendum to CLC2006 Technical Guidelines, Version 2, ΕΕΑ. https://forum.eionet.europa.eu/nrc_land_covers/library/gio-land/corine-land-cover-clc/technical-guidelines/clc2012-addendum-clc2006-technical-guidelines + +[5] Enhanced CLC Nomenclature Guidelines +https://forum.eionet.europa.eu/nrc_land_covers/library/copernicus-2014-2020/pan-european-component/corine-land-cover-clc-2018/technical-guidelines/clc2018-nomenclature-guidelines and online version http://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/ + +[6] EEA Task Force, 1992: CORINE Land Cover. A European Community project. + +[7] Büttner G, J Feranec, G Jaffrain, L Mari L, G Maucha, T Soukup, 2004. The CORINE Land Cover 2000 Project. http://www.eproceedings.org/static/vol03_3/03_3_buttner2.pdf + +[8] Steenmans C, G Büttner, 2006, Mapping land cover of Europe for 2006 under GMES. Proceedings of the 2nd workshop of the EARSEL SIG on land use and land cover. Bonn, Germany, 28-30 September 2006 pp. 202-207. + +[9] European Commission, 2005, Global Monitoring for Environment and Security (GMES: From Concept to Reality), COM 2005 565 final. + +[10] Maucha G, G Taracsák, G Büttner, 2004. Methodological questions of CORINE Land Cover change mapping. Proceedings of the 2nd International Workshop on the Analysis of Multi-Temporal Remote Sensing images, MultiTemp-2003 Workshop, pp. 302-313. World Scientific Publishing Co. + +[11] REGULATION (EU) No 911/2010 of the European Parliament and of the Council of 22 September 2010 on the European Earth monitoring programme (GMES) and its initial operations (2011 to 2013) http://www.google.com/search?q=EU+Regulation+%28EU%29+n%C2%B0911%2F201 + +[12] GMES Space Component Data Access Portfolio, Data Warehouse 2011-2014. http://gmesdata.esa.int/web/gsc/dap_document + +[13] Büttner, G., Maucha, G.: The thematic accuracy of Corine Land Cover 2000. Assessment using LUCAS (land use / cover area frame statistical survey). EEA Technical Report No 7/2006. ISSN 1725-2237. http://reports.eea.europa.eu/technical_report_2006_7/en + +[14] Copernicus Open Access Hub. https://scihub.copernicus.eu/ + +[15] Büttner, Gy: CORINE Land Cover 2012. Presentation at: New Horizons for European and Global land monitoring – Copernicus products and services ready to use. 19-20/10/2015 EEA, Copenhagen + +[16] http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-2 + +[17] https://sentinel.esa.int/documents/247904/685211/Sentinel-2-Product-Specifications-Document + +[18] http://www.esa.int/Our_Activities/Observing_the_Earth/Copernicus/Sentinel-2/Instrument +[19] https://sentinel.esa.int/web/sentinel/sentinel-data-access + +[20] Drush M et al., 2012. Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment 120(2012) pp. 25-36. + +[21] Taracsák, G. 2003: InterChange1.1 Software for interpreters of land cover changes. User's Manual + +[22] Maucha G, Gy. Büttner and R. Pataki, 2011. Development of methodology to eliminate contradictions between CLC-Change1990-2000 and CLC-Change2000-2006, ETC-SIA Report + +[23] Büttner Gy, B. Kosztra, 2017, Manual of CORINE Land Cover Changes, Version 2. ETC-ULS. https://forum.eionet.europa.eu/nrc_land_covers/library/gio-land/corine-land-cover-clc/technical-guidelines/manual-corine-land-cover-changes + +[24] Taracsák G. 2012-2014. CLC2012 Support Package http://clc2012.taracsak.hu + +[25] Eurostat, LUCAS. http://ec.europa.eu/eurostat/statistics-explained/index.php/LUCAS_Land_use_and_land_cover_survey + +[26] Pataki R. 2013. ArcGIS macro program for generating CLC2012 https://forum.eionet.europa.eu/nrc_land_covers/library/gio-land/corine-land-cover-clc/technical-guidelines/macro-program-generating-clc2006 + +[27] GISAT 2014. The CLC QC Quick Guide http://clcqc.gisat.cz + +[28] General user guide for INSPIRE metadata editor: http://www.eurogeoss.eu/Documents/EuroGEOSS_D_2_2_3.pdf + +[29] https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi/resolutions/spatial + +[30] https://en.wikipedia.org/wiki/Landsat_8 + +[31] Taracsák G, 2017. CLC2018 Support Package +https://forum.eionet.europa.eu/nrc_land_covers/library/copernicus-2014-2020/pan-european-component/corine-land-cover-clc-2018/clc2018-support-package/ + +[32] Step-by-step guidance for IMAGE2018 data selection by CLC national teams. Framework Service Contract EEA/MDI/14/010 LOT 2 https://forum.eionet.europa.eu/nrc_land_covers/library/copernicus-2014-2020/pan-european-component/corine-land-cover-clc-2018/technical-guidelines/image2018-guidelines/ + +# List of abbreviations + +| | | +|-------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------| +| **ATCOR** | Atmospheric and Topographic Correction | +| **BOA** | Bottom of Atmosphere (reflectance) | +| **CAPI** | Computer Assisted Photointerpretation | +| **CLC** | CORINE Land Cover | +| **CLC90, CLC1990** | The first CLC inventory for reference year 1990 | +| **CLC2000** | CORINE Land Cover update for reference year 2000 | +| **CLC2006** | CORINE Land Cover update for reference year 2006 | +| **CLC2012** | CORINE Land Cover update for reference year 2012 | +| **CLC2018** | CORINE Land Cover update for reference year 2018 | +| **CLC TT** | CLC Technical Team | +| **CCM** | Copernicus Contributing Mission | +| **CORINE** | Co-ordination of information on the Environment | +| **CSC** | Copernicus Space Component | +| **CSCDA** | Copernicus Space Component Data Access system | +| **DEM** | Digital Elevation Model | +| **DG AGRI** | Directorate General for Agriculture | +| **DG ENV** | Directorate General for Environment | +| **EC** | European Commission | +| **EEA** | European Environment Agency | +| **EEA39** | EEA39 composed of : 33 member countries and cooperating countries | +| **Eionet** | European Environment Information and Observation Network | +| **ESA** | European Space Agency | +| **ESTAT** | EUROSTAT, statistical office of the EU | +| **ETC ULS** | European Topic Centre on Urban, Land and Soil Systems | +| **ETM** | Enhanced Thematic Mapper (US Landsat-7 sensor) | +| **EO** | Earth Observation | +| **EU** | European Union | +| **FTSP** | Fast Track Service Precursor (a term used in 2006 for CLC2006 and Imperviousness mapping) | +| **GCP** | Ground Control Points | +| **GIS** | Geographic Information System | +| **GMES** | Global Monitoring for Environment and Security (earlier name of Copernicus programme) | +| **GRI** | Global Reference Image | +| **GDSD** | ground sampling distance | +| **IMAGE1990** | Satellite image coverage for reference year 1990 | +| **IMAGE2000** | Satellite image coverage for Europe, reference year 2000 | +| **IMAGE2006** | Satellite image coverage for Europe, reference year 2006 | +| **IMAGE2012** | Satellite image coverage for Europe, reference year 2012 | +| **IMAGE2012** | Satellite image coverage for Europe, reference year 2018 | +| **IMAGE&CLC2000** | IMAGE2000 & CORINE Land Cover 2000 project | +| **INSPIRE** | Infrastructure for Spatial Information in Europe | +| **IP** | image processing | +| **IRS** | Indian Remote Sensing (satellites) | +| **JRC** | Joint Research Centre | +| **LC** | Land Cover | +| **LISS** | Linear Self Scanning (sensor on-board of IRS satellites) | +| **LPIS** | Land Parcel Identification System | +| **LUCAS** | Land Use/Cover area framework statistical survey (Eurostat) | +| **LULC** | Land Use & Land Cover | +| **MMU** | minimum mapping unit | +| **MSS** | Multispectral scanner (US Landsat 1-3 sensor) | +| **MS** | Member States (of EEA) | +| **MSI** | Multispectral Imager (Sentinel-2 sensor) | + +| | | +|---------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------| +| **NIR** | Near Infrared (sensor band) | +| **NRC** | National Reference Centre | +| **Phare** | Poland and Hungary: Assistance for Restructuring their Economies (EU pre-accession aid in the 90s') | +| **SAFE** | Standard Archive Format for Europe (Sentinels) | +| **SOER** | State of Environment Europe Report (EEA publication in every 5 yr) | +| **SPOT** | Système Probatoire d'Observation de la Terre (series of French EO satellites) | +| **S2** | Sentinel-2 (satellites) | +| **QA/QC** | Quality assurance/Quality control | +| **SWIR** | Short Wave Infrared (sensor band) | +| **TG** | Technical Guidelines | +| **TM** | Thematic Mapper (US Landsat-4 and Landsat-5 sensor) | +| **TOA** | Top of Atmosphere (reflectance) | +| **UTM/WGS84** | Universal Transverse Mercator projection; World Geodetic System 1984 | +| **VIS** | visible and infrared (sensor bands) | +| **VWU** | Verification Working Unit | +| **WP** | Work Package | +| **WU** | Working Unit | \ No newline at end of file diff --git a/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/2018_Technical_Guidelines_v1.source.pdf b/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/2018_Technical_Guidelines_v1.source.pdf new file mode 100644 index 00000000..561cc2b4 Binary files /dev/null and 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between change outlines (right side, magenta) and CLC status layer outlines (yellow)", + "fig_id": "FIG_5", + "md5": "22bf6b35fea1b9877dc6add6adf9582c", + "file": "img-22bf6b35fea1b9877dc6add6adf9582c.png", + "origin": "detector" + }, + { + "page": 29, + "bbox": [ + 81.05000305175781, + 67.99998474121094, + 542.7999877929688, + 271.04998779296875 + ], + "rtype": "figure", + "confidence": 0.98, + "caption": "Figure 6 Mistake: Change outlines (right side, magenta) not matching CLC status layer outlines (yellow)", + "fig_id": "FIG_6", + "md5": "d1ab3a86f9f60dbadafe5f4011772ab4", + "file": "img-d1ab3a86f9f60dbadafe5f4011772ab4.png", + "origin": "detector" + }, + { + "page": 31, + "bbox": [ + 81.05000305175781, + 71.59996795654297, + 570.52001953125, + 231.0999755859375 + ], + "rtype": "figure", + "confidence": 0.98, + "caption": "Figure 7 Principle of interpreting real change: the loss of urban green (141) < 25 ha by becoming a construction site (133) must be coded 141-133 in the CLC-Change database, although the patch is generalised into discontinuous urban fabric (112) in both CLC2012 and CLC2018.", + "fig_id": "FIG_7", + "md5": "af6a6e916eeaf774d590142b7c71a344", + "file": "img-af6a6e916eeaf774d590142b7c71a344.png", + "origin": "detector" + }, + { + "page": 32, + "bbox": [ + 76.76000213623047, + 160.6400146484375, + 549.760009765625, + 316.25 + ], + "rtype": "figure", + "confidence": 0.95, + "caption": "Figure 8 Complex change and elementary changes: Settlement (112) has taken 1 ha area from pasture (231) and 4 ha from arable land (211). These two elementary changes make up a complex change of 5 ha.", + "fig_id": "FIG_8", + "md5": "7575af570d9b639b931b536058324cb2", + "file": "img-7575af570d9b639b931b536058324cb2.png", + "origin": "detector" + }, + { + "page": 34, + "bbox": [ + 80.4800033569336, + 600.7999877929688, + 548.9500122070312, + 737.3599853515625 + ], + "rtype": "figure", + "confidence": 0.95, + "caption": "Figure 9 Simple change (growth): A settlement (112) > 25 ha grows with > 5 ha, occupying arable land (211)", + "fig_id": "FIG_9", + "md5": "d0e917bdb6b341c5bb7d33af826ecc82", + "file": "img-d0e917bdb6b341c5bb7d33af826ecc82.png", + "origin": "detector" + }, + { + "page": 35, + "bbox": [ + 75.9000015258789, + 74.5999755859375, + 547.25, + 230.29998779296875 + ], + "rtype": "figure", + "confidence": 0.99, + "caption": "Figure 10 Simple change (shrinkage): A fruit orchard (222) > 25 ha decreases with > 5 ha, while area of arable land (211) is increasing. The resulting 222 polygon is still > 25 ha in 2018.", + "fig_id": "FIG_10", + "md5": "255c2f9273d97961a02e1c64321a1928", + "file": "img-255c2f9273d97961a02e1c64321a1928.png", + "origin": "detector" + }, + { + "page": 35, + "bbox": [ + 84.30000305175781, + 521.2399291992188, + 547.25, + 673.8499755859375 + ], + "rtype": "figure", + "confidence": 0.99, + "caption": "Figure 11 Disappearing polygon, case-1: Most of the area of a park (141) is built up so that the park's size actually decreases under 25 ha. 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As a result, the clearcut's area exceeds 25 ha. Two change polygons must be delineated: an 8-ha real change (311-324) and a 20 ha technical change (324-324). The technical change will be deleted from final version of CLC-Change, while the corresponding change polygons will make up a 324 polygon in CLC2018.", + "fig_id": "FIG_14", + "md5": "131f35aabb6f062700d920780cbc90c3", + "file": "img-131f35aabb6f062700d920780cbc90c3.png", + "origin": "detector" + }, + { + "page": 37, + "bbox": [ + 84.68000030517578, + 419.3599853515625, + 547.25, + 570.0399780273438 + ], + "rtype": "figure", + "confidence": 0.95, + "caption": "Figure 15 New polygon with Technical change, case-2: A 7 ha fishpond (512) grows with 30 ha. Although the change is > 25 ha, so the polygon would be enough to form new polygon in CLC2018, in order not to miss the 7-ha part, a technical change polygon (512-512) must also be delineated. This will be deleted from final version of CLC-Change, while CLC2018 will contain a correct 37 ha water body (512) polygon.", + "fig_id": "FIG_15", + "md5": "8d2d0906615b659312c24f35bf49daff", + "file": "img-8d2d0906615b659312c24f35bf49daff.png", + "origin": "detector" + }, + { + "page": 38, + "bbox": [ + 83.0, + 74.23999786376953, + 545.5999755859375, + 227.79998779296875 + ], + "rtype": "figure", + "confidence": 0.95, + "caption": "Figure 16 Code change: A new industrial unit (121) is built on a > 25 ha pasture (231), totally occupying its area. 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The polygon should however appear in CLC2018, so a 25-ha technical change (142-142) polygon must be drawn.", + "fig_id": "FIG_17", + "md5": "86b97f5e8b7d0b036eec001b27288127", + "file": "img-86b97f5e8b7d0b036eec001b27288127.png", + "origin": "detector" + }, + { + "page": 39, + "bbox": [ + 78.69999694824219, + 125.36000061035156, + 550.5, + 272.0999755859375 + ], + "rtype": "figure", + "confidence": 0.98, + "caption": "Figure 18 Changes in non-existing polygons, case-1: A new small industrial unit (121) > 5 ha is built on former arable land (211), while a small patch of fruit orchard", + "fig_id": "FIG_18", + "md5": "8b91c4ebc1793917f0351f2c5a30fa37", + "file": "img-8b91c4ebc1793917f0351f2c5a30fa37.png", + "origin": "detector" + }, + { + "page": 39, + "bbox": [ + 84.30000305175781, + 349.1600036621094, + 554.5999755859375, + 505.1999816894531 + ], + "rtype": "figure", + "confidence": 0.98, + "caption": "Figure 19 Changes in non-existing polygons, case-2: A 13 ha sport facility (142) expands with 6 ha, while in the neighbourhood 9 ha of a 15-ha dumpsite (132) is recultivated by being turned into grassland (231). Both changed areas are >5 ha thus resulting a valid polygon in CLC-Change database. 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The exact mathematical relation between the three databases (CLC2012rev, CLC-Change2012-2018, CLC2018) is fulfilled.", + "fig_id": "FIG_25", + "md5": "6014fb4f8acc12bb10298284a8da201d", + "file": "img-6014fb4f8acc12bb10298284a8da201d.png", + "origin": "detector" + }, + { + "page": 47, + "bbox": [ + 84.5, + 67.99993896484375, + 545.0499877929688, + 223.8399658203125 + ], + "rtype": "figure", + "confidence": 0.95, + "caption": "Figure 26 10 ha of new forest plantation on former arable land.", + "fig_id": "FIG_26", + "md5": "a0917797da03fe98f2e014107a2032e6", + "file": "img-a0917797da03fe98f2e014107a2032e6.png", + "origin": "detector" + }, + { + "page": 47, + "bbox": [ + 80.5, + 323.54998779296875, + 542.0, + 480.04998779296875 + ], + "rtype": "figure", + "confidence": 0.95, + "caption": "Figure 27 New industry (23.5 ha) on arable land.", + "fig_id": "FIG_27", + "md5": "ee4f72adde91bc9b48263df7f9b15f06", + "file": "img-ee4f72adde91bc9b48263df7f9b15f06.png", + "origin": "detector" + }, + { + "page": 54, + "bbox": [ + 81.05000305175781, + 218.67999267578125, + 539.4500122070312, + 472.8299865722656 + ], + "rtype": "figure", + "confidence": 0.98, + "caption": "Figure 28 CLC QC Tool home page - http://clcqc.gisat.cz", + "fig_id": "FIG_28", + "md5": "4227270587d5e3c608dcd570ff75d872", + "file": "img-4227270587d5e3c608dcd570ff75d872.png", + "origin": "detector" + } + ], + "other_detections": [], + "cover": { + "is_cover": true, + "fields": { + "title": "CLC2018 Technical Guidelines", + "subtitle": "Service Contract No 3436/R0-Copernicus/EEA.56665", + "date": "2017-10-25", + "version": "Final" + } + } +} \ No newline at end of file diff --git a/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/phase1.json b/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/phase1.json new file mode 100644 index 00000000..f1ff56f8 --- /dev/null +++ b/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/phase1.json @@ -0,0 +1,48 @@ +{ + "chrome_images_removed": 0, + "chrome_pages_affected": 60, + "pages_total": 61, + "pages_candidate": 24, + "pages_skipped": 36, + "candidate_pages": [ + 8, + 13, + 14, + 22, + 23, + 24, + 25, + 26, + 28, + 29, + 31, + 32, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 46, + 47, + 54 + ], + "cover": { + "is_cover": true, + "fields": { + "title": "CLC2018 Technical Guidelines", + "subtitle": "Service Contract No 3436/R0-Copernicus/EEA.56665", + "date": "2017-10-25", + "version": "Final" + } + }, + "cost_usd": { + "cover": 0.0008054, + "detect": 0.554326875 + }, + "figures": 28, + "tables": 0 +} \ No newline at end of file diff --git a/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/result.json b/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/result.json new file mode 100644 index 00000000..620489ba --- /dev/null +++ b/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/result.json @@ -0,0 +1,73 @@ +{ + "pdf": "Files_to_convert/processed_documents/inbox/2018_Technical_Guidelines_v1.pdf", + "stem": "2018_Technical_Guidelines_v1", + "out_dir": "Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1", + "status": "warn", + "error": "", + "resumed": false, + "est": { + "expected_usd": 1.12123, + "low_usd": 0.821986, + "high_usd": 2.318207, + "pages": 61, + "candidate_pages": 25, + "text_chars": 131855, + "breakdown": { + "cover": 0.005, + "detect": 0.517742, + "convert": 0.598489 + }, + "calibrated": true + }, + "est_usd": 1.12123, + "figures": 28, + "tables": 22, + "verify_status": "warn", + "verify_issues": [ + { + "name": "structural_counts", + "status": "warn", + "summary": "figures 27/28; 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tables 22 in .qmd (~27 source region(s), rough \u2014 see table_coverage)", + "metric": null, + "findings": [ + { + "message": "28 figures detected but 27 image refs in .qmd", + "severity": "warn", + "location": "figures" + } + ] + }, + { + "name": "figure_placement", + "status": "warn", + "summary": "27/28 detected figures placed, 1 unreferenced", + "metric": 96.4, + "findings": [ + { + "message": "detected figure FIG_3 not placed in the .qmd", + "severity": "warn", + "location": "" + } + ] + }, + { + "name": "text_coverage", + "status": "warn", + "summary": "text coverage 98.5% (1549/1573 sentences; 24 missing)", + "metric": 98.5, + "findings": [ + { + "message": "missing: Chapter 4 provides guidelines for mapping CLC\u2011Changes", + "severity": "warn", + "location": "" + }, + { + "message": "missing: changes (named CLC-Change1990-2000) by using CLC1990, IMAGE1990 and", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 1.2.3 CLC2006 under GMES", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 1.2.4 CLC2012 under Copernicus", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 1.5.1 Participating countries", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 1.5.2 Technical documents", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 3.1.2 Access to IMAGE2012 satellite imagery in ESA\u2019s DWH", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 3.3.2 Image selection workflow and timing", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 4.2.2 Particular requirements concerning CLC2018 mapping", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 4.2.2.2 CLC change interpretation", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 4.2.2.2.1 Geometry", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 4.2.2.2.2 Coding", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 4.2.2.2.4 Nomenclature", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 4.3.3 Change typology \u2013 guidelines for interpretation", + "severity": "warn", + "location": "" + }, + { + "message": "missing: As a result, the clearcut\u2019s area exceeds 25 ha.", + "severity": "warn", + "location": "" + }, + { + "message": "missing: (or CLC2018) and CLC-Change2012-2018 data.", + "severity": "warn", + "location": "" + }, + { + "message": "missing: Both CLC2012rev and CLC-Change2012-2018 are topologically correct databases (no", + "severity": "warn", + "location": "" + }, + { + "message": "missing: (CLC2012rev, CLC-Change2012-2018, CLC2018) is fulfilled.", + "severity": "warn", + "location": "" + }, + { + "message": "missing: the integration of CLC2012 and CLC-Change2012-2018 is not straightforward and", + "severity": "warn", + "location": "" + }, + { + "message": "missing: three databases (CLC2012, CLC-Change2012-2018, CLC2018) is not fulfilled.", + "severity": "warn", + "location": "" + }, + { + "message": "missing: CLC-Change2012-2018 databases will be verified.", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 9.1.1 Online quality screening", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 9.1.2 DBTA Report", + "severity": "warn", + "location": "" + }, + { + "message": "missing: 9.1.3 Final delivery", + "severity": "warn", + "location": "" + } + ] + }, + { + "name": "table_coverage", + "status": "ok", + "summary": "27 source table(s); weighted word coverage 97.3% (simple avg 94.1%); 1 substantial table(s) below 70%", + "metric": 97.3, + "findings": [ + { + "message": "table 18: 33% of words matched; e.g. missing ['the clc2012 polygon including the change is taken over into the clc change datab']", + "severity": "info", + "location": "table 18" + } + ] + }, + { + "name": "oversized_tables", + "status": "ok", + "summary": "no oversized source tables", + "metric": null, + "findings": [] + }, + { + "name": "wide_table_legibility", + "status": "ok", + "summary": "no tables shrunk below 8pt", + "metric": null, + "findings": [] + } + ] +} \ No newline at end of file diff --git a/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/verify_report.md b/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/verify_report.md new file mode 100644 index 00000000..b4fe9254 --- /dev/null +++ b/Files_to_convert/processed_documents/output/2018_Technical_Guidelines_v1/verify_report.md @@ -0,0 +1,68 @@ +# Verify report + +**Overall: warn** + +## ✅ frontmatter — ok + +all required fields present + +## ⚠️ structural_counts — warn + +figures 27/28; tables 22 in .qmd (~27 source region(s), rough — see table_coverage) + +- **warn** (figures): 28 figures detected but 27 image refs in .qmd + +## ⚠️ figure_placement — warn + +27/28 detected figures placed, 1 unreferenced + +_metric: 96.4_ + +- **warn**: detected figure FIG_3 not placed in the .qmd + +## ⚠️ text_coverage — warn + +text coverage 98.5% (1549/1573 sentences; 24 missing) + +_metric: 98.5_ + +- **warn**: missing: Chapter 4 provides guidelines for mapping CLC‑Changes +- **warn**: missing: changes (named CLC-Change1990-2000) by using CLC1990, IMAGE1990 and +- **warn**: missing: 1.2.3 CLC2006 under GMES +- **warn**: missing: 1.2.4 CLC2012 under Copernicus +- **warn**: missing: 1.5.1 Participating countries +- **warn**: missing: 1.5.2 Technical documents +- **warn**: missing: 3.1.2 Access to IMAGE2012 satellite imagery in ESA’s DWH +- **warn**: missing: 3.3.2 Image selection workflow and timing +- **warn**: missing: 4.2.2 Particular requirements concerning CLC2018 mapping +- **warn**: missing: 4.2.2.2 CLC change interpretation +- **warn**: missing: 4.2.2.2.1 Geometry +- **warn**: missing: 4.2.2.2.2 Coding +- **warn**: missing: 4.2.2.2.4 Nomenclature +- **warn**: missing: 4.3.3 Change typology – guidelines for interpretation +- **warn**: missing: As a result, the clearcut’s area exceeds 25 ha. +- **warn**: missing: (or CLC2018) and CLC-Change2012-2018 data. +- **warn**: missing: Both CLC2012rev and CLC-Change2012-2018 are topologically correct databases (no +- **warn**: missing: (CLC2012rev, CLC-Change2012-2018, CLC2018) is fulfilled. +- **warn**: missing: the integration of CLC2012 and CLC-Change2012-2018 is not straightforward and +- **warn**: missing: three databases (CLC2012, CLC-Change2012-2018, CLC2018) is not fulfilled. +- **warn**: missing: CLC-Change2012-2018 databases will be verified. +- **warn**: missing: 9.1.1 Online quality screening +- **warn**: missing: 9.1.2 DBTA Report +- **warn**: missing: 9.1.3 Final delivery + +## ✅ table_coverage — ok + +27 source table(s); weighted word coverage 97.3% (simple avg 94.1%); 1 substantial table(s) below 70% + +_metric: 97.3_ + +- **info** (table 18): table 18: 33% of words matched; e.g. missing ['the clc2012 polygon including the change is taken over into the clc change datab'] + +## ✅ oversized_tables — ok + +no oversized source tables + +## ✅ wide_table_legibility — ok + +no tables shrunk below 8pt diff --git a/Files_to_convert/processed_documents/output/2018_Validation_Report_v1/2018_Validation_Report_v1.source.pdf b/Files_to_convert/processed_documents/output/2018_Validation_Report_v1/2018_Validation_Report_v1.source.pdf new file mode 100644 index 00000000..3d3c655c Binary files /dev/null and b/Files_to_convert/processed_documents/output/2018_Validation_Report_v1/2018_Validation_Report_v1.source.pdf differ diff --git a/Files_to_convert/processed_documents/output/2018_Validation_Report_v1/2018_Validation_Report_v1.working.pdf b/Files_to_convert/processed_documents/output/2018_Validation_Report_v1/2018_Validation_Report_v1.working.pdf new file mode 100644 index 00000000..70343986 Binary files /dev/null and b/Files_to_convert/processed_documents/output/2018_Validation_Report_v1/2018_Validation_Report_v1.working.pdf differ diff --git a/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/LICENSE b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/LICENSE new file mode 100644 index 00000000..fdb0416f --- /dev/null +++ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 duskmoon314 (Campbell He) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/README.md b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/README.md new file mode 100644 index 00000000..cd258ce8 --- /dev/null +++ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/README.md @@ -0,0 +1,162 @@ +# typst-fontawesome + +A Typst library for Font Awesome icons through the desktop fonts. + +p.s. The library is based on the Font Awesome 6 desktop fonts (v6.6.0) + +## Usage + +### Install the fonts + +You can download the fonts from the official website: https://fontawesome.com/download + +After downloading the zip file, you can install the fonts depending on your OS. + +#### Typst web app + +You can simply upload the `otf` files to the web app and use them with this package. + +#### Mac + +You can double click the `otf` files to install them. + +#### Windows + +You can right-click the `otf` files and select `Install`. + +#### Some notes + +This library is tested with the otf files of the Font Awesome Free set. TrueType fonts may not work as expected. (Though I am not sure whether Font Awesome provides TrueType fonts, some issue is reported with TrueType fonts.) + +### Import the library + +#### Using the typst packages + +You can install the library using the typst packages: + +`#import "@preview/fontawesome:0.5.0": *` + +#### Manually install + +Copy all files start with `lib` to your project and import the library: + +`#import "lib.typ": *` + +There are three files: + +- `lib.typ`: The main entrypoint of the library. +- `lib-impl.typ`: The implementation of `fa-icon`. +- `lib-gen.typ`: The generated icon map and functions. + +I recommend renaming these files to avoid conflicts with other libraries. + +### Use the icons + +You can use the `fa-icon` function to create an icon with its name: + +`#fa-icon("chess-queen")` + +Or you can use the `fa-` prefix to create an icon with its name: + +`#fa-chess-queen()` (This is equivalent to `#fa-icon().with("chess-queen")`) + +You can also set `solid` to `true` to use the solid version of the icon: + +`#fa-icon("chess-queen", solid: true)` + +Some icons only have the solid version in the Free set, so you need to set `solid` to `true` to use them if you are using the Free set. +Otherwise, you may not get the expected glyph. + +#### Full list of icons + +You can find all icons on the [official website](https://fontawesome.com/search) + +#### Different sets + +By default, the library supports `Free`, `Brands`, `Pro`, `Duotone` and `Sharp` sets. +(See [Enable Pro sets](#enable-pro-sets) for enabling Pro sets.) + +But only `Free` and `Brands` are tested by me. +That is, three font files are used to test: + +- Font Awesome 6 Free (Also named as _Font Awesome 6 Free Regular_) +- Font Awesome 6 Free Solid +- Font Awesome 6 Brands + +Due to some limitations of typst 0.12.0, the regular and solid versions are treated as different fonts. +In this library, `solid` is used to switch between the regular and solid versions. + +To use other sets or specify one set, you can pass the `font` parameter to the inner `text` function: \ +`fa-icon("github", font: "Font Awesome 6 Pro Solid")` + +If you have Font Awesome Pro, please help me test the library with the Pro set. +Any feedback is appreciated. + +##### Enable Pro sets + +Typst 0.12.0 raise a warning when the font is not found. +To use the Pro set, `#fa-use-pro()` should be called before any `fa-*` functions. + +```typst +#fa-use-pro() // Enable Pro sets + +#fa-icon("chess-queen-piece") // Use icons from Pro sets +``` + +#### Customization + +The `fa-icon` function passes args to `text`, so you can customize the icon by passing parameters to it: + +`#fa-icon("chess-queen", fill: blue)` + +#### Stacking icons + +The `fa-stack` function can be used to create stacked icons: + +`#fa-stack(fa-icon-args: (solid: true), "square", ("chess-queen", (fill: white, size: 5.5pt)))` + +Declaration is `fa-stack(box-args: (:), grid-args: (:), fa-icon-args: (:), ..icons)` + +- The order of the icons is from the bottom to the top. +- `fa-icon-args` is used to set the default args for all icons. +- You can also control the internal `box` and `grid` by passing the `box-args` and `grid-args` to the `fa-stack` function. +- Currently, four types of icons are supported. The first three types leverage the `fa-icon` function, and the last type is just a content you want to put in the stack. + - `str`, e.g., `"square"` + - `array`, e.g., `("chess-queen", (fill: white, size: 5.5pt))` + - `arguments`, e.g. `arguments("chess-queen", solid: true, fill: white)` + - `content`, e.g. `fa-chess-queen(solid: true, fill: white)` + +#### Known Issues + +- [typst#2578](https://github.com/typst/typst/issues/2578) [typst-fontawesome#2](https://github.com/duskmoon314/typst-fontawesome/issues/2) + + This is a known issue that the ligatures may not work in headings, list items, grid items, and other elements. You can use the Unicode from the [official website](https://fontawesome.com) to avoid this issue when using Pro sets. + + For most icons, Unicode is used implicitly. So I assume we usually don't need to worry about this. + + Any help on this issue is appreciated. + +## Example + +See the [`example.typ`](https://typst.app/project/rQwGUWt5p33vrsb_uNPR9F) file for a complete example. + +## Contribution + +Feel free to open an issue or a pull request if you find any problems or have any suggestions. + +### Python helper + +The `helper.py` script is used to get metadata via the GraphQL API and generate typst code. I aim only to use standard python libraries, so running it on any platform with python installed should be easy. + +### Repo structure + +- `helper.py`: The helper script to get metadata and generate typst code. +- `lib.typ`: The main entrypoint of the library. +- `lib-impl.typ`: The implementation of `fa-icon`. +- `lib-gen.typ`: The generated functions of icons. +- `example.typ`: An example file to show how to use the library. +- `gallery.typ`: The generated gallery of icons. It is used in the example file. + +## License + +This library is licensed under the MIT license. Feel free to use it in your project. diff --git a/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib-gen.typ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib-gen.typ new file mode 100644 index 00000000..0ac48f1e --- /dev/null +++ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib-gen.typ @@ -0,0 +1,9648 @@ +#import "lib-impl.typ": fa-icon + +// Generated icon list of Font Aewsome 6.6.0 + +#let fa-icon-map = ( + "0": "\u{30}", + "00": "\u{e467}", + "1": "\u{31}", + "2": "\u{32}", + "3": "\u{33}", + "360-degrees": "\u{e2dc}", + "4": "\u{34}", + "42-group": "\u{e080}", + "innosoft": "\u{e080}", + "5": "\u{35}", + "500px": "\u{f26e}", + "6": "\u{36}", + "7": "\u{37}", + "8": "\u{38}", + "9": "\u{39}", + "a": "\u{41}", + "abacus": "\u{f640}", + "accent-grave": "\u{60}", + "accessible-icon": "\u{f368}", + "accusoft": "\u{f369}", + "acorn": "\u{f6ae}", + "address-book": "\u{f2b9}", + "contact-book": "\u{f2b9}", + "address-card": "\u{f2bb}", + "contact-card": "\u{f2bb}", + "vcard": "\u{f2bb}", + "adn": "\u{f170}", + "adversal": "\u{f36a}", + "affiliatetheme": "\u{f36b}", + "airbnb": "\u{f834}", + "air-conditioner": "\u{f8f4}", + "airplay": "\u{e089}", + "alarm-clock": "\u{f34e}", + "alarm-exclamation": "\u{f843}", + "alarm-plus": "\u{f844}", + "alarm-snooze": "\u{f845}", + "album": "\u{f89f}", + "album-circle-plus": "\u{e48c}", + "album-circle-user": "\u{e48d}", + "album-collection": "\u{f8a0}", + "album-collection-circle-plus": "\u{e48e}", + "album-collection-circle-user": "\u{e48f}", + "algolia": "\u{f36c}", + "alicorn": "\u{f6b0}", + "alien": "\u{f8f5}", + "alien-8bit": "\u{f8f6}", + "alien-monster": "\u{f8f6}", + "align-center": "\u{f037}", + "align-justify": "\u{f039}", + "align-left": "\u{f036}", + "align-right": "\u{f038}", + "align-slash": "\u{f846}", + "alipay": "\u{f642}", + "alt": "\u{e08a}", + "amazon": "\u{f270}", + "amazon-pay": "\u{f42c}", + "amilia": "\u{f36d}", + "ampersand": "\u{26}", + "amp-guitar": "\u{f8a1}", + "anchor": "\u{f13d}", + "anchor-circle-check": "\u{e4aa}", + "anchor-circle-exclamation": "\u{e4ab}", + "anchor-circle-xmark": "\u{e4ac}", + "anchor-lock": "\u{e4ad}", + "android": "\u{f17b}", + "angel": "\u{f779}", + "angellist": "\u{f209}", + "angle": "\u{e08c}", + "angle-90": "\u{e08d}", + "angle-down": "\u{f107}", + "angle-left": "\u{f104}", + "angle-right": "\u{f105}", + "angles-down": "\u{f103}", + "angle-double-down": "\u{f103}", + "angles-left": "\u{f100}", + "angle-double-left": "\u{f100}", + "angles-right": "\u{f101}", + "angle-double-right": "\u{f101}", + "angles-up": "\u{f102}", + "angle-double-up": "\u{f102}", + "angles-up-down": "\u{e60d}", + "angle-up": "\u{f106}", + "angrycreative": "\u{f36e}", + "angular": "\u{f420}", + "ankh": "\u{f644}", + "ant": "\u{e680}", + "apartment": "\u{e468}", + "aperture": "\u{e2df}", + "apostrophe": "\u{27}", + "apper": "\u{f371}", + "apple": "\u{f179}", + "apple-core": "\u{e08f}", + "apple-pay": "\u{f415}", + "apple-whole": "\u{f5d1}", + "apple-alt": "\u{f5d1}", + "app-store": "\u{f36f}", + "app-store-ios": "\u{f370}", + "archway": "\u{f557}", + "arrow-down": "\u{f063}", + "arrow-down-1-9": "\u{f162}", + "sort-numeric-asc": "\u{f162}", + "sort-numeric-down": "\u{f162}", + "arrow-down-9-1": "\u{f886}", + "sort-numeric-desc": "\u{f886}", + "sort-numeric-down-alt": "\u{f886}", + "arrow-down-arrow-up": "\u{f883}", + "sort-alt": "\u{f883}", + "arrow-down-a-z": "\u{f15d}", + "sort-alpha-asc": "\u{f15d}", + "sort-alpha-down": "\u{f15d}", + "arrow-down-big-small": "\u{f88c}", + "sort-size-down": "\u{f88c}", + "arrow-down-from-arc": "\u{e614}", + "arrow-down-from-bracket": "\u{e667}", + "arrow-down-from-dotted-line": "\u{e090}", + "arrow-down-from-line": "\u{f345}", + "arrow-from-top": "\u{f345}", + "arrow-down-left": "\u{e091}", + "arrow-down-left-and-arrow-up-right-to-center": "\u{e092}", + "arrow-down-long": "\u{f175}", + "long-arrow-down": "\u{f175}", + "arrow-down-right": "\u{e093}", + "arrow-down-short-wide": "\u{f884}", + "sort-amount-desc": "\u{f884}", + "sort-amount-down-alt": "\u{f884}", + "arrow-down-small-big": "\u{f88d}", + "sort-size-down-alt": "\u{f88d}", + "arrow-down-square-triangle": "\u{f889}", + "sort-shapes-down-alt": "\u{f889}", + "arrow-down-to-arc": "\u{e4ae}", + "arrow-down-to-bracket": "\u{e094}", + "arrow-down-to-dotted-line": "\u{e095}", + "arrow-down-to-line": "\u{f33d}", + "arrow-to-bottom": "\u{f33d}", + "arrow-down-to-square": "\u{e096}", + "arrow-down-triangle-square": "\u{f888}", + "sort-shapes-down": "\u{f888}", + "arrow-down-up-across-line": "\u{e4af}", + "arrow-down-up-lock": "\u{e4b0}", + "arrow-down-wide-short": "\u{f160}", + "sort-amount-asc": "\u{f160}", + "sort-amount-down": "\u{f160}", + "arrow-down-z-a": "\u{f881}", + "sort-alpha-desc": "\u{f881}", + "sort-alpha-down-alt": "\u{f881}", + "arrow-left": "\u{f060}", + "arrow-left-from-arc": "\u{e615}", + "arrow-left-from-bracket": "\u{e668}", + "arrow-left-from-line": "\u{f344}", + "arrow-from-right": "\u{f344}", + "arrow-left-long": "\u{f177}", + "long-arrow-left": "\u{f177}", + "arrow-left-long-to-line": "\u{e3d4}", + "arrow-left-to-arc": "\u{e616}", + "arrow-left-to-bracket": "\u{e669}", + "arrow-left-to-line": "\u{f33e}", + "arrow-to-left": "\u{f33e}", + "arrow-pointer": "\u{f245}", + "mouse-pointer": "\u{f245}", + "arrow-progress": "\u{e5df}", + "arrow-right": "\u{f061}", + "arrow-right-arrow-left": "\u{f0ec}", + "exchange": "\u{f0ec}", + "arrow-right-from-arc": "\u{e4b1}", + "arrow-right-from-bracket": "\u{f08b}", + "sign-out": "\u{f08b}", + "arrow-right-from-line": "\u{f343}", + "arrow-from-left": "\u{f343}", + "arrow-right-long": "\u{f178}", + "long-arrow-right": "\u{f178}", + "arrow-right-long-to-line": "\u{e3d5}", + "arrow-right-to-arc": "\u{e4b2}", + "arrow-right-to-bracket": "\u{f090}", + "sign-in": "\u{f090}", + "arrow-right-to-city": "\u{e4b3}", + "arrow-right-to-line": "\u{f340}", + "arrow-to-right": "\u{f340}", + "arrow-rotate-left": "\u{f0e2}", + "arrow-left-rotate": "\u{f0e2}", + "arrow-rotate-back": "\u{f0e2}", + "arrow-rotate-backward": "\u{f0e2}", + "undo": "\u{f0e2}", + "arrow-rotate-right": "\u{f01e}", + "arrow-right-rotate": "\u{f01e}", + "arrow-rotate-forward": "\u{f01e}", + "redo": "\u{f01e}", + "arrows-cross": "\u{e0a2}", + "arrows-down-to-line": "\u{e4b8}", + "arrows-down-to-people": "\u{e4b9}", + "arrows-from-dotted-line": "\u{e0a3}", + "arrows-from-line": "\u{e0a4}", + "arrows-left-right": "\u{f07e}", + "arrows-h": "\u{f07e}", + "arrows-left-right-to-line": "\u{e4ba}", + "arrows-maximize": "\u{f31d}", + "expand-arrows": "\u{f31d}", + "arrows-minimize": "\u{e0a5}", + "compress-arrows": "\u{e0a5}", + "arrows-repeat": "\u{f364}", + "repeat-alt": "\u{f364}", + "arrows-repeat-1": "\u{f366}", + "repeat-1-alt": "\u{f366}", + "arrows-retweet": "\u{f361}", + "retweet-alt": "\u{f361}", + "arrows-rotate": "\u{f021}", + "refresh": "\u{f021}", + "sync": "\u{f021}", + "arrows-rotate-reverse": "\u{e630}", + "arrows-spin": "\u{e4bb}", + "arrows-split-up-and-left": "\u{e4bc}", + "arrows-to-circle": "\u{e4bd}", + "arrows-to-dot": "\u{e4be}", + "arrows-to-dotted-line": "\u{e0a6}", + "arrows-to-eye": "\u{e4bf}", + "arrows-to-line": "\u{e0a7}", + "arrows-turn-right": "\u{e4c0}", + "arrows-turn-to-dots": "\u{e4c1}", + "arrows-up-down": "\u{f07d}", + "arrows-v": "\u{f07d}", + "arrows-up-down-left-right": "\u{f047}", + "arrows": "\u{f047}", + "arrows-up-to-line": "\u{e4c2}", + "arrow-trend-down": "\u{e097}", + "arrow-trend-up": "\u{e098}", + "arrow-turn-down": "\u{f149}", + "level-down": "\u{f149}", + "arrow-turn-down-left": "\u{e2e1}", + "arrow-turn-down-right": "\u{e3d6}", + "arrow-turn-left": "\u{e632}", + "arrow-turn-left-down": "\u{e633}", + "arrow-turn-left-up": "\u{e634}", + "arrow-turn-right": "\u{e635}", + "arrow-turn-up": "\u{f148}", + "level-up": "\u{f148}", + "arrow-up": "\u{f062}", + "arrow-up-1-9": "\u{f163}", + "sort-numeric-up": "\u{f163}", + "arrow-up-9-1": "\u{f887}", + "sort-numeric-up-alt": "\u{f887}", + "arrow-up-arrow-down": "\u{e099}", + "sort-up-down": "\u{e099}", + "arrow-up-a-z": "\u{f15e}", + "sort-alpha-up": "\u{f15e}", + "arrow-up-big-small": "\u{f88e}", + "sort-size-up": "\u{f88e}", + "arrow-up-from-arc": "\u{e4b4}", + "arrow-up-from-bracket": "\u{e09a}", + "arrow-up-from-dotted-line": "\u{e09b}", + "arrow-up-from-ground-water": "\u{e4b5}", + "arrow-up-from-line": "\u{f342}", + "arrow-from-bottom": "\u{f342}", + "arrow-up-from-square": "\u{e09c}", + "arrow-up-from-water-pump": "\u{e4b6}", + "arrow-up-left": "\u{e09d}", + "arrow-up-left-from-circle": "\u{e09e}", + "arrow-up-long": "\u{f176}", + "long-arrow-up": "\u{f176}", + "arrow-up-right": "\u{e09f}", + "arrow-up-right-and-arrow-down-left-from-center": "\u{e0a0}", + "arrow-up-right-dots": "\u{e4b7}", + "arrow-up-right-from-square": "\u{f08e}", + "external-link": "\u{f08e}", + "arrow-up-short-wide": "\u{f885}", + "sort-amount-up-alt": "\u{f885}", + "arrow-up-small-big": "\u{f88f}", + "sort-size-up-alt": "\u{f88f}", + "arrow-up-square-triangle": "\u{f88b}", + "sort-shapes-up-alt": "\u{f88b}", + "arrow-up-to-arc": "\u{e617}", + "arrow-up-to-bracket": "\u{e66a}", + "arrow-up-to-dotted-line": "\u{e0a1}", + "arrow-up-to-line": "\u{f341}", + "arrow-to-top": "\u{f341}", + "arrow-up-triangle-square": "\u{f88a}", + "sort-shapes-up": "\u{f88a}", + "arrow-up-wide-short": "\u{f161}", + "sort-amount-up": "\u{f161}", + "arrow-up-z-a": "\u{f882}", + "sort-alpha-up-alt": "\u{f882}", + "artstation": "\u{f77a}", + "asterisk": "\u{2a}", + "asymmetrik": "\u{f372}", + "at": "\u{40}", + "atlassian": "\u{f77b}", + "atom": "\u{f5d2}", + "atom-simple": "\u{f5d3}", + "atom-alt": "\u{f5d3}", + "audible": "\u{f373}", + "audio-description": "\u{f29e}", + "audio-description-slash": "\u{e0a8}", + "austral-sign": "\u{e0a9}", + "autoprefixer": "\u{f41c}", + "avianex": "\u{f374}", + "aviato": "\u{f421}", + "avocado": "\u{e0aa}", + "award": "\u{f559}", + "award-simple": "\u{e0ab}", + "aws": "\u{f375}", + "axe": "\u{f6b2}", + "axe-battle": "\u{f6b3}", + "b": "\u{42}", + "baby": "\u{f77c}", + "baby-carriage": "\u{f77d}", + "carriage-baby": "\u{f77d}", + "backpack": "\u{f5d4}", + "backward": "\u{f04a}", + "backward-fast": "\u{f049}", + "fast-backward": "\u{f049}", + "backward-step": "\u{f048}", + "step-backward": "\u{f048}", + "bacon": "\u{f7e5}", + "bacteria": "\u{e059}", + "bacterium": "\u{e05a}", + "badge": "\u{f335}", + "badge-check": "\u{f336}", + "badge-dollar": "\u{f645}", + "badge-percent": "\u{f646}", + "badger-honey": "\u{f6b4}", + "badge-sheriff": "\u{f8a2}", + "badminton": "\u{e33a}", + "bagel": "\u{e3d7}", + "bag-seedling": "\u{e5f2}", + "bag-shopping": "\u{f290}", + "shopping-bag": "\u{f290}", + "bag-shopping-minus": "\u{e650}", + "bag-shopping-plus": "\u{e651}", + "bags-shopping": "\u{f847}", + "baguette": "\u{e3d8}", + "bahai": "\u{f666}", + "haykal": "\u{f666}", + "baht-sign": "\u{e0ac}", + "balloon": "\u{e2e3}", + "balloons": "\u{e2e4}", + "ballot": "\u{f732}", + "ballot-check": "\u{f733}", + "ball-pile": "\u{f77e}", + "ban": "\u{f05e}", + "cancel": "\u{f05e}", + "banana": "\u{e2e5}", + "ban-bug": "\u{f7f9}", + "debug": "\u{f7f9}", + "bandage": "\u{f462}", + "band-aid": "\u{f462}", + "bandcamp": "\u{f2d5}", + "bangladeshi-taka-sign": "\u{e2e6}", + "banjo": "\u{f8a3}", + "ban-parking": "\u{f616}", + "parking-circle-slash": "\u{f616}", + "ban-smoking": "\u{f54d}", + "smoking-ban": "\u{f54d}", + "barcode": "\u{f02a}", + "barcode-read": "\u{f464}", + "barcode-scan": "\u{f465}", + "bars": "\u{f0c9}", + "navicon": "\u{f0c9}", + "bars-filter": "\u{e0ad}", + "bars-progress": "\u{f828}", + "tasks-alt": "\u{f828}", + "bars-sort": "\u{e0ae}", + "bars-staggered": "\u{f550}", + "reorder": "\u{f550}", + "stream": "\u{f550}", + "baseball": "\u{f433}", + "baseball-ball": "\u{f433}", + "baseball-bat-ball": "\u{f432}", + "basketball": "\u{f434}", + "basketball-ball": "\u{f434}", + "basketball-hoop": "\u{f435}", + "basket-shopping": "\u{f291}", + "shopping-basket": "\u{f291}", + "basket-shopping-minus": "\u{e652}", + "basket-shopping-plus": "\u{e653}", + "basket-shopping-simple": "\u{e0af}", + "shopping-basket-alt": "\u{e0af}", + "bat": "\u{f6b5}", + "bath": "\u{f2cd}", + "bathtub": "\u{f2cd}", + "battery-bolt": "\u{f376}", + "battery-empty": "\u{f244}", + "battery-0": "\u{f244}", + "battery-exclamation": "\u{e0b0}", + "battery-full": "\u{f240}", + "battery": "\u{f240}", + "battery-5": "\u{f240}", + "battery-half": "\u{f242}", + "battery-3": "\u{f242}", + "battery-low": "\u{e0b1}", + "battery-1": "\u{e0b1}", + "battery-quarter": "\u{f243}", + "battery-2": "\u{f243}", + "battery-slash": "\u{f377}", + "battery-three-quarters": "\u{f241}", + "battery-4": "\u{f241}", + "battle-net": "\u{f835}", + "bed": "\u{f236}", + "bed-bunk": "\u{f8f8}", + "bed-empty": "\u{f8f9}", + "bed-front": "\u{f8f7}", + "bed-alt": "\u{f8f7}", + "bed-pulse": "\u{f487}", + "procedures": "\u{f487}", + "bee": "\u{e0b2}", + "beer-mug": "\u{e0b3}", + "beer-foam": "\u{e0b3}", + "beer-mug-empty": "\u{f0fc}", + "beer": "\u{f0fc}", + "behance": "\u{f1b4}", + "bell": "\u{f0f3}", + "bell-concierge": "\u{f562}", + "concierge-bell": "\u{f562}", + "bell-exclamation": "\u{f848}", + "bell-on": "\u{f8fa}", + "bell-plus": "\u{f849}", + "bell-ring": "\u{e62c}", + "bells": "\u{f77f}", + "bell-school": "\u{f5d5}", + "bell-school-slash": "\u{f5d6}", + "bell-slash": "\u{f1f6}", + "bench-tree": "\u{e2e7}", + "bezier-curve": "\u{f55b}", + "bicycle": "\u{f206}", + "bilibili": "\u{e3d9}", + "billboard": "\u{e5cd}", + "bimobject": "\u{f378}", + "binary": "\u{e33b}", + "binary-circle-check": "\u{e33c}", + "binary-lock": "\u{e33d}", + "binary-slash": "\u{e33e}", + "bin-bottles": "\u{e5f5}", + "bin-bottles-recycle": "\u{e5f6}", + "binoculars": "\u{f1e5}", + "bin-recycle": "\u{e5f7}", + "biohazard": "\u{f780}", + "bird": "\u{e469}", + "bitbucket": "\u{f171}", + "bitcoin": "\u{f379}", + "bitcoin-sign": "\u{e0b4}", + "bity": "\u{f37a}", + "blackberry": "\u{f37b}", + "black-tie": "\u{f27e}", + "blanket": "\u{f498}", + "blanket-fire": "\u{e3da}", + "blender": "\u{f517}", + "blender-phone": "\u{f6b6}", + "blinds": "\u{f8fb}", + "blinds-open": "\u{f8fc}", + "blinds-raised": "\u{f8fd}", + "block": "\u{e46a}", + "block-brick": "\u{e3db}", + "wall-brick": "\u{e3db}", + "block-brick-fire": "\u{e3dc}", + "firewall": "\u{e3dc}", + "block-question": "\u{e3dd}", + "block-quote": "\u{e0b5}", + "blog": "\u{f781}", + "blogger": "\u{f37c}", + "blogger-b": "\u{f37d}", + "blueberries": "\u{e2e8}", + "bluesky": "\u{e671}", + "bluetooth": "\u{f293}", + "bluetooth-b": "\u{f294}", + "bold": "\u{f032}", + "bolt": "\u{f0e7}", + "zap": "\u{f0e7}", + "bolt-auto": "\u{e0b6}", + "bolt-lightning": "\u{e0b7}", + "bolt-slash": "\u{e0b8}", + "bomb": "\u{f1e2}", + "bone": "\u{f5d7}", + "bone-break": "\u{f5d8}", + "bong": "\u{f55c}", + "book": "\u{f02d}", + "book-arrow-right": "\u{e0b9}", + "book-arrow-up": "\u{e0ba}", + "book-atlas": "\u{f558}", + "atlas": "\u{f558}", + "book-bible": "\u{f647}", + "bible": "\u{f647}", + "book-blank": "\u{f5d9}", + "book-alt": "\u{f5d9}", + "book-bookmark": "\u{e0bb}", + "book-circle-arrow-right": "\u{e0bc}", + "book-circle-arrow-up": "\u{e0bd}", + "book-copy": "\u{e0be}", + "book-font": "\u{e0bf}", + "book-heart": "\u{f499}", + "book-journal-whills": "\u{f66a}", + "journal-whills": "\u{f66a}", + "bookmark": "\u{f02e}", + "bookmark-slash": "\u{e0c2}", + "book-medical": "\u{f7e6}", + "book-open": "\u{f518}", + "book-open-cover": "\u{e0c0}", + "book-open-alt": "\u{e0c0}", + "book-open-reader": "\u{f5da}", + "book-reader": "\u{f5da}", + "book-quran": "\u{f687}", + "quran": "\u{f687}", + "books": "\u{f5db}", + "book-section": "\u{e0c1}", + "book-law": "\u{e0c1}", + "book-skull": "\u{f6b7}", + "book-dead": "\u{f6b7}", + "books-medical": "\u{f7e8}", + "book-sparkles": "\u{f6b8}", + "book-spells": "\u{f6b8}", + "book-tanakh": "\u{f827}", + "tanakh": "\u{f827}", + "book-user": "\u{f7e7}", + "boombox": "\u{f8a5}", + "boot": "\u{f782}", + "booth-curtain": "\u{f734}", + "boot-heeled": "\u{e33f}", + "bootstrap": "\u{f836}", + "border-all": "\u{f84c}", + "border-bottom": "\u{f84d}", + "border-bottom-right": "\u{f854}", + "border-style-alt": "\u{f854}", + "border-center-h": "\u{f89c}", + "border-center-v": "\u{f89d}", + "border-inner": "\u{f84e}", + "border-left": "\u{f84f}", + "border-none": "\u{f850}", + "border-outer": "\u{f851}", + "border-right": "\u{f852}", + "border-top": "\u{f855}", + "border-top-left": "\u{f853}", + "border-style": "\u{f853}", + "bore-hole": "\u{e4c3}", + "bots": "\u{e340}", + "bottle-baby": "\u{e673}", + "bottle-droplet": "\u{e4c4}", + "bottle-water": "\u{e4c5}", + "bow-arrow": "\u{f6b9}", + "bowl-chopsticks": "\u{e2e9}", + "bowl-chopsticks-noodles": "\u{e2ea}", + "bowl-food": "\u{e4c6}", + "bowl-hot": "\u{f823}", + "soup": "\u{f823}", + "bowling-ball": "\u{f436}", + "bowling-ball-pin": "\u{e0c3}", + "bowling-pins": "\u{f437}", + "bowl-rice": "\u{e2eb}", + "bowl-scoop": "\u{e3de}", + "bowl-shaved-ice": "\u{e3de}", + "bowl-scoops": "\u{e3df}", + "bowl-soft-serve": "\u{e46b}", + "bowl-spoon": "\u{e3e0}", + "box": "\u{f466}", + "box-archive": "\u{f187}", + "archive": "\u{f187}", + "box-ballot": "\u{f735}", + "box-check": "\u{f467}", + "box-circle-check": "\u{e0c4}", + "box-dollar": "\u{f4a0}", + "box-usd": "\u{f4a0}", + "boxes-packing": "\u{e4c7}", + "boxes-stacked": "\u{f468}", + "boxes": "\u{f468}", + "boxes-alt": "\u{f468}", + "box-heart": "\u{f49d}", + "boxing-glove": "\u{f438}", + "glove-boxing": "\u{f438}", + "box-open": "\u{f49e}", + "box-open-full": "\u{f49c}", + "box-full": "\u{f49c}", + "box-taped": "\u{f49a}", + "box-alt": "\u{f49a}", + "box-tissue": "\u{e05b}", + "bracket-curly": "\u{7b}", + "bracket-curly-left": "\u{7b}", + "bracket-curly-right": "\u{7d}", + "bracket-round": "\u{28}", + "parenthesis": "\u{28}", + "bracket-round-right": "\u{29}", + "brackets-curly": "\u{f7ea}", + "bracket-square": "\u{5b}", + "bracket": "\u{5b}", + "bracket-left": "\u{5b}", + "bracket-square-right": "\u{5d}", + "brackets-round": "\u{e0c5}", + "parentheses": "\u{e0c5}", + "brackets-square": "\u{f7e9}", + "brackets": "\u{f7e9}", + "braille": "\u{f2a1}", + "brain": "\u{f5dc}", + "brain-arrow-curved-right": "\u{f677}", + "mind-share": "\u{f677}", + "brain-circuit": "\u{e0c6}", + "brake-warning": "\u{e0c7}", + "brave": "\u{e63c}", + "brave-reverse": "\u{e63d}", + "brazilian-real-sign": "\u{e46c}", + "bread-loaf": "\u{f7eb}", + "bread-slice": "\u{f7ec}", + "bread-slice-butter": "\u{e3e1}", + "bridge": "\u{e4c8}", + "bridge-circle-check": "\u{e4c9}", + "bridge-circle-exclamation": "\u{e4ca}", + "bridge-circle-xmark": "\u{e4cb}", + "bridge-lock": "\u{e4cc}", + "bridge-suspension": "\u{e4cd}", + "bridge-water": "\u{e4ce}", + "briefcase": "\u{f0b1}", + "briefcase-arrow-right": "\u{e2f2}", + "briefcase-blank": "\u{e0c8}", + "briefcase-medical": "\u{f469}", + "brightness": "\u{e0c9}", + "brightness-low": "\u{e0ca}", + "bring-forward": "\u{f856}", + "bring-front": "\u{f857}", + "broccoli": "\u{e3e2}", + "broom": "\u{f51a}", + "broom-ball": "\u{f458}", + "quidditch": "\u{f458}", + "quidditch-broom-ball": "\u{f458}", + "broom-wide": "\u{e5d1}", + "browser": "\u{f37e}", + "browsers": "\u{e0cb}", + "brush": "\u{f55d}", + "btc": "\u{f15a}", + "bucket": "\u{e4cf}", + "buffer": "\u{f837}", + "bug": "\u{f188}", + "bugs": "\u{e4d0}", + "bug-slash": "\u{e490}", + "building": "\u{f1ad}", + "building-circle-arrow-right": "\u{e4d1}", + "building-circle-check": "\u{e4d2}", + "building-circle-exclamation": "\u{e4d3}", + "building-circle-xmark": "\u{e4d4}", + "building-columns": "\u{f19c}", + "bank": "\u{f19c}", + "institution": "\u{f19c}", + "museum": "\u{f19c}", + "university": "\u{f19c}", + "building-flag": "\u{e4d5}", + "building-lock": "\u{e4d6}", + "building-magnifying-glass": "\u{e61c}", + "building-memo": "\u{e61e}", + "building-ngo": "\u{e4d7}", + "buildings": "\u{e0cc}", + "building-shield": "\u{e4d8}", + "building-un": "\u{e4d9}", + "building-user": "\u{e4da}", + "building-wheat": "\u{e4db}", + "bulldozer": "\u{e655}", + "bullhorn": "\u{f0a1}", + "bullseye": "\u{f140}", + "bullseye-arrow": "\u{f648}", + "bullseye-pointer": "\u{f649}", + "buoy": "\u{e5b5}", + "buoy-mooring": "\u{e5b6}", + "burger": "\u{f805}", + "hamburger": "\u{f805}", + "burger-cheese": "\u{f7f1}", + "cheeseburger": "\u{f7f1}", + "burger-fries": "\u{e0cd}", + "burger-glass": "\u{e0ce}", + "burger-lettuce": "\u{e3e3}", + "burger-soda": "\u{f858}", + "buromobelexperte": "\u{f37f}", + "burrito": "\u{f7ed}", + "burst": "\u{e4dc}", + "bus": "\u{f207}", + "business-time": "\u{f64a}", + "briefcase-clock": "\u{f64a}", + "bus-school": "\u{f5dd}", + "bus-simple": "\u{f55e}", + "bus-alt": "\u{f55e}", + "butter": "\u{e3e4}", + "buy-n-large": "\u{f8a6}", + "buysellads": "\u{f20d}", + "c": "\u{43}", + "cabin": "\u{e46d}", + "cabinet-filing": "\u{f64b}", + "cable-car": "\u{f7da}", + "tram": "\u{f7da}", + "cactus": "\u{f8a7}", + "caduceus": "\u{e681}", + "cake-candles": "\u{f1fd}", + "birthday-cake": "\u{f1fd}", + "cake": "\u{f1fd}", + "cake-slice": "\u{e3e5}", + "shortcake": "\u{e3e5}", + "calculator": "\u{f1ec}", + "calculator-simple": "\u{f64c}", + "calculator-alt": "\u{f64c}", + "calendar": "\u{f133}", + "calendar-arrow-down": "\u{e0d0}", + "calendar-download": "\u{e0d0}", + "calendar-arrow-up": "\u{e0d1}", + "calendar-upload": "\u{e0d1}", + "calendar-check": "\u{f274}", + "calendar-circle-exclamation": "\u{e46e}", + "calendar-circle-minus": "\u{e46f}", + "calendar-circle-plus": "\u{e470}", + "calendar-circle-user": "\u{e471}", + "calendar-clock": "\u{e0d2}", + "calendar-time": "\u{e0d2}", + "calendar-day": "\u{f783}", + "calendar-days": "\u{f073}", + "calendar-alt": "\u{f073}", + "calendar-exclamation": "\u{f334}", + "calendar-heart": "\u{e0d3}", + "calendar-image": "\u{e0d4}", + "calendar-lines": "\u{e0d5}", + "calendar-note": "\u{e0d5}", + "calendar-lines-pen": "\u{e472}", + "calendar-minus": "\u{f272}", + "calendar-pen": "\u{f333}", + "calendar-edit": "\u{f333}", + "calendar-plus": "\u{f271}", + "calendar-range": "\u{e0d6}", + "calendars": "\u{e0d7}", + "calendar-star": "\u{f736}", + "calendar-users": "\u{e5e2}", + "calendar-week": "\u{f784}", + "calendar-xmark": "\u{f273}", + "calendar-times": "\u{f273}", + "camcorder": "\u{f8a8}", + "video-handheld": "\u{f8a8}", + "camera": "\u{f030}", + "camera-alt": "\u{f030}", + "camera-cctv": "\u{f8ac}", + "cctv": "\u{f8ac}", + "camera-movie": "\u{f8a9}", + "camera-polaroid": "\u{f8aa}", + "camera-retro": "\u{f083}", + "camera-rotate": "\u{e0d8}", + "camera-security": "\u{f8fe}", + "camera-home": "\u{f8fe}", + "camera-slash": "\u{e0d9}", + "camera-viewfinder": "\u{e0da}", + "screenshot": "\u{e0da}", + "camera-web": "\u{f832}", + "webcam": "\u{f832}", + "camera-web-slash": "\u{f833}", + "webcam-slash": "\u{f833}", + "campfire": "\u{f6ba}", + "campground": "\u{f6bb}", + "canadian-maple-leaf": "\u{f785}", + "candle-holder": "\u{f6bc}", + "candy": "\u{e3e7}", + "candy-bar": "\u{e3e8}", + "chocolate-bar": "\u{e3e8}", + "candy-cane": "\u{f786}", + "candy-corn": "\u{f6bd}", + "can-food": "\u{e3e6}", + "cannabis": "\u{f55f}", + "cannon": "\u{e642}", + "capsules": "\u{f46b}", + "car": "\u{f1b9}", + "automobile": "\u{f1b9}", + "caravan": "\u{f8ff}", + "caravan-simple": "\u{e000}", + "caravan-alt": "\u{e000}", + "car-battery": "\u{f5df}", + "battery-car": "\u{f5df}", + "car-bolt": "\u{e341}", + "car-building": "\u{f859}", + "car-bump": "\u{f5e0}", + "car-burst": "\u{f5e1}", + "car-crash": "\u{f5e1}", + "car-bus": "\u{f85a}", + "car-circle-bolt": "\u{e342}", + "card-club": "\u{e3e9}", + "card-diamond": "\u{e3ea}", + "card-heart": "\u{e3eb}", + "cards": "\u{e3ed}", + "cards-blank": "\u{e4df}", + "card-spade": "\u{e3ec}", + "caret-down": "\u{f0d7}", + "caret-left": "\u{f0d9}", + "caret-right": "\u{f0da}", + "caret-up": "\u{f0d8}", + "car-garage": "\u{f5e2}", + "car-mirrors": "\u{e343}", + "car-on": "\u{e4dd}", + "car-rear": "\u{f5de}", + "car-alt": "\u{f5de}", + "carrot": "\u{f787}", + "cars": "\u{f85b}", + "car-side": "\u{f5e4}", + "car-side-bolt": "\u{e344}", + "cart-arrow-down": "\u{f218}", + "cart-arrow-up": "\u{e3ee}", + "cart-circle-arrow-down": "\u{e3ef}", + "cart-circle-arrow-up": "\u{e3f0}", + "cart-circle-check": "\u{e3f1}", + "cart-circle-exclamation": "\u{e3f2}", + "cart-circle-plus": "\u{e3f3}", + "cart-circle-xmark": "\u{e3f4}", + "cart-flatbed": "\u{f474}", + "dolly-flatbed": "\u{f474}", + "cart-flatbed-boxes": "\u{f475}", + "dolly-flatbed-alt": "\u{f475}", + "cart-flatbed-empty": "\u{f476}", + "dolly-flatbed-empty": "\u{f476}", + "cart-flatbed-suitcase": "\u{f59d}", + "luggage-cart": "\u{f59d}", + "car-tilt": "\u{f5e5}", + "cart-minus": "\u{e0db}", + "cart-plus": "\u{f217}", + "cart-shopping": "\u{f07a}", + "shopping-cart": "\u{f07a}", + "cart-shopping-fast": "\u{e0dc}", + "car-tunnel": "\u{e4de}", + "cart-xmark": "\u{e0dd}", + "car-wash": "\u{f5e6}", + "car-wrench": "\u{f5e3}", + "car-mechanic": "\u{f5e3}", + "cash-register": "\u{f788}", + "cassette-betamax": "\u{f8a4}", + "betamax": "\u{f8a4}", + "cassette-tape": "\u{f8ab}", + "cassette-vhs": "\u{f8ec}", + "vhs": "\u{f8ec}", + "castle": "\u{e0de}", + "cat": "\u{f6be}", + "cat-space": "\u{e001}", + "cauldron": "\u{f6bf}", + "cc-amazon-pay": "\u{f42d}", + "cc-amex": "\u{f1f3}", + "cc-apple-pay": "\u{f416}", + "cc-diners-club": "\u{f24c}", + "cc-discover": "\u{f1f2}", + "cc-jcb": "\u{f24b}", + "cc-mastercard": "\u{f1f1}", + "cc-paypal": "\u{f1f4}", + "cc-stripe": "\u{f1f5}", + "cc-visa": "\u{f1f0}", + "cedi-sign": "\u{e0df}", + "centercode": "\u{f380}", + "centos": "\u{f789}", + "cent-sign": "\u{e3f5}", + "certificate": "\u{f0a3}", + "chair": "\u{f6c0}", + "chair-office": "\u{f6c1}", + "chalkboard": "\u{f51b}", + "blackboard": "\u{f51b}", + "chalkboard-user": "\u{f51c}", + "chalkboard-teacher": "\u{f51c}", + "champagne-glass": "\u{f79e}", + "glass-champagne": "\u{f79e}", + "champagne-glasses": "\u{f79f}", + "glass-cheers": "\u{f79f}", + "charging-station": "\u{f5e7}", + "chart-area": "\u{f1fe}", + "area-chart": "\u{f1fe}", + "chart-bar": "\u{f080}", + "bar-chart": "\u{f080}", + "chart-bullet": "\u{e0e1}", + "chart-candlestick": "\u{e0e2}", + "chart-column": "\u{e0e3}", + "chart-gantt": "\u{e0e4}", + "chart-kanban": "\u{e64f}", + "chart-line": "\u{f201}", + "line-chart": "\u{f201}", + "chart-line-down": "\u{f64d}", + "chart-line-up": "\u{e0e5}", + "chart-line-up-down": "\u{e5d7}", + "chart-mixed": "\u{f643}", + "analytics": "\u{f643}", + "chart-mixed-up-circle-currency": "\u{e5d8}", + "chart-mixed-up-circle-dollar": "\u{e5d9}", + "chart-network": "\u{f78a}", + "chart-pie": "\u{f200}", + "pie-chart": "\u{f200}", + "chart-pie-simple": "\u{f64e}", + "chart-pie-alt": "\u{f64e}", + "chart-pie-simple-circle-currency": "\u{e604}", + "chart-pie-simple-circle-dollar": "\u{e605}", + "chart-pyramid": "\u{e0e6}", + "chart-radar": "\u{e0e7}", + "chart-scatter": "\u{f7ee}", + "chart-scatter-3d": "\u{e0e8}", + "chart-scatter-bubble": "\u{e0e9}", + "chart-simple": "\u{e473}", + "chart-simple-horizontal": "\u{e474}", + "chart-tree-map": "\u{e0ea}", + "chart-user": "\u{f6a3}", + "user-chart": "\u{f6a3}", + "chart-waterfall": "\u{e0eb}", + "check": "\u{f00c}", + "check-double": "\u{f560}", + "check-to-slot": "\u{f772}", + "vote-yea": "\u{f772}", + "cheese": "\u{f7ef}", + "cheese-swiss": "\u{f7f0}", + "cherries": "\u{e0ec}", + "chess": "\u{f439}", + "chess-bishop": "\u{f43a}", + "chess-bishop-piece": "\u{f43b}", + "chess-bishop-alt": "\u{f43b}", + "chess-board": "\u{f43c}", + "chess-clock": "\u{f43d}", + "chess-clock-flip": "\u{f43e}", + "chess-clock-alt": "\u{f43e}", + "chess-king": "\u{f43f}", + "chess-king-piece": "\u{f440}", + "chess-king-alt": "\u{f440}", + "chess-knight": "\u{f441}", + "chess-knight-piece": "\u{f442}", + "chess-knight-alt": "\u{f442}", + "chess-pawn": "\u{f443}", + "chess-pawn-piece": "\u{f444}", + "chess-pawn-alt": "\u{f444}", + "chess-queen": "\u{f445}", + "chess-queen-piece": "\u{f446}", + "chess-queen-alt": "\u{f446}", + "chess-rook": "\u{f447}", + "chess-rook-piece": "\u{f448}", + "chess-rook-alt": "\u{f448}", + "chestnut": "\u{e3f6}", + "chevron-down": "\u{f078}", + "chevron-left": "\u{f053}", + "chevron-right": "\u{f054}", + "chevrons-down": "\u{f322}", + "chevron-double-down": "\u{f322}", + "chevrons-left": "\u{f323}", + "chevron-double-left": "\u{f323}", + "chevrons-right": "\u{f324}", + "chevron-double-right": "\u{f324}", + "chevrons-up": "\u{f325}", + "chevron-double-up": "\u{f325}", + "chevron-up": "\u{f077}", + "chf-sign": "\u{e602}", + "child": "\u{f1ae}", + "child-combatant": "\u{e4e0}", + "child-rifle": "\u{e4e0}", + "child-dress": "\u{e59c}", + "child-reaching": "\u{e59d}", + "children": "\u{e4e1}", + "chimney": "\u{f78b}", + "chopsticks": "\u{e3f7}", + "chrome": "\u{f268}", + "chromecast": "\u{f838}", + "church": "\u{f51d}", + "circle": "\u{f111}", + "circle-0": "\u{e0ed}", + "circle-1": "\u{e0ee}", + "circle-2": "\u{e0ef}", + "circle-3": "\u{e0f0}", + "circle-4": "\u{e0f1}", + "circle-5": "\u{e0f2}", + "circle-6": "\u{e0f3}", + "circle-7": "\u{e0f4}", + "circle-8": "\u{e0f5}", + "circle-9": "\u{e0f6}", + "circle-a": "\u{e0f7}", + "circle-ampersand": "\u{e0f8}", + "circle-arrow-down": "\u{f0ab}", + "arrow-circle-down": "\u{f0ab}", + "circle-arrow-down-left": "\u{e0f9}", + "circle-arrow-down-right": "\u{e0fa}", + "circle-arrow-left": "\u{f0a8}", + "arrow-circle-left": "\u{f0a8}", + "circle-arrow-right": "\u{f0a9}", + "arrow-circle-right": "\u{f0a9}", + "circle-arrow-up": "\u{f0aa}", + "arrow-circle-up": "\u{f0aa}", + "circle-arrow-up-left": "\u{e0fb}", + "circle-arrow-up-right": "\u{e0fc}", + "circle-b": "\u{e0fd}", + "circle-bolt": "\u{e0fe}", + "circle-bookmark": "\u{e100}", + "bookmark-circle": "\u{e100}", + "circle-book-open": "\u{e0ff}", + "book-circle": "\u{e0ff}", + "circle-c": "\u{e101}", + "circle-calendar": "\u{e102}", + "calendar-circle": "\u{e102}", + "circle-camera": "\u{e103}", + "camera-circle": "\u{e103}", + "circle-caret-down": "\u{f32d}", + "caret-circle-down": "\u{f32d}", + "circle-caret-left": "\u{f32e}", + "caret-circle-left": "\u{f32e}", + "circle-caret-right": "\u{f330}", + "caret-circle-right": "\u{f330}", + "circle-caret-up": "\u{f331}", + "caret-circle-up": "\u{f331}", + "circle-check": "\u{f058}", + "check-circle": "\u{f058}", + "circle-chevron-down": "\u{f13a}", + "chevron-circle-down": "\u{f13a}", + "circle-chevron-left": "\u{f137}", + "chevron-circle-left": "\u{f137}", + "circle-chevron-right": "\u{f138}", + "chevron-circle-right": "\u{f138}", + "circle-chevron-up": "\u{f139}", + "chevron-circle-up": "\u{f139}", + "circle-d": "\u{e104}", + "circle-dashed": "\u{e105}", + "circle-divide": "\u{e106}", + "circle-dollar": "\u{f2e8}", + "dollar-circle": "\u{f2e8}", + "usd-circle": "\u{f2e8}", + "circle-dollar-to-slot": "\u{f4b9}", + "donate": "\u{f4b9}", + "circle-dot": "\u{f192}", + "dot-circle": "\u{f192}", + "circle-down": "\u{f358}", + "arrow-alt-circle-down": "\u{f358}", + "circle-down-left": "\u{e107}", + "circle-down-right": "\u{e108}", + "circle-e": "\u{e109}", + "circle-ellipsis": "\u{e10a}", + "circle-ellipsis-vertical": "\u{e10b}", + "circle-envelope": "\u{e10c}", + "envelope-circle": "\u{e10c}", + "circle-euro": "\u{e5ce}", + "circle-exclamation": "\u{f06a}", + "exclamation-circle": "\u{f06a}", + "circle-exclamation-check": "\u{e10d}", + "circle-f": "\u{e10e}", + "circle-g": "\u{e10f}", + "circle-gf": "\u{e67f}", + "circle-h": "\u{f47e}", + "hospital-symbol": "\u{f47e}", + "circle-half": "\u{e110}", + "circle-half-stroke": "\u{f042}", + "adjust": "\u{f042}", + "circle-heart": "\u{f4c7}", + "heart-circle": "\u{f4c7}", + "circle-i": "\u{e111}", + "circle-info": "\u{f05a}", + "info-circle": "\u{f05a}", + "circle-j": "\u{e112}", + "circle-k": "\u{e113}", + "circle-l": "\u{e114}", + "circle-left": "\u{f359}", + "arrow-alt-circle-left": "\u{f359}", + "circle-location-arrow": "\u{f602}", + "location-circle": "\u{f602}", + "circle-m": "\u{e115}", + "circle-microphone": "\u{e116}", + "microphone-circle": "\u{e116}", + "circle-microphone-lines": "\u{e117}", + "microphone-circle-alt": "\u{e117}", + "circle-minus": "\u{f056}", + "minus-circle": "\u{f056}", + "circle-n": "\u{e118}", + "circle-nodes": "\u{e4e2}", + "circle-notch": "\u{f1ce}", + "circle-o": "\u{e119}", + "circle-p": "\u{e11a}", + "circle-parking": "\u{f615}", + "parking-circle": "\u{f615}", + "circle-pause": "\u{f28b}", + "pause-circle": "\u{f28b}", + "circle-phone": "\u{e11b}", + "phone-circle": "\u{e11b}", + "circle-phone-flip": "\u{e11c}", + "phone-circle-alt": "\u{e11c}", + "circle-phone-hangup": "\u{e11d}", + "phone-circle-down": "\u{e11d}", + "circle-play": "\u{f144}", + "play-circle": "\u{f144}", + "circle-plus": "\u{f055}", + "plus-circle": "\u{f055}", + "circle-q": "\u{e11e}", + "circle-quarter": "\u{e11f}", + "circle-quarters": "\u{e3f8}", + "circle-quarter-stroke": "\u{e5d3}", + "circle-question": "\u{f059}", + "question-circle": "\u{f059}", + "circle-r": "\u{e120}", + "circle-radiation": "\u{f7ba}", + "radiation-alt": "\u{f7ba}", + "circle-right": "\u{f35a}", + "arrow-alt-circle-right": "\u{f35a}", + "circle-s": "\u{e121}", + "circle-small": "\u{e122}", + "circle-sort": "\u{e030}", + "sort-circle": "\u{e030}", + "circle-sort-down": "\u{e031}", + "sort-circle-down": "\u{e031}", + "circle-sort-up": "\u{e032}", + "sort-circle-up": "\u{e032}", + "circles-overlap": "\u{e600}", + "circle-star": "\u{e123}", + "star-circle": "\u{e123}", + "circle-sterling": "\u{e5cf}", + "circle-stop": "\u{f28d}", + "stop-circle": "\u{f28d}", + "circle-t": "\u{e124}", + "circle-three-quarters": "\u{e125}", + "circle-three-quarters-stroke": "\u{e5d4}", + "circle-trash": "\u{e126}", + "trash-circle": "\u{e126}", + "circle-u": "\u{e127}", + "circle-up": "\u{f35b}", + "arrow-alt-circle-up": "\u{f35b}", + "circle-up-left": "\u{e128}", + "circle-up-right": "\u{e129}", + "circle-user": "\u{f2bd}", + "user-circle": "\u{f2bd}", + "circle-v": "\u{e12a}", + "circle-video": "\u{e12b}", + "video-circle": "\u{e12b}", + "circle-w": "\u{e12c}", + "circle-waveform-lines": "\u{e12d}", + "waveform-circle": "\u{e12d}", + "circle-wifi": "\u{e67d}", + "circle-wifi-circle-wifi": "\u{e67e}", + "circle-wifi-group": "\u{e67e}", + "circle-x": "\u{e12e}", + "circle-xmark": "\u{f057}", + "times-circle": "\u{f057}", + "xmark-circle": "\u{f057}", + "circle-y": "\u{e12f}", + "circle-yen": "\u{e5d0}", + "circle-z": "\u{e130}", + "citrus": "\u{e2f4}", + "citrus-slice": "\u{e2f5}", + "city": "\u{f64f}", + "clapperboard": "\u{e131}", + "clapperboard-play": "\u{e132}", + "clarinet": "\u{f8ad}", + "claw-marks": "\u{f6c2}", + "clipboard": "\u{f328}", + "clipboard-check": "\u{f46c}", + "clipboard-list": "\u{f46d}", + "clipboard-list-check": "\u{f737}", + "clipboard-medical": "\u{e133}", + "clipboard-prescription": "\u{f5e8}", + "clipboard-question": "\u{e4e3}", + "clipboard-user": "\u{f7f3}", + "clock": "\u{f017}", + "clock-four": "\u{f017}", + "clock-desk": "\u{e134}", + "clock-eight": "\u{e345}", + "clock-eight-thirty": "\u{e346}", + "clock-eleven": "\u{e347}", + "clock-eleven-thirty": "\u{e348}", + "clock-five": "\u{e349}", + "clock-five-thirty": "\u{e34a}", + "clock-four-thirty": "\u{e34b}", + "clock-nine": "\u{e34c}", + "clock-nine-thirty": "\u{e34d}", + "clock-one": "\u{e34e}", + "clock-one-thirty": "\u{e34f}", + "clock-rotate-left": "\u{f1da}", + "history": "\u{f1da}", + "clock-seven": "\u{e350}", + "clock-seven-thirty": "\u{e351}", + "clock-six": "\u{e352}", + "clock-six-thirty": "\u{e353}", + "clock-ten": "\u{e354}", + "clock-ten-thirty": "\u{e355}", + "clock-three": "\u{e356}", + "clock-three-thirty": "\u{e357}", + "clock-twelve": "\u{e358}", + "clock-twelve-thirty": "\u{e359}", + "clock-two": "\u{e35a}", + "clock-two-thirty": "\u{e35b}", + "clone": "\u{f24d}", + "closed-captioning": "\u{f20a}", + "closed-captioning-slash": "\u{e135}", + "clothes-hanger": "\u{e136}", + "cloud": "\u{f0c2}", + "cloud-arrow-down": "\u{f0ed}", + "cloud-download": "\u{f0ed}", + "cloud-download-alt": "\u{f0ed}", + "cloud-arrow-up": "\u{f0ee}", + "cloud-upload": "\u{f0ee}", + "cloud-upload-alt": "\u{f0ee}", + "cloud-binary": "\u{e601}", + "cloud-bolt": "\u{f76c}", + "thunderstorm": "\u{f76c}", + "cloud-bolt-moon": "\u{f76d}", + "thunderstorm-moon": "\u{f76d}", + "cloud-bolt-sun": "\u{f76e}", + "thunderstorm-sun": "\u{f76e}", + "cloud-check": "\u{e35c}", + "cloud-drizzle": "\u{f738}", + "cloud-exclamation": "\u{e491}", + "cloudflare": "\u{e07d}", + "cloud-fog": "\u{f74e}", + "fog": "\u{f74e}", + "cloud-hail": "\u{f739}", + "cloud-hail-mixed": "\u{f73a}", + "cloud-meatball": "\u{f73b}", + "cloud-minus": "\u{e35d}", + "cloud-moon": "\u{f6c3}", + "cloud-moon-rain": "\u{f73c}", + "cloud-music": "\u{f8ae}", + "cloud-plus": "\u{e35e}", + "cloud-question": "\u{e492}", + "cloud-rain": "\u{f73d}", + "cloud-rainbow": "\u{f73e}", + "clouds": "\u{f744}", + "cloudscale": "\u{f383}", + "cloud-showers": "\u{f73f}", + "cloud-showers-heavy": "\u{f740}", + "cloud-showers-water": "\u{e4e4}", + "cloud-slash": "\u{e137}", + "cloud-sleet": "\u{f741}", + "cloudsmith": "\u{f384}", + "clouds-moon": "\u{f745}", + "cloud-snow": "\u{f742}", + "clouds-sun": "\u{f746}", + "cloud-sun": "\u{f6c4}", + "cloud-sun-rain": "\u{f743}", + "cloudversify": "\u{f385}", + "cloud-word": "\u{e138}", + "cloud-xmark": "\u{e35f}", + "clover": "\u{e139}", + "club": "\u{f327}", + "cmplid": "\u{e360}", + "coconut": "\u{e2f6}", + "code": "\u{f121}", + "code-branch": "\u{f126}", + "code-commit": "\u{f386}", + "code-compare": "\u{e13a}", + "code-fork": "\u{e13b}", + "code-merge": "\u{f387}", + "codepen": "\u{f1cb}", + "code-pull-request": "\u{e13c}", + "code-pull-request-closed": "\u{e3f9}", + "code-pull-request-draft": "\u{e3fa}", + "code-simple": "\u{e13d}", + "codiepie": "\u{f284}", + "coffee-bean": "\u{e13e}", + "coffee-beans": "\u{e13f}", + "coffee-pot": "\u{e002}", + "coffin": "\u{f6c6}", + "coffin-cross": "\u{e051}", + "coin": "\u{f85c}", + "coin-blank": "\u{e3fb}", + "coin-front": "\u{e3fc}", + "coins": "\u{f51e}", + "coin-vertical": "\u{e3fd}", + "colon": "\u{3a}", + "colon-sign": "\u{e140}", + "columns-3": "\u{e361}", + "comet": "\u{e003}", + "comma": "\u{2c}", + "command": "\u{e142}", + "comment": "\u{f075}", + "comment-arrow-down": "\u{e143}", + "comment-arrow-up": "\u{e144}", + "comment-arrow-up-right": "\u{e145}", + "comment-captions": "\u{e146}", + "comment-check": "\u{f4ac}", + "comment-code": "\u{e147}", + "comment-dollar": "\u{f651}", + "comment-dots": "\u{f4ad}", + "commenting": "\u{f4ad}", + "comment-exclamation": "\u{f4af}", + "comment-heart": "\u{e5c8}", + "comment-image": "\u{e148}", + "comment-lines": "\u{f4b0}", + "comment-medical": "\u{f7f5}", + "comment-middle": "\u{e149}", + "comment-middle-top": "\u{e14a}", + "comment-minus": "\u{f4b1}", + "comment-music": "\u{f8b0}", + "comment-pen": "\u{f4ae}", + "comment-edit": "\u{f4ae}", + "comment-plus": "\u{f4b2}", + "comment-question": "\u{e14b}", + "comment-quote": "\u{e14c}", + "comments": "\u{f086}", + "comments-dollar": "\u{f653}", + "comment-slash": "\u{f4b3}", + "comment-smile": "\u{f4b4}", + "comment-sms": "\u{f7cd}", + "sms": "\u{f7cd}", + "comments-question": "\u{e14e}", + "comments-question-check": "\u{e14f}", + "comment-text": "\u{e14d}", + "comment-xmark": "\u{f4b5}", + "comment-times": "\u{f4b5}", + "compact-disc": "\u{f51f}", + "compass": "\u{f14e}", + "compass-drafting": "\u{f568}", + "drafting-compass": "\u{f568}", + "compass-slash": "\u{f5e9}", + "compress": "\u{f066}", + "compress-wide": "\u{f326}", + "computer": "\u{e4e5}", + "computer-classic": "\u{f8b1}", + "computer-mouse": "\u{f8cc}", + "mouse": "\u{f8cc}", + "computer-mouse-scrollwheel": "\u{f8cd}", + "mouse-alt": "\u{f8cd}", + "computer-speaker": "\u{f8b2}", + "confluence": "\u{f78d}", + "connectdevelop": "\u{f20e}", + "container-storage": "\u{f4b7}", + "contao": "\u{f26d}", + "conveyor-belt": "\u{f46e}", + "conveyor-belt-arm": "\u{e5f8}", + "conveyor-belt-boxes": "\u{f46f}", + "conveyor-belt-alt": "\u{f46f}", + "conveyor-belt-empty": "\u{e150}", + "cookie": "\u{f563}", + "cookie-bite": "\u{f564}", + "copy": "\u{f0c5}", + "copyright": "\u{f1f9}", + "corn": "\u{f6c7}", + "corner": "\u{e3fe}", + "cotton-bureau": "\u{f89e}", + "couch": "\u{f4b8}", + "court-sport": "\u{e643}", + "cow": "\u{f6c8}", + "cowbell": "\u{f8b3}", + "cowbell-circle-plus": "\u{f8b4}", + "cowbell-more": "\u{f8b4}", + "cpanel": "\u{f388}", + "crab": "\u{e3ff}", + "crate-apple": "\u{f6b1}", + "apple-crate": "\u{f6b1}", + "crate-empty": "\u{e151}", + "creative-commons": "\u{f25e}", + "creative-commons-by": "\u{f4e7}", + "creative-commons-nc": "\u{f4e8}", + "creative-commons-nc-eu": "\u{f4e9}", + "creative-commons-nc-jp": "\u{f4ea}", + "creative-commons-nd": "\u{f4eb}", + "creative-commons-pd": "\u{f4ec}", + "creative-commons-pd-alt": "\u{f4ed}", + "creative-commons-remix": "\u{f4ee}", + "creative-commons-sa": "\u{f4ef}", + "creative-commons-sampling": "\u{f4f0}", + "creative-commons-sampling-plus": "\u{f4f1}", + "creative-commons-share": "\u{f4f2}", + "creative-commons-zero": "\u{f4f3}", + "credit-card": "\u{f09d}", + "credit-card-alt": "\u{f09d}", + "credit-card-blank": "\u{f389}", + "credit-card-front": "\u{f38a}", + "cricket-bat-ball": "\u{f449}", + "cricket": "\u{f449}", + "critical-role": "\u{f6c9}", + "croissant": "\u{f7f6}", + "crop": "\u{f125}", + "crop-simple": "\u{f565}", + "crop-alt": "\u{f565}", + "cross": "\u{f654}", + "crosshairs": "\u{f05b}", + "crosshairs-simple": "\u{e59f}", + "crow": "\u{f520}", + "crown": "\u{f521}", + "crutch": "\u{f7f7}", + "crutches": "\u{f7f8}", + "cruzeiro-sign": "\u{e152}", + "crystal-ball": "\u{e362}", + "css3": "\u{f13c}", + "css3-alt": "\u{f38b}", + "cube": "\u{f1b2}", + "cubes": "\u{f1b3}", + "cubes-stacked": "\u{e4e6}", + "cucumber": "\u{e401}", + "cupcake": "\u{e402}", + "cup-straw": "\u{e363}", + "cup-straw-swoosh": "\u{e364}", + "cup-togo": "\u{f6c5}", + "coffee-togo": "\u{f6c5}", + "curling-stone": "\u{f44a}", + "curling": "\u{f44a}", + "custard": "\u{e403}", + "cuttlefish": "\u{f38c}", + "d": "\u{44}", + "dagger": "\u{f6cb}", + "dailymotion": "\u{e052}", + "d-and-d": "\u{f38d}", + "d-and-d-beyond": "\u{f6ca}", + "dart-lang": "\u{e693}", + "dash": "\u{e404}", + "minus-large": "\u{e404}", + "dashcube": "\u{f210}", + "database": "\u{f1c0}", + "debian": "\u{e60b}", + "deer": "\u{f78e}", + "deer-rudolph": "\u{f78f}", + "deezer": "\u{e077}", + "delete-left": "\u{f55a}", + "backspace": "\u{f55a}", + "delete-right": "\u{e154}", + "delicious": "\u{f1a5}", + "democrat": "\u{f747}", + "deploydog": "\u{f38e}", + "deskpro": "\u{f38f}", + "desktop": "\u{f390}", + "desktop-alt": "\u{f390}", + "desktop-arrow-down": "\u{e155}", + "dev": "\u{f6cc}", + "deviantart": "\u{f1bd}", + "dharmachakra": "\u{f655}", + "dhl": "\u{f790}", + "diagram-cells": "\u{e475}", + "diagram-lean-canvas": "\u{e156}", + "diagram-nested": "\u{e157}", + "diagram-next": "\u{e476}", + "diagram-predecessor": "\u{e477}", + "diagram-previous": "\u{e478}", + "diagram-project": "\u{f542}", + "project-diagram": "\u{f542}", + "diagram-sankey": "\u{e158}", + "diagram-subtask": "\u{e479}", + "diagram-successor": "\u{e47a}", + "diagram-venn": "\u{e15a}", + "dial": "\u{e15b}", + "dial-med-high": "\u{e15b}", + "dial-high": "\u{e15c}", + "dial-low": "\u{e15d}", + "dial-max": "\u{e15e}", + "dial-med": "\u{e15f}", + "dial-med-low": "\u{e160}", + "dial-min": "\u{e161}", + "dial-off": "\u{e162}", + "diamond": "\u{f219}", + "diamond-exclamation": "\u{e405}", + "diamond-half": "\u{e5b7}", + "diamond-half-stroke": "\u{e5b8}", + "diamonds-4": "\u{e68b}", + "diamond-turn-right": "\u{f5eb}", + "directions": "\u{f5eb}", + "diaspora": "\u{f791}", + "dice": "\u{f522}", + "dice-d10": "\u{f6cd}", + "dice-d12": "\u{f6ce}", + "dice-d20": "\u{f6cf}", + "dice-d4": "\u{f6d0}", + "dice-d6": "\u{f6d1}", + "dice-d8": "\u{f6d2}", + "dice-five": "\u{f523}", + "dice-four": "\u{f524}", + "dice-one": "\u{f525}", + "dice-six": "\u{f526}", + "dice-three": "\u{f527}", + "dice-two": "\u{f528}", + "digg": "\u{f1a6}", + "digital-ocean": "\u{f391}", + "dinosaur": "\u{e5fe}", + "diploma": "\u{f5ea}", + "scroll-ribbon": "\u{f5ea}", + "disc-drive": "\u{f8b5}", + "discord": "\u{f392}", + "discourse": "\u{f393}", + "disease": "\u{f7fa}", + "display": "\u{e163}", + "display-arrow-down": "\u{e164}", + "display-chart-up": "\u{e5e3}", + "display-chart-up-circle-currency": "\u{e5e5}", + "display-chart-up-circle-dollar": "\u{e5e6}", + "display-code": "\u{e165}", + "desktop-code": "\u{e165}", + "display-medical": "\u{e166}", + "desktop-medical": "\u{e166}", + "display-slash": "\u{e2fa}", + "desktop-slash": "\u{e2fa}", + "distribute-spacing-horizontal": "\u{e365}", + "distribute-spacing-vertical": "\u{e366}", + "ditto": "\u{22}", + "divide": "\u{f529}", + "dna": "\u{f471}", + "dochub": "\u{f394}", + "docker": "\u{f395}", + "dog": "\u{f6d3}", + "dog-leashed": "\u{f6d4}", + "dollar-sign": "\u{24}", + "dollar": "\u{24}", + "usd": "\u{24}", + "dolly": "\u{f472}", + "dolly-box": "\u{f472}", + "dolly-empty": "\u{f473}", + "dolphin": "\u{e168}", + "dong-sign": "\u{e169}", + "do-not-enter": "\u{f5ec}", + "donut": "\u{e406}", + "doughnut": "\u{e406}", + "door-closed": "\u{f52a}", + "door-open": "\u{f52b}", + "dove": "\u{f4ba}", + "down": "\u{f354}", + "arrow-alt-down": "\u{f354}", + "down-from-bracket": "\u{e66b}", + "down-from-dotted-line": "\u{e407}", + "down-from-line": "\u{f349}", + "arrow-alt-from-top": "\u{f349}", + "down-left": "\u{e16a}", + "down-left-and-up-right-to-center": "\u{f422}", + "compress-alt": "\u{f422}", + "download": "\u{f019}", + "down-long": "\u{f309}", + "long-arrow-alt-down": "\u{f309}", + "down-right": "\u{e16b}", + "down-to-bracket": "\u{e4e7}", + "down-to-dotted-line": "\u{e408}", + "down-to-line": "\u{f34a}", + "arrow-alt-to-bottom": "\u{f34a}", + "draft2digital": "\u{f396}", + "dragon": "\u{f6d5}", + "draw-circle": "\u{f5ed}", + "draw-polygon": "\u{f5ee}", + "draw-square": "\u{f5ef}", + "dreidel": "\u{f792}", + "dribbble": "\u{f17d}", + "drone": "\u{f85f}", + "drone-front": "\u{f860}", + "drone-alt": "\u{f860}", + "dropbox": "\u{f16b}", + "droplet": "\u{f043}", + "tint": "\u{f043}", + "droplet-degree": "\u{f748}", + "dewpoint": "\u{f748}", + "droplet-percent": "\u{f750}", + "humidity": "\u{f750}", + "droplet-slash": "\u{f5c7}", + "tint-slash": "\u{f5c7}", + "drum": "\u{f569}", + "drum-steelpan": "\u{f56a}", + "drumstick": "\u{f6d6}", + "drumstick-bite": "\u{f6d7}", + "drupal": "\u{f1a9}", + "dryer": "\u{f861}", + "dryer-heat": "\u{f862}", + "dryer-alt": "\u{f862}", + "duck": "\u{f6d8}", + "dumbbell": "\u{f44b}", + "dumpster": "\u{f793}", + "dumpster-fire": "\u{f794}", + "dungeon": "\u{f6d9}", + "dyalog": "\u{f399}", + "e": "\u{45}", + "ear": "\u{f5f0}", + "ear-deaf": "\u{f2a4}", + "deaf": "\u{f2a4}", + "deafness": "\u{f2a4}", + "hard-of-hearing": "\u{f2a4}", + "ear-listen": "\u{f2a2}", + "assistive-listening-systems": "\u{f2a2}", + "earlybirds": "\u{f39a}", + "ear-muffs": "\u{f795}", + "earth-africa": "\u{f57c}", + "globe-africa": "\u{f57c}", + "earth-americas": "\u{f57d}", + "earth": "\u{f57d}", + "earth-america": "\u{f57d}", + "globe-americas": "\u{f57d}", + "earth-asia": "\u{f57e}", + "globe-asia": "\u{f57e}", + "earth-europe": "\u{f7a2}", + "globe-europe": "\u{f7a2}", + "earth-oceania": "\u{e47b}", + "globe-oceania": "\u{e47b}", + "ebay": "\u{f4f4}", + "eclipse": "\u{f749}", + "edge": "\u{f282}", + "edge-legacy": "\u{e078}", + "egg": "\u{f7fb}", + "egg-fried": "\u{f7fc}", + "eggplant": "\u{e16c}", + "eject": "\u{f052}", + "elementor": "\u{f430}", + "elephant": "\u{f6da}", + "elevator": "\u{e16d}", + "ellipsis": "\u{f141}", + "ellipsis-h": "\u{f141}", + "ellipsis-stroke": "\u{f39b}", + "ellipsis-h-alt": "\u{f39b}", + "ellipsis-stroke-vertical": "\u{f39c}", + "ellipsis-v-alt": "\u{f39c}", + "ellipsis-vertical": "\u{f142}", + "ellipsis-v": "\u{f142}", + "ello": "\u{f5f1}", + "ember": "\u{f423}", + "empire": "\u{f1d1}", + "empty-set": "\u{f656}", + "engine": "\u{e16e}", + "engine-warning": "\u{f5f2}", + "engine-exclamation": "\u{f5f2}", + "envelope": "\u{f0e0}", + "envelope-circle-check": "\u{e4e8}", + "envelope-dot": "\u{e16f}", + "envelope-badge": "\u{e16f}", + "envelope-open": "\u{f2b6}", + "envelope-open-dollar": "\u{f657}", + "envelope-open-text": "\u{f658}", + "envelopes": "\u{e170}", + "envelopes-bulk": "\u{f674}", + "mail-bulk": "\u{f674}", + "envira": "\u{f299}", + "equals": "\u{3d}", + "eraser": "\u{f12d}", + "erlang": "\u{f39d}", + "escalator": "\u{e171}", + "ethereum": "\u{f42e}", + "ethernet": "\u{f796}", + "etsy": "\u{f2d7}", + "euro-sign": "\u{f153}", + "eur": "\u{f153}", + "euro": "\u{f153}", + "evernote": "\u{f839}", + "excavator": "\u{e656}", + "exclamation": "\u{21}", + "expand": "\u{f065}", + "expand-wide": "\u{f320}", + "expeditedssl": "\u{f23e}", + "explosion": "\u{e4e9}", + "eye": "\u{f06e}", + "eye-dropper": "\u{f1fb}", + "eye-dropper-empty": "\u{f1fb}", + "eyedropper": "\u{f1fb}", + "eye-dropper-full": "\u{e172}", + "eye-dropper-half": "\u{e173}", + "eye-evil": "\u{f6db}", + "eye-low-vision": "\u{f2a8}", + "low-vision": "\u{f2a8}", + "eyes": "\u{e367}", + "eye-slash": "\u{f070}", + "f": "\u{46}", + "face-angry": "\u{f556}", + "angry": "\u{f556}", + "face-angry-horns": "\u{e368}", + "face-anguished": "\u{e369}", + "face-anxious-sweat": "\u{e36a}", + "face-astonished": "\u{e36b}", + "face-awesome": "\u{e409}", + "gave-dandy": "\u{e409}", + "face-beam-hand-over-mouth": "\u{e47c}", + "facebook": "\u{f09a}", + "facebook-f": "\u{f39e}", + "facebook-messenger": "\u{f39f}", + "face-clouds": "\u{e47d}", + "face-confounded": "\u{e36c}", + "face-confused": "\u{e36d}", + "face-cowboy-hat": "\u{e36e}", + "face-diagonal-mouth": "\u{e47e}", + "face-disappointed": "\u{e36f}", + "face-disguise": "\u{e370}", + "face-dizzy": "\u{f567}", + "dizzy": "\u{f567}", + "face-dotted": "\u{e47f}", + "face-downcast-sweat": "\u{e371}", + "face-drooling": "\u{e372}", + "face-exhaling": "\u{e480}", + "face-explode": "\u{e2fe}", + "exploding-head": "\u{e2fe}", + "face-expressionless": "\u{e373}", + "face-eyes-xmarks": "\u{e374}", + "face-fearful": "\u{e375}", + "face-flushed": "\u{f579}", + "flushed": "\u{f579}", + "face-frown": "\u{f119}", + "frown": "\u{f119}", + "face-frown-open": "\u{f57a}", + "frown-open": "\u{f57a}", + "face-frown-slight": "\u{e376}", + "face-glasses": "\u{e377}", + "face-grimace": "\u{f57f}", + "grimace": "\u{f57f}", + "face-grin": "\u{f580}", + "grin": "\u{f580}", + "face-grin-beam": "\u{f582}", + "grin-beam": "\u{f582}", + "face-grin-beam-sweat": "\u{f583}", + "grin-beam-sweat": "\u{f583}", + "face-grin-hearts": "\u{f584}", + "grin-hearts": "\u{f584}", + "face-grin-squint": "\u{f585}", + "grin-squint": "\u{f585}", + "face-grin-squint-tears": "\u{f586}", + "grin-squint-tears": "\u{f586}", + "face-grin-stars": "\u{f587}", + "grin-stars": "\u{f587}", + "face-grin-tears": "\u{f588}", + "grin-tears": "\u{f588}", + "face-grin-tongue": "\u{f589}", + "grin-tongue": "\u{f589}", + "face-grin-tongue-squint": "\u{f58a}", + "grin-tongue-squint": "\u{f58a}", + "face-grin-tongue-wink": "\u{f58b}", + "grin-tongue-wink": "\u{f58b}", + "face-grin-wide": "\u{f581}", + "grin-alt": "\u{f581}", + "face-grin-wink": "\u{f58c}", + "grin-wink": "\u{f58c}", + "face-hand-over-mouth": "\u{e378}", + "face-hand-peeking": "\u{e481}", + "face-hand-yawn": "\u{e379}", + "face-head-bandage": "\u{e37a}", + "face-holding-back-tears": "\u{e482}", + "face-hushed": "\u{e37b}", + "face-icicles": "\u{e37c}", + "face-kiss": "\u{f596}", + "kiss": "\u{f596}", + "face-kiss-beam": "\u{f597}", + "kiss-beam": "\u{f597}", + "face-kiss-closed-eyes": "\u{e37d}", + "face-kiss-wink-heart": "\u{f598}", + "kiss-wink-heart": "\u{f598}", + "face-laugh": "\u{f599}", + "laugh": "\u{f599}", + "face-laugh-beam": "\u{f59a}", + "laugh-beam": "\u{f59a}", + "face-laugh-squint": "\u{f59b}", + "laugh-squint": "\u{f59b}", + "face-laugh-wink": "\u{f59c}", + "laugh-wink": "\u{f59c}", + "face-lying": "\u{e37e}", + "face-mask": "\u{e37f}", + "face-meh": "\u{f11a}", + "meh": "\u{f11a}", + "face-meh-blank": "\u{f5a4}", + "meh-blank": "\u{f5a4}", + "face-melting": "\u{e483}", + "face-monocle": "\u{e380}", + "face-nauseated": "\u{e381}", + "face-nose-steam": "\u{e382}", + "face-party": "\u{e383}", + "face-pensive": "\u{e384}", + "face-persevering": "\u{e385}", + "face-pleading": "\u{e386}", + "face-pouting": "\u{e387}", + "face-raised-eyebrow": "\u{e388}", + "face-relieved": "\u{e389}", + "face-rolling-eyes": "\u{f5a5}", + "meh-rolling-eyes": "\u{f5a5}", + "face-sad-cry": "\u{f5b3}", + "sad-cry": "\u{f5b3}", + "face-sad-sweat": "\u{e38a}", + "face-sad-tear": "\u{f5b4}", + "sad-tear": "\u{f5b4}", + "face-saluting": "\u{e484}", + "face-scream": "\u{e38b}", + "face-shush": "\u{e38c}", + "face-sleeping": "\u{e38d}", + "face-sleepy": "\u{e38e}", + 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"\u{e3a0}", + "face-weary": "\u{e3a1}", + "face-woozy": "\u{e3a2}", + "face-worried": "\u{e3a3}", + "face-zany": "\u{e3a4}", + "face-zipper": "\u{e3a5}", + "falafel": "\u{e40a}", + "family": "\u{e300}", + "family-dress": "\u{e301}", + "family-pants": "\u{e302}", + "fan": "\u{f863}", + "fan-table": "\u{e004}", + "fantasy-flight-games": "\u{f6dc}", + "farm": "\u{f864}", + "barn-silo": "\u{f864}", + "faucet": "\u{e005}", + "faucet-drip": "\u{e006}", + "fax": "\u{f1ac}", + "feather": "\u{f52d}", + "feather-pointed": "\u{f56b}", + "feather-alt": "\u{f56b}", + "fedex": "\u{f797}", + "fedora": "\u{f798}", + "fence": "\u{e303}", + "ferris-wheel": "\u{e174}", + "ferry": "\u{e4ea}", + "field-hockey-stick-ball": "\u{f44c}", + "field-hockey": "\u{f44c}", + "figma": "\u{f799}", + "file": "\u{f15b}", + "file-arrow-down": "\u{f56d}", + "file-download": "\u{f56d}", + "file-arrow-up": "\u{f574}", + "file-upload": "\u{f574}", + "file-audio": "\u{f1c7}", + "file-binary": "\u{e175}", + "file-cad": "\u{e672}", + "file-certificate": "\u{f5f3}", + "file-award": "\u{f5f3}", + "file-chart-column": "\u{f659}", + "file-chart-line": "\u{f659}", + "file-chart-pie": "\u{f65a}", + "file-check": "\u{f316}", + "file-circle-check": "\u{e5a0}", + "file-circle-exclamation": "\u{e4eb}", + "file-circle-info": "\u{e493}", + "file-circle-minus": "\u{e4ed}", + "file-circle-plus": "\u{e494}", + "file-circle-question": "\u{e4ef}", + "file-circle-xmark": "\u{e5a1}", + "file-code": "\u{f1c9}", + "file-contract": "\u{f56c}", + "file-csv": "\u{f6dd}", + "file-dashed-line": "\u{f877}", + "page-break": "\u{f877}", + "file-doc": "\u{e5ed}", + "file-eps": "\u{e644}", + "file-excel": "\u{f1c3}", + "file-exclamation": "\u{f31a}", + "file-export": "\u{f56e}", + "arrow-right-from-file": "\u{f56e}", + "file-gif": "\u{e645}", + "file-heart": "\u{e176}", + "file-image": "\u{f1c5}", + "file-import": "\u{f56f}", + "arrow-right-to-file": "\u{f56f}", + "file-invoice": "\u{f570}", + "file-invoice-dollar": "\u{f571}", + "file-jpg": "\u{e646}", + "file-lines": "\u{f15c}", + "file-alt": "\u{f15c}", + "file-text": "\u{f15c}", + "file-lock": "\u{e3a6}", + "file-magnifying-glass": "\u{f865}", + "file-search": "\u{f865}", + "file-medical": "\u{f477}", + "file-minus": "\u{f318}", + "file-mov": "\u{e647}", + "file-mp3": "\u{e648}", + "file-mp4": "\u{e649}", + "file-music": "\u{f8b6}", + "file-pdf": "\u{f1c1}", + "file-pen": "\u{f31c}", + "file-edit": "\u{f31c}", + "file-plus": "\u{f319}", + "file-plus-minus": "\u{e177}", + "file-png": "\u{e666}", + "file-powerpoint": "\u{f1c4}", + "file-ppt": "\u{e64a}", + "file-prescription": "\u{f572}", + "files": "\u{e178}", + "file-shield": "\u{e4f0}", + "file-signature": "\u{f573}", + "file-slash": "\u{e3a7}", + "files-medical": "\u{f7fd}", + "file-spreadsheet": "\u{f65b}", + "file-svg": "\u{e64b}", + "file-user": "\u{f65c}", + "file-vector": "\u{e64c}", + "file-video": "\u{f1c8}", + "file-waveform": "\u{f478}", + "file-medical-alt": "\u{f478}", + "file-word": "\u{f1c2}", + "file-xls": "\u{e64d}", + "file-xmark": "\u{f317}", + "file-times": "\u{f317}", + "file-xml": "\u{e654}", + "file-zip": "\u{e5ee}", + "file-zipper": "\u{f1c6}", + "file-archive": "\u{f1c6}", + "fill": "\u{f575}", + "fill-drip": "\u{f576}", + "film": "\u{f008}", + "film-canister": "\u{f8b7}", + "film-cannister": "\u{f8b7}", + "films": "\u{e17a}", + "film-simple": "\u{f3a0}", + "film-alt": "\u{f3a0}", + "film-slash": "\u{e179}", + "filter": "\u{f0b0}", + "filter-circle-dollar": "\u{f662}", + "funnel-dollar": "\u{f662}", + "filter-circle-xmark": "\u{e17b}", + "filter-list": "\u{e17c}", + "filters": "\u{e17e}", + "filter-slash": "\u{e17d}", + "fingerprint": "\u{f577}", + "fire": "\u{f06d}", + "fire-burner": "\u{e4f1}", + "fire-extinguisher": "\u{f134}", + "fire-flame": "\u{f6df}", + "flame": "\u{f6df}", + "fire-flame-curved": "\u{f7e4}", + "fire-alt": "\u{f7e4}", + "fire-flame-simple": "\u{f46a}", + "burn": "\u{f46a}", + "firefox": "\u{f269}", + "firefox-browser": "\u{e007}", + "fire-hydrant": "\u{e17f}", + "fireplace": "\u{f79a}", + "fire-smoke": "\u{f74b}", + "firstdraft": "\u{f3a1}", + "first-order": "\u{f2b0}", + "first-order-alt": "\u{f50a}", + "fish": "\u{f578}", + "fish-bones": "\u{e304}", + "fish-cooked": "\u{f7fe}", + "fish-fins": "\u{e4f2}", + "fishing-rod": "\u{e3a8}", + "flag": "\u{f024}", + "flag-checkered": "\u{f11e}", + "flag-pennant": "\u{f456}", + "pennant": "\u{f456}", + "flag-swallowtail": "\u{f74c}", + "flag-alt": "\u{f74c}", + "flag-usa": "\u{f74d}", + "flashlight": "\u{f8b8}", + "flask": "\u{f0c3}", + "flask-gear": "\u{e5f1}", + "flask-round-poison": "\u{f6e0}", + "flask-poison": "\u{f6e0}", + "flask-round-potion": "\u{f6e1}", + "flask-potion": "\u{f6e1}", + "flask-vial": "\u{e4f3}", + "flatbread": "\u{e40b}", + "flatbread-stuffed": "\u{e40c}", + "flickr": "\u{f16e}", + "flipboard": "\u{f44d}", + "floppy-disk": "\u{f0c7}", + "save": "\u{f0c7}", + "floppy-disk-circle-arrow-right": "\u{e180}", + "save-circle-arrow-right": "\u{e180}", + "floppy-disk-circle-xmark": "\u{e181}", + "floppy-disk-times": "\u{e181}", + "save-circle-xmark": "\u{e181}", + "save-times": "\u{e181}", + "floppy-disk-pen": "\u{e182}", + "floppy-disks": "\u{e183}", + "florin-sign": "\u{e184}", + "flower": "\u{f7ff}", + "flower-daffodil": "\u{f800}", + "flower-tulip": "\u{f801}", + "flute": "\u{f8b9}", + "flutter": "\u{e694}", + "flux-capacitor": "\u{f8ba}", + "fly": "\u{f417}", + "flying-disc": "\u{e3a9}", + "folder": "\u{f07b}", + "folder-blank": "\u{f07b}", + "folder-arrow-down": "\u{e053}", + "folder-download": "\u{e053}", + "folder-arrow-up": "\u{e054}", + "folder-upload": "\u{e054}", + "folder-bookmark": "\u{e186}", + "folder-check": "\u{e64e}", + "folder-closed": "\u{e185}", + "folder-gear": "\u{e187}", + "folder-cog": "\u{e187}", + "folder-grid": "\u{e188}", + "folder-heart": "\u{e189}", + "folder-image": "\u{e18a}", + "folder-magnifying-glass": "\u{e18b}", + "folder-search": "\u{e18b}", + "folder-medical": "\u{e18c}", + "folder-minus": "\u{f65d}", + "folder-music": "\u{e18d}", + "folder-open": "\u{f07c}", + "folder-plus": "\u{f65e}", + "folders": "\u{f660}", + "folder-tree": "\u{f802}", + "folder-user": "\u{e18e}", + "folder-xmark": "\u{f65f}", + "folder-times": "\u{f65f}", + "fondue-pot": "\u{e40d}", + "font": "\u{f031}", + "font-awesome": "\u{f2b4}", + "font-awesome-flag": "\u{f2b4}", + "font-awesome-logo-full": "\u{f2b4}", + "font-case": "\u{f866}", + "fonticons": "\u{f280}", + "fonticons-fi": "\u{f3a2}", + "football": "\u{f44e}", + "football-ball": "\u{f44e}", + "football-helmet": "\u{f44f}", + "fork": "\u{f2e3}", + "utensil-fork": "\u{f2e3}", + "fork-knife": "\u{f2e6}", + "utensils-alt": "\u{f2e6}", + "forklift": "\u{f47a}", + "fort": "\u{e486}", + "fort-awesome": "\u{f286}", + "fort-awesome-alt": "\u{f3a3}", + "forumbee": "\u{f211}", + "forward": "\u{f04e}", + "forward-fast": "\u{f050}", + "fast-forward": "\u{f050}", + "forward-step": "\u{f051}", + "step-forward": "\u{f051}", + "foursquare": "\u{f180}", + "frame": "\u{e495}", + "franc-sign": "\u{e18f}", + "freebsd": "\u{f3a4}", + "free-code-camp": "\u{f2c5}", + "french-fries": "\u{f803}", + "frog": "\u{f52e}", + "fulcrum": "\u{f50b}", + "function": "\u{f661}", + "futbol": "\u{f1e3}", + "futbol-ball": "\u{f1e3}", + "soccer-ball": "\u{f1e3}", + "g": "\u{47}", + "galactic-republic": "\u{f50c}", + "galactic-senate": "\u{f50d}", + "galaxy": "\u{e008}", + "gallery-thumbnails": "\u{e3aa}", + "game-board": "\u{f867}", + "game-board-simple": "\u{f868}", + "game-board-alt": "\u{f868}", + "game-console-handheld": "\u{f8bb}", + "game-console-handheld-crank": "\u{e5b9}", + "gamepad": "\u{f11b}", + "gamepad-modern": "\u{e5a2}", + "gamepad-alt": "\u{e5a2}", + "garage": "\u{e009}", + "garage-car": "\u{e00a}", + "garage-open": "\u{e00b}", + "garlic": "\u{e40e}", + "gas-pump": "\u{f52f}", + "gas-pump-slash": "\u{f5f4}", + "gauge": "\u{f624}", + "dashboard": "\u{f624}", + "gauge-med": "\u{f624}", + "tachometer-alt-average": "\u{f624}", + "gauge-circle-bolt": "\u{e496}", + "gauge-circle-minus": "\u{e497}", + "gauge-circle-plus": "\u{e498}", + "gauge-high": "\u{f625}", + "tachometer-alt": "\u{f625}", + "tachometer-alt-fast": "\u{f625}", + "gauge-low": "\u{f627}", + "tachometer-alt-slow": "\u{f627}", + "gauge-max": "\u{f626}", + "tachometer-alt-fastest": "\u{f626}", + "gauge-min": "\u{f628}", + "tachometer-alt-slowest": "\u{f628}", + "gauge-simple": "\u{f629}", + "gauge-simple-med": "\u{f629}", + "tachometer-average": "\u{f629}", + "gauge-simple-high": "\u{f62a}", + "tachometer": "\u{f62a}", + "tachometer-fast": "\u{f62a}", + "gauge-simple-low": "\u{f62c}", + "tachometer-slow": "\u{f62c}", + "gauge-simple-max": "\u{f62b}", + "tachometer-fastest": "\u{f62b}", + "gauge-simple-min": "\u{f62d}", + "tachometer-slowest": "\u{f62d}", + "gavel": "\u{f0e3}", + "legal": "\u{f0e3}", + "gear": "\u{f013}", + "cog": "\u{f013}", + "gear-code": "\u{e5e8}", + "gear-complex": "\u{e5e9}", + "gear-complex-code": "\u{e5eb}", + "gears": "\u{f085}", + "cogs": "\u{f085}", + "gem": "\u{f3a5}", + "genderless": "\u{f22d}", + "get-pocket": "\u{f265}", + "gg": "\u{f260}", + "gg-circle": "\u{f261}", + "ghost": "\u{f6e2}", + "gif": "\u{e190}", + "gift": "\u{f06b}", + "gift-card": "\u{f663}", + "gifts": "\u{f79c}", + "gingerbread-man": "\u{f79d}", + "git": "\u{f1d3}", + "git-alt": "\u{f841}", + "github": "\u{f09b}", + "github-alt": "\u{f113}", + "gitkraken": "\u{f3a6}", + "gitlab": "\u{f296}", + "gitter": "\u{f426}", + "glass": "\u{f804}", + "glass-citrus": "\u{f869}", + "glass-empty": "\u{e191}", + "glasses": "\u{f530}", + "glasses-round": "\u{f5f5}", + "glasses-alt": "\u{f5f5}", + "glass-half": "\u{e192}", + "glass-half-empty": "\u{e192}", + "glass-half-full": "\u{e192}", + "glass-water": "\u{e4f4}", + "glass-water-droplet": "\u{e4f5}", + "glide": "\u{f2a5}", + "glide-g": "\u{f2a6}", + "globe": "\u{f0ac}", + "globe-pointer": "\u{e60e}", + "globe-snow": "\u{f7a3}", + "globe-stand": "\u{f5f6}", + "globe-wifi": "\u{e685}", + "goal-net": "\u{e3ab}", + "gofore": "\u{f3a7}", + "golang": "\u{e40f}", + "golf-ball-tee": "\u{f450}", + "golf-ball": "\u{f450}", + "golf-club": "\u{f451}", + "golf-flag-hole": "\u{e3ac}", + "goodreads": "\u{f3a8}", + "goodreads-g": "\u{f3a9}", + "google": "\u{f1a0}", + "google-drive": "\u{f3aa}", + "google-pay": "\u{e079}", + "google-play": "\u{f3ab}", + "google-plus": "\u{f2b3}", + "google-plus-g": "\u{f0d5}", + "google-scholar": "\u{e63b}", + "google-wallet": "\u{f1ee}", + "gopuram": "\u{f664}", + "graduation-cap": "\u{f19d}", + "mortar-board": "\u{f19d}", + "gramophone": "\u{f8bd}", + "grapes": "\u{e306}", + "grate": "\u{e193}", + "grate-droplet": "\u{e194}", + "gratipay": "\u{f184}", + "grav": "\u{f2d6}", + "greater-than": "\u{3e}", + "greater-than-equal": "\u{f532}", + "grid": "\u{e195}", + "grid-3": "\u{e195}", + "grid-2": "\u{e196}", + "grid-2-plus": "\u{e197}", + "grid-4": "\u{e198}", + "grid-5": "\u{e199}", + "grid-dividers": "\u{e3ad}", + "grid-horizontal": "\u{e307}", + "grid-round": "\u{e5da}", + "grid-round-2": "\u{e5db}", + "grid-round-2-plus": "\u{e5dc}", + "grid-round-4": "\u{e5dd}", + "grid-round-5": "\u{e5de}", + "grill": "\u{e5a3}", + "grill-fire": "\u{e5a4}", + "grill-hot": "\u{e5a5}", + "grip": "\u{f58d}", + "grip-horizontal": "\u{f58d}", + "grip-dots": "\u{e410}", + "grip-dots-vertical": "\u{e411}", + "gripfire": "\u{f3ac}", + "grip-lines": "\u{f7a4}", + "grip-lines-vertical": "\u{f7a5}", + "grip-vertical": "\u{f58e}", + "group-arrows-rotate": "\u{e4f6}", + "grunt": "\u{f3ad}", + "guarani-sign": "\u{e19a}", + "guilded": "\u{e07e}", + "guitar": "\u{f7a6}", + "guitar-electric": "\u{f8be}", + "guitars": "\u{f8bf}", + "gulp": "\u{f3ae}", + "gun": "\u{e19b}", + "gun-slash": "\u{e19c}", + "gun-squirt": "\u{e19d}", + "h": "\u{48}", + "h1": "\u{f313}", + "h2": "\u{f314}", + "h3": "\u{f315}", + "h4": "\u{f86a}", + "h5": "\u{e412}", + "h6": "\u{e413}", + "hacker-news": "\u{f1d4}", + "hackerrank": "\u{f5f7}", + "hammer": "\u{f6e3}", + "hammer-brush": "\u{e620}", + "hammer-crash": "\u{e414}", + "hammer-war": "\u{f6e4}", + "hamsa": "\u{f665}", + "hand": "\u{f256}", + "hand-paper": "\u{f256}", + "hand-back-fist": "\u{f255}", + "hand-rock": "\u{f255}", + "hand-back-point-down": "\u{e19e}", + "hand-back-point-left": "\u{e19f}", + "hand-back-point-ribbon": "\u{e1a0}", + "hand-back-point-right": "\u{e1a1}", + "hand-back-point-up": "\u{e1a2}", + "handcuffs": "\u{e4f8}", + "hand-dots": "\u{f461}", + "allergies": "\u{f461}", + "hand-fingers-crossed": "\u{e1a3}", + "hand-fist": "\u{f6de}", + "fist-raised": "\u{f6de}", + "hand-heart": "\u{f4bc}", + "hand-holding": "\u{f4bd}", + "hand-holding-box": "\u{f47b}", + "hand-holding-circle-dollar": "\u{e621}", + "hand-holding-dollar": "\u{f4c0}", + "hand-holding-usd": "\u{f4c0}", + "hand-holding-droplet": "\u{f4c1}", + "hand-holding-water": "\u{f4c1}", + "hand-holding-hand": "\u{e4f7}", + "hand-holding-heart": "\u{f4be}", + "hand-holding-magic": "\u{f6e5}", + "hand-holding-medical": "\u{e05c}", + "hand-holding-seedling": "\u{f4bf}", + "hand-holding-skull": "\u{e1a4}", + "hand-horns": "\u{e1a9}", + "hand-lizard": "\u{f258}", + "hand-love": "\u{e1a5}", + "hand-middle-finger": "\u{f806}", + "hand-peace": "\u{f25b}", + "hand-point-down": "\u{f0a7}", + "hand-pointer": "\u{f25a}", + "hand-point-left": "\u{f0a5}", + "hand-point-ribbon": "\u{e1a6}", + "hand-point-right": "\u{f0a4}", + "hand-point-up": "\u{f0a6}", + "hands": "\u{f2a7}", + "sign-language": "\u{f2a7}", + "signing": "\u{f2a7}", + "hands-asl-interpreting": "\u{f2a3}", + "american-sign-language-interpreting": "\u{f2a3}", + "asl-interpreting": "\u{f2a3}", + "hands-american-sign-language-interpreting": "\u{f2a3}", + "hands-bound": "\u{e4f9}", + "hands-bubbles": "\u{e05e}", + "hands-wash": "\u{e05e}", + "hand-scissors": "\u{f257}", + "hands-clapping": "\u{e1a8}", + "handshake": "\u{f2b5}", + "handshake-angle": "\u{f4c4}", + "hands-helping": "\u{f4c4}", + "handshake-simple": "\u{f4c6}", + "handshake-alt": "\u{f4c6}", + "handshake-simple-slash": "\u{e05f}", + "handshake-alt-slash": "\u{e05f}", + "handshake-slash": "\u{e060}", + "hands-holding": "\u{f4c2}", + "hands-holding-child": "\u{e4fa}", + "hands-holding-circle": "\u{e4fb}", + "hands-holding-diamond": "\u{f47c}", + "hand-receiving": "\u{f47c}", + "hands-holding-dollar": "\u{f4c5}", + "hands-usd": "\u{f4c5}", + "hands-holding-heart": "\u{f4c3}", + "hands-heart": "\u{f4c3}", + "hand-sparkles": "\u{e05d}", + "hand-spock": "\u{f259}", + "hands-praying": "\u{f684}", + "praying-hands": "\u{f684}", + "hand-wave": "\u{e1a7}", + "hanukiah": "\u{f6e6}", + "hard-drive": "\u{f0a0}", + "hdd": "\u{f0a0}", + "hashnode": "\u{e499}", + "hashtag": "\u{23}", + "hashtag-lock": "\u{e415}", + "hat-beach": "\u{e606}", + "hat-chef": "\u{f86b}", + "hat-cowboy": "\u{f8c0}", + "hat-cowboy-side": "\u{f8c1}", + "hat-santa": "\u{f7a7}", + "hat-winter": "\u{f7a8}", + "hat-witch": "\u{f6e7}", + "hat-wizard": "\u{f6e8}", + "heading": "\u{f1dc}", + "header": "\u{f1dc}", + "headphones": "\u{f025}", + "headphones-simple": "\u{f58f}", + "headphones-alt": "\u{f58f}", + "headset": "\u{f590}", + "head-side": "\u{f6e9}", + "head-side-brain": "\u{f808}", + "head-side-cough": "\u{e061}", + "head-side-cough-slash": "\u{e062}", + "head-side-gear": "\u{e611}", + "head-side-goggles": "\u{f6ea}", + "head-vr": "\u{f6ea}", + "head-side-headphones": "\u{f8c2}", + "head-side-heart": "\u{e1aa}", + "head-side-mask": "\u{e063}", + "head-side-medical": "\u{f809}", + "head-side-virus": "\u{e064}", + "heart": "\u{f004}", + "heart-circle-bolt": "\u{e4fc}", + "heart-circle-check": "\u{e4fd}", + "heart-circle-exclamation": "\u{e4fe}", + "heart-circle-minus": "\u{e4ff}", + "heart-circle-plus": "\u{e500}", + "heart-circle-xmark": "\u{e501}", + "heart-crack": "\u{f7a9}", + "heart-broken": "\u{f7a9}", + "heart-half": "\u{e1ab}", + "heart-half-stroke": "\u{e1ac}", + "heart-half-alt": "\u{e1ac}", + "heart-pulse": "\u{f21e}", + "heartbeat": "\u{f21e}", + "heat": "\u{e00c}", + "helicopter": "\u{f533}", + "helicopter-symbol": "\u{e502}", + "helmet-battle": "\u{f6eb}", + "helmet-safety": "\u{f807}", + "hard-hat": "\u{f807}", + "hat-hard": "\u{f807}", + "helmet-un": "\u{e503}", + "hexagon": "\u{f312}", + "hexagon-check": "\u{e416}", + "hexagon-divide": "\u{e1ad}", + "hexagon-exclamation": "\u{e417}", + "hexagon-image": "\u{e504}", + "hexagon-minus": "\u{f307}", + "minus-hexagon": "\u{f307}", + "hexagon-plus": "\u{f300}", + "plus-hexagon": "\u{f300}", + "hexagon-vertical-nft": "\u{e505}", + "hexagon-vertical-nft-slanted": "\u{e506}", + "hexagon-xmark": "\u{f2ee}", + "times-hexagon": "\u{f2ee}", + "xmark-hexagon": "\u{f2ee}", + "high-definition": "\u{e1ae}", + "rectangle-hd": "\u{e1ae}", + "highlighter": "\u{f591}", + "highlighter-line": "\u{e1af}", + "hill-avalanche": "\u{e507}", + "hill-rockslide": "\u{e508}", + "hippo": "\u{f6ed}", + "hips": "\u{f452}", + "hire-a-helper": "\u{f3b0}", + "hive": "\u{e07f}", + "hockey-mask": "\u{f6ee}", + "hockey-puck": 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"home-lg-alt": "\u{f015}", + "house-blank": "\u{e487}", + "home-blank": "\u{e487}", + "house-building": "\u{e1b1}", + "house-chimney": "\u{e3af}", + "home-lg": "\u{e3af}", + "house-chimney-blank": "\u{e3b0}", + "house-chimney-crack": "\u{f6f1}", + "house-damage": "\u{f6f1}", + "house-chimney-heart": "\u{e1b2}", + "house-chimney-medical": "\u{f7f2}", + "clinic-medical": "\u{f7f2}", + "house-chimney-user": "\u{e065}", + "house-chimney-window": "\u{e00d}", + "house-circle-check": "\u{e509}", + "house-circle-exclamation": "\u{e50a}", + "house-circle-xmark": "\u{e50b}", + "house-crack": "\u{e3b1}", + "house-day": "\u{e00e}", + "house-fire": "\u{e50c}", + "house-flag": "\u{e50d}", + "house-flood-water": "\u{e50e}", + "house-flood-water-circle-arrow-right": "\u{e50f}", + "house-heart": "\u{f4c9}", + "home-heart": "\u{f4c9}", + "house-laptop": "\u{e066}", + "laptop-house": "\u{e066}", + "house-lock": "\u{e510}", + "house-medical": "\u{e3b2}", + "house-medical-circle-check": "\u{e511}", + "house-medical-circle-exclamation": "\u{e512}", + "house-medical-circle-xmark": "\u{e513}", + "house-medical-flag": "\u{e514}", + "house-night": "\u{e010}", + "house-person-leave": "\u{e00f}", + "house-leave": "\u{e00f}", + "house-person-depart": "\u{e00f}", + "house-person-return": "\u{e011}", + "house-person-arrive": "\u{e011}", + "house-return": "\u{e011}", + "house-signal": "\u{e012}", + "house-tree": "\u{e1b3}", + "house-tsunami": "\u{e515}", + "house-turret": "\u{e1b4}", + "house-user": "\u{e1b0}", + "home-user": "\u{e1b0}", + "house-water": "\u{f74f}", + "house-flood": "\u{f74f}", + "house-window": "\u{e3b3}", + "houzz": "\u{f27c}", + "hryvnia-sign": "\u{f6f2}", + "hryvnia": "\u{f6f2}", + "html5": "\u{f13b}", + "hubspot": "\u{f3b2}", + "hundred-points": "\u{e41c}", + "100": "\u{e41c}", + "hurricane": "\u{f751}", + "hydra": "\u{e686}", + "hyphen": "\u{2d}", + "i": "\u{49}", + "ice-cream": "\u{f810}", + "ice-skate": "\u{f7ac}", + "icicles": "\u{f7ad}", + "icons": "\u{f86d}", + 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"jsfiddle": "\u{f1cc}", + "jug": "\u{f8c6}", + "jug-bottle": "\u{e5fb}", + "jug-detergent": "\u{e519}", + "jxl": "\u{e67b}", + "k": "\u{4b}", + "kaaba": "\u{f66b}", + "kaggle": "\u{f5fa}", + "kazoo": "\u{f8c7}", + "kerning": "\u{f86f}", + "key": "\u{f084}", + "keybase": "\u{f4f5}", + "keyboard": "\u{f11c}", + "keyboard-brightness": "\u{e1c0}", + "keyboard-brightness-low": "\u{e1c1}", + "keyboard-down": "\u{e1c2}", + "keyboard-left": "\u{e1c3}", + "keycdn": "\u{f3ba}", + "keynote": "\u{f66c}", + "key-skeleton": "\u{f6f3}", + "key-skeleton-left-right": "\u{e3b4}", + "khanda": "\u{f66d}", + "kickstarter": "\u{f3bb}", + "square-kickstarter": "\u{f3bb}", + "kickstarter-k": "\u{f3bc}", + "kidneys": "\u{f5fb}", + "kip-sign": "\u{e1c4}", + "kitchen-set": "\u{e51a}", + "kite": "\u{f6f4}", + "kit-medical": "\u{f479}", + "first-aid": "\u{f479}", + "kiwi-bird": "\u{f535}", + "kiwi-fruit": "\u{e30c}", + "knife": "\u{f2e4}", + "utensil-knife": "\u{f2e4}", + "knife-kitchen": "\u{f6f5}", + "korvue": "\u{f42f}", + "l": "\u{4c}", + "lacrosse-stick": "\u{e3b5}", + "lacrosse-stick-ball": "\u{e3b6}", + "lambda": "\u{f66e}", + "lamp": "\u{f4ca}", + "lamp-desk": "\u{e014}", + "lamp-floor": "\u{e015}", + "lamp-street": "\u{e1c5}", + "landmark": "\u{f66f}", + "landmark-dome": "\u{f752}", + "landmark-alt": "\u{f752}", + "landmark-flag": "\u{e51c}", + "landmark-magnifying-glass": "\u{e622}", + "land-mine-on": "\u{e51b}", + "language": "\u{f1ab}", + "laptop": "\u{f109}", + "laptop-arrow-down": "\u{e1c6}", + "laptop-binary": "\u{e5e7}", + "laptop-code": "\u{f5fc}", + "laptop-file": "\u{e51d}", + "laptop-medical": "\u{f812}", + "laptop-mobile": "\u{f87a}", + "phone-laptop": "\u{f87a}", + "laptop-slash": "\u{e1c7}", + "laravel": "\u{f3bd}", + "lari-sign": "\u{e1c8}", + "lasso": "\u{f8c8}", + "lasso-sparkles": "\u{e1c9}", + "lastfm": "\u{f202}", + "layer-group": "\u{f5fd}", + "layer-minus": "\u{f5fe}", + "layer-group-minus": "\u{f5fe}", + "layer-plus": "\u{f5ff}", + "layer-group-plus": "\u{f5ff}", + "leaf": "\u{f06c}", + "leaf-heart": "\u{f4cb}", + "leaf-maple": "\u{f6f6}", + "leaf-oak": "\u{f6f7}", + "leafy-green": "\u{e41d}", + "leanpub": "\u{f212}", + "left": "\u{f355}", + "arrow-alt-left": "\u{f355}", + "left-from-bracket": "\u{e66c}", + "left-from-line": "\u{f348}", + "arrow-alt-from-right": "\u{f348}", + "left-long": "\u{f30a}", + "long-arrow-alt-left": "\u{f30a}", + "left-long-to-line": "\u{e41e}", + "left-right": "\u{f337}", + "arrows-alt-h": "\u{f337}", + "left-to-bracket": "\u{e66d}", + "left-to-line": "\u{f34b}", + "arrow-alt-to-left": "\u{f34b}", + "lemon": "\u{f094}", + "less": "\u{f41d}", + "less-than": "\u{3c}", + "less-than-equal": "\u{f537}", + "letterboxd": "\u{e62d}", + "life-ring": "\u{f1cd}", + "lightbulb": "\u{f0eb}", + "lightbulb-cfl": "\u{e5a6}", + "lightbulb-cfl-on": "\u{e5a7}", + "lightbulb-dollar": "\u{f670}", + "lightbulb-exclamation": "\u{f671}", + "lightbulb-exclamation-on": "\u{e1ca}", + "lightbulb-gear": "\u{e5fd}", + "lightbulb-message": "\u{e687}", + "lightbulb-on": "\u{f672}", + "lightbulb-slash": "\u{f673}", + "light-ceiling": "\u{e016}", + "light-emergency": "\u{e41f}", + "light-emergency-on": "\u{e420}", + "lighthouse": "\u{e612}", + "lights-holiday": "\u{f7b2}", + "light-switch": "\u{e017}", + "light-switch-off": "\u{e018}", + "light-switch-on": "\u{e019}", + "line": "\u{f3c0}", + "line-columns": "\u{f870}", + "line-height": "\u{f871}", + "lines-leaning": "\u{e51e}", + "link": "\u{f0c1}", + "chain": "\u{f0c1}", + "linkedin": "\u{f08c}", + "linkedin-in": "\u{f0e1}", + "link-horizontal": "\u{e1cb}", + "chain-horizontal": "\u{e1cb}", + "link-horizontal-slash": "\u{e1cc}", + "chain-horizontal-slash": "\u{e1cc}", + "link-simple": "\u{e1cd}", + "link-simple-slash": "\u{e1ce}", + "link-slash": "\u{f127}", + "chain-broken": "\u{f127}", + "chain-slash": "\u{f127}", + "unlink": "\u{f127}", + "linode": "\u{f2b8}", + "linux": "\u{f17c}", + "lips": "\u{f600}", + "lira-sign": "\u{f195}", + "list": "\u{f03a}", + "list-squares": "\u{f03a}", + "list-check": "\u{f0ae}", + "tasks": "\u{f0ae}", + "list-dropdown": "\u{e1cf}", + "list-music": "\u{f8c9}", + "list-ol": "\u{f0cb}", + "list-1-2": "\u{f0cb}", + "list-numeric": "\u{f0cb}", + "list-radio": "\u{e1d0}", + "list-timeline": "\u{e1d1}", + "list-tree": "\u{e1d2}", + "list-ul": "\u{f0ca}", + "list-dots": "\u{f0ca}", + "litecoin-sign": "\u{e1d3}", + "loader": "\u{e1d4}", + "lobster": "\u{e421}", + "location-arrow": "\u{f124}", + "location-arrow-up": "\u{e63a}", + "location-check": "\u{f606}", + "map-marker-check": "\u{f606}", + "location-crosshairs": "\u{f601}", + "location": "\u{f601}", + "location-crosshairs-slash": "\u{f603}", + "location-slash": "\u{f603}", + "location-dot": "\u{f3c5}", + "map-marker-alt": "\u{f3c5}", + "location-dot-slash": "\u{f605}", + "map-marker-alt-slash": "\u{f605}", + "location-exclamation": "\u{f608}", + "map-marker-exclamation": "\u{f608}", + "location-minus": "\u{f609}", + "map-marker-minus": "\u{f609}", + "location-pen": "\u{f607}", + "map-marker-edit": "\u{f607}", + "location-pin": "\u{f041}", + "map-marker": "\u{f041}", + "location-pin-lock": "\u{e51f}", + "location-pin-slash": "\u{f60c}", + "map-marker-slash": "\u{f60c}", + "location-plus": "\u{f60a}", + "map-marker-plus": "\u{f60a}", + "location-question": "\u{f60b}", + "map-marker-question": "\u{f60b}", + "location-smile": "\u{f60d}", + "map-marker-smile": "\u{f60d}", + "location-xmark": "\u{f60e}", + "map-marker-times": "\u{f60e}", + "map-marker-xmark": "\u{f60e}", + "lock": "\u{f023}", + "lock-a": "\u{e422}", + "lock-hashtag": "\u{e423}", + "lock-keyhole": "\u{f30d}", + "lock-alt": "\u{f30d}", + "lock-keyhole-open": "\u{f3c2}", + "lock-open-alt": "\u{f3c2}", + "lock-open": "\u{f3c1}", + "locust": "\u{e520}", + "lollipop": "\u{e424}", + "lollypop": "\u{e424}", + "loveseat": "\u{f4cc}", + "couch-small": "\u{f4cc}", + "luchador-mask": "\u{f455}", + "luchador": "\u{f455}", + "mask-luchador": "\u{f455}", + "lungs": "\u{f604}", + "lungs-virus": "\u{e067}", + "lyft": "\u{f3c3}", + "m": "\u{4d}", + "mace": "\u{f6f8}", + "magento": "\u{f3c4}", + "magnet": "\u{f076}", + "magnifying-glass": "\u{f002}", + "search": "\u{f002}", + "magnifying-glass-arrow-right": "\u{e521}", + "magnifying-glass-arrows-rotate": "\u{e65e}", + "magnifying-glass-chart": "\u{e522}", + "magnifying-glass-dollar": "\u{f688}", + "search-dollar": "\u{f688}", + "magnifying-glass-location": "\u{f689}", + "search-location": "\u{f689}", + "magnifying-glass-minus": "\u{f010}", + "search-minus": "\u{f010}", + "magnifying-glass-music": "\u{e65f}", + "magnifying-glass-play": "\u{e660}", + "magnifying-glass-plus": "\u{f00e}", + "search-plus": "\u{f00e}", + "magnifying-glass-waveform": "\u{e661}", + "mailbox": "\u{f813}", + "mailbox-flag-up": "\u{e5bb}", + "mailchimp": "\u{f59e}", + "manat-sign": "\u{e1d5}", + "mandalorian": "\u{f50f}", + "mandolin": "\u{f6f9}", + "mango": "\u{e30f}", + "manhole": "\u{e1d6}", + "map": "\u{f279}", + "map-location": "\u{f59f}", + "map-marked": "\u{f59f}", + "map-location-dot": "\u{f5a0}", + "map-marked-alt": "\u{f5a0}", + "map-pin": "\u{f276}", + "markdown": "\u{f60f}", + "marker": "\u{f5a1}", + "mars": "\u{f222}", + "mars-and-venus": "\u{f224}", + "mars-and-venus-burst": "\u{e523}", + "mars-double": "\u{f227}", + "mars-stroke": "\u{f229}", + "mars-stroke-right": "\u{f22b}", + "mars-stroke-h": "\u{f22b}", + "mars-stroke-up": "\u{f22a}", + "mars-stroke-v": "\u{f22a}", + "martini-glass": "\u{f57b}", + "glass-martini-alt": "\u{f57b}", + "martini-glass-citrus": "\u{f561}", + "cocktail": "\u{f561}", + "martini-glass-empty": "\u{f000}", + "glass-martini": "\u{f000}", + "mask": "\u{f6fa}", + "mask-face": "\u{e1d7}", + "mask-snorkel": "\u{e3b7}", + "masks-theater": "\u{f630}", + "theater-masks": "\u{f630}", + "mask-ventilator": "\u{e524}", + "mastodon": "\u{f4f6}", + "mattress-pillow": "\u{e525}", + "maxcdn": "\u{f136}", + "maximize": "\u{f31e}", + "expand-arrows-alt": "\u{f31e}", + "mdb": "\u{f8ca}", + "meat": "\u{f814}", + "medal": "\u{f5a2}", + "medapps": "\u{f3c6}", + "medium": "\u{f23a}", + "medium-m": "\u{f23a}", + "medrt": "\u{f3c8}", + "meetup": "\u{f2e0}", + "megaphone": "\u{f675}", + "megaport": "\u{f5a3}", + "melon": "\u{e310}", + "melon-slice": "\u{e311}", + "memo": "\u{e1d8}", + "memo-circle-check": "\u{e1d9}", + "memo-circle-info": "\u{e49a}", + "memo-pad": "\u{e1da}", + "memory": "\u{f538}", + "mendeley": "\u{f7b3}", + "menorah": "\u{f676}", + "mercury": "\u{f223}", + "merge": "\u{e526}", + "message": "\u{f27a}", + "comment-alt": "\u{f27a}", + "message-arrow-down": "\u{e1db}", + "comment-alt-arrow-down": "\u{e1db}", + "message-arrow-up": "\u{e1dc}", + "comment-alt-arrow-up": "\u{e1dc}", + "message-arrow-up-right": "\u{e1dd}", + "message-bot": "\u{e3b8}", + "message-captions": "\u{e1de}", + "comment-alt-captions": "\u{e1de}", + "message-check": "\u{f4a2}", + "comment-alt-check": "\u{f4a2}", + "message-code": "\u{e1df}", + "message-dollar": "\u{f650}", + "comment-alt-dollar": "\u{f650}", + "message-dots": "\u{f4a3}", + "comment-alt-dots": "\u{f4a3}", + "messaging": "\u{f4a3}", + "message-exclamation": "\u{f4a5}", + "comment-alt-exclamation": "\u{f4a5}", + "message-heart": "\u{e5c9}", + "message-image": "\u{e1e0}", + "comment-alt-image": "\u{e1e0}", + "message-lines": "\u{f4a6}", + "comment-alt-lines": "\u{f4a6}", + "message-medical": "\u{f7f4}", + "comment-alt-medical": "\u{f7f4}", + "message-middle": "\u{e1e1}", + "comment-middle-alt": "\u{e1e1}", + "message-middle-top": "\u{e1e2}", + "comment-middle-top-alt": "\u{e1e2}", + "message-minus": "\u{f4a7}", + "comment-alt-minus": "\u{f4a7}", + "message-music": "\u{f8af}", + "comment-alt-music": "\u{f8af}", + "message-pen": "\u{f4a4}", + "comment-alt-edit": "\u{f4a4}", + "message-edit": "\u{f4a4}", + "message-plus": "\u{f4a8}", + "comment-alt-plus": "\u{f4a8}", + "message-question": "\u{e1e3}", + "message-quote": "\u{e1e4}", + "comment-alt-quote": "\u{e1e4}", + "messages": "\u{f4b6}", + "comments-alt": "\u{f4b6}", + "messages-dollar": "\u{f652}", + "comments-alt-dollar": "\u{f652}", + "message-slash": "\u{f4a9}", + "comment-alt-slash": "\u{f4a9}", + "message-smile": "\u{f4aa}", + "comment-alt-smile": "\u{f4aa}", + "message-sms": "\u{e1e5}", + "messages-question": "\u{e1e7}", + "message-text": "\u{e1e6}", + "comment-alt-text": "\u{e1e6}", + "message-xmark": "\u{f4ab}", + "comment-alt-times": "\u{f4ab}", + "message-times": "\u{f4ab}", + "meta": "\u{e49b}", + "meteor": "\u{f753}", + "meter": "\u{e1e8}", + "meter-bolt": "\u{e1e9}", + "meter-droplet": "\u{e1ea}", + "meter-fire": "\u{e1eb}", + "microblog": "\u{e01a}", + "microchip": "\u{f2db}", + "microchip-ai": "\u{e1ec}", + "microphone": "\u{f130}", + "microphone-lines": "\u{f3c9}", + "microphone-alt": "\u{f3c9}", + "microphone-lines-slash": "\u{f539}", + "microphone-alt-slash": "\u{f539}", + "microphone-slash": "\u{f131}", + "microphone-stand": "\u{f8cb}", + "microscope": "\u{f610}", + "microsoft": "\u{f3ca}", + "microwave": "\u{e01b}", + "mill-sign": "\u{e1ed}", + "minimize": "\u{f78c}", + "compress-arrows-alt": "\u{f78c}", + "mintbit": "\u{e62f}", + "minus": "\u{f068}", + "subtract": "\u{f068}", + "mistletoe": "\u{f7b4}", + "mitten": "\u{f7b5}", + "mix": "\u{f3cb}", + "mixcloud": "\u{f289}", + "mixer": "\u{e056}", + "mizuni": "\u{f3cc}", + "mobile": "\u{f3ce}", + "mobile-android": "\u{f3ce}", + "mobile-phone": "\u{f3ce}", + "mobile-button": "\u{f10b}", + "mobile-notch": "\u{e1ee}", + "mobile-iphone": "\u{e1ee}", + "mobile-retro": "\u{e527}", + "mobile-screen": "\u{f3cf}", + "mobile-android-alt": "\u{f3cf}", + "mobile-screen-button": "\u{f3cd}", + "mobile-alt": "\u{f3cd}", + "mobile-signal": "\u{e1ef}", + "mobile-signal-out": "\u{e1f0}", + "modx": "\u{f285}", + "monero": "\u{f3d0}", + "money-bill": "\u{f0d6}", + "money-bill-1": "\u{f3d1}", + "money-bill-alt": "\u{f3d1}", + "money-bill-1-wave": "\u{f53b}", + "money-bill-wave-alt": "\u{f53b}", + "money-bills": "\u{e1f3}", + "money-bill-simple": "\u{e1f1}", + "money-bill-simple-wave": "\u{e1f2}", + "money-bills-simple": "\u{e1f4}", + "money-bills-alt": "\u{e1f4}", + "money-bill-transfer": "\u{e528}", + "money-bill-trend-up": "\u{e529}", + "money-bill-wave": "\u{f53a}", + "money-bill-wheat": "\u{e52a}", + "money-check": "\u{f53c}", + "money-check-dollar": "\u{f53d}", + "money-check-alt": "\u{f53d}", + "money-check-dollar-pen": "\u{f873}", + "money-check-edit-alt": "\u{f873}", + "money-check-pen": "\u{f872}", + "money-check-edit": "\u{f872}", + "money-from-bracket": "\u{e312}", + "money-simple-from-bracket": "\u{e313}", + "monitor-waveform": "\u{f611}", + "monitor-heart-rate": "\u{f611}", + "monkey": "\u{f6fb}", + "monument": "\u{f5a6}", + "moon": "\u{f186}", + "moon-cloud": "\u{f754}", + "moon-over-sun": "\u{f74a}", + "eclipse-alt": "\u{f74a}", + "moon-stars": "\u{f755}", + "moped": "\u{e3b9}", + "mortar-pestle": "\u{f5a7}", + "mosque": "\u{f678}", + "mosquito": "\u{e52b}", + "mosquito-net": "\u{e52c}", + "motorcycle": "\u{f21c}", + "mound": "\u{e52d}", + "mountain": "\u{f6fc}", + "mountain-city": "\u{e52e}", + "mountains": "\u{f6fd}", + "mountain-sun": "\u{e52f}", + "mouse-field": "\u{e5a8}", + "mp3-player": "\u{f8ce}", + "mug": "\u{f874}", + "mug-hot": "\u{f7b6}", + "mug-marshmallows": "\u{f7b7}", + "mug-saucer": "\u{f0f4}", + "coffee": "\u{f0f4}", + "mug-tea": "\u{f875}", + "mug-tea-saucer": "\u{e1f5}", + "mushroom": "\u{e425}", + "music": "\u{f001}", + "music-magnifying-glass": "\u{e662}", + "music-note": "\u{f8cf}", + "music-alt": "\u{f8cf}", + "music-note-slash": "\u{f8d0}", + "music-alt-slash": "\u{f8d0}", + "music-slash": "\u{f8d1}", + "mustache": "\u{e5bc}", + "n": "\u{4e}", + "naira-sign": "\u{e1f6}", + "napster": "\u{f3d2}", + "narwhal": "\u{f6fe}", + "neos": "\u{f612}", + "nesting-dolls": "\u{e3ba}", + "network-wired": "\u{f6ff}", + "neuter": "\u{f22c}", + "newspaper": "\u{f1ea}", + "nfc": "\u{e1f7}", + "nfc-directional": "\u{e530}", + "nfc-lock": "\u{e1f8}", + "nfc-magnifying-glass": "\u{e1f9}", + "nfc-pen": "\u{e1fa}", + "nfc-signal": "\u{e1fb}", + "nfc-slash": "\u{e1fc}", + "nfc-symbol": "\u{e531}", + "nfc-trash": "\u{e1fd}", + "nimblr": "\u{f5a8}", + "node": "\u{f419}", + "node-js": "\u{f3d3}", + "nose": "\u{e5bd}", + "notdef": "\u{e1fe}", + "note": "\u{e1ff}", + "notebook": "\u{e201}", + "note-medical": "\u{e200}", + "not-equal": "\u{f53e}", + "notes": "\u{e202}", + "notes-medical": "\u{f481}", + "note-sticky": "\u{f249}", + "sticky-note": "\u{f249}", + "npm": "\u{f3d4}", + "ns8": "\u{f3d5}", + "nutritionix": "\u{f3d6}", + "o": "\u{4f}", + "object-exclude": "\u{e49c}", + "object-group": "\u{f247}", + "object-intersect": "\u{e49d}", + "objects-align-bottom": "\u{e3bb}", + "objects-align-center-horizontal": "\u{e3bc}", + "objects-align-center-vertical": "\u{e3bd}", + "objects-align-left": "\u{e3be}", + "objects-align-right": "\u{e3bf}", + "objects-align-top": "\u{e3c0}", + "objects-column": "\u{e3c1}", + "object-subtract": "\u{e49e}", + "object-ungroup": "\u{f248}", + "object-union": "\u{e49f}", + "octagon": "\u{f306}", + "octagon-check": "\u{e426}", + "octagon-divide": "\u{e203}", + "octagon-exclamation": "\u{e204}", + "octagon-minus": "\u{f308}", + "minus-octagon": "\u{f308}", + "octagon-plus": "\u{f301}", + "plus-octagon": "\u{f301}", + "octagon-xmark": "\u{f2f0}", + "times-octagon": "\u{f2f0}", + "xmark-octagon": "\u{f2f0}", + "octopus": "\u{e688}", + "octopus-deploy": "\u{e082}", + "odnoklassniki": "\u{f263}", + "odysee": "\u{e5c6}", + "oil-can": "\u{f613}", + "oil-can-drip": "\u{e205}", + "oil-temperature": "\u{f614}", + "oil-temp": "\u{f614}", + "oil-well": "\u{e532}", + "old-republic": "\u{f510}", + "olive": "\u{e316}", + "olive-branch": "\u{e317}", + "om": "\u{f679}", + "omega": "\u{f67a}", + "onion": "\u{e427}", + "opencart": "\u{f23d}", + "openid": "\u{f19b}", + "opensuse": "\u{e62b}", + "opera": "\u{f26a}", + "optin-monster": "\u{f23c}", + "option": "\u{e318}", + "orcid": "\u{f8d2}", + "ornament": "\u{f7b8}", + "osi": "\u{f41a}", + "otter": "\u{f700}", + "outdent": "\u{f03b}", + "dedent": "\u{f03b}", + "outlet": "\u{e01c}", + "oven": "\u{e01d}", + "overline": "\u{f876}", + "p": "\u{50}", + "padlet": "\u{e4a0}", + "page": "\u{e428}", + "page4": "\u{f3d7}", + "page-caret-down": "\u{e429}", + "file-caret-down": "\u{e429}", + "page-caret-up": "\u{e42a}", + "file-caret-up": "\u{e42a}", + "pagelines": "\u{f18c}", + "pager": "\u{f815}", + "paintbrush": "\u{f1fc}", + "paint-brush": "\u{f1fc}", + "paintbrush-fine": "\u{f5a9}", + "paint-brush-alt": "\u{f5a9}", + "paint-brush-fine": "\u{f5a9}", + "paintbrush-alt": "\u{f5a9}", + "paintbrush-pencil": "\u{e206}", + "paint-roller": "\u{f5aa}", + "palette": "\u{f53f}", + "palfed": "\u{f3d8}", + "pallet": "\u{f482}", + "pallet-box": "\u{e208}", + "pallet-boxes": "\u{f483}", + "palette-boxes": "\u{f483}", + "pallet-alt": "\u{f483}", + "pancakes": "\u{e42d}", + "panel-ews": "\u{e42e}", + "panel-fire": "\u{e42f}", + "pan-food": "\u{e42b}", + "pan-frying": "\u{e42c}", + "panorama": "\u{e209}", + "paperclip": "\u{f0c6}", + "paperclip-vertical": "\u{e3c2}", + "paper-plane": "\u{f1d8}", + "paper-plane-top": "\u{e20a}", + "paper-plane-alt": "\u{e20a}", + "send": "\u{e20a}", + "parachute-box": "\u{f4cd}", + "paragraph": "\u{f1dd}", + "paragraph-left": "\u{f878}", + "paragraph-rtl": "\u{f878}", + "party-bell": "\u{e31a}", + "party-horn": "\u{e31b}", + "passport": "\u{f5ab}", + "paste": "\u{f0ea}", + "file-clipboard": "\u{f0ea}", + "patreon": "\u{f3d9}", + "pause": "\u{f04c}", + "paw": "\u{f1b0}", + "paw-claws": "\u{f702}", + "paw-simple": "\u{f701}", + "paw-alt": "\u{f701}", + "paypal": "\u{f1ed}", + "peace": "\u{f67c}", + "peach": "\u{e20b}", + "peanut": "\u{e430}", + "peanuts": "\u{e431}", + "peapod": "\u{e31c}", + "pear": "\u{e20c}", + "pedestal": "\u{e20d}", + "pegasus": "\u{f703}", + "pen": "\u{f304}", + "pencil": "\u{f303}", + "pencil-alt": "\u{f303}", + "pencil-mechanical": "\u{e5ca}", + "pencil-slash": "\u{e215}", + "pen-circle": "\u{e20e}", + "pen-clip": "\u{f305}", + "pen-alt": "\u{f305}", + "pen-clip-slash": "\u{e20f}", + "pen-alt-slash": "\u{e20f}", + "pen-fancy": "\u{f5ac}", + "pen-fancy-slash": "\u{e210}", + "pen-field": "\u{e211}", + "pen-line": "\u{e212}", + "pen-nib": "\u{f5ad}", + "pen-nib-slash": "\u{e4a1}", + "pen-paintbrush": "\u{f618}", + "pencil-paintbrush": "\u{f618}", + "pen-ruler": "\u{f5ae}", + "pencil-ruler": "\u{f5ae}", + "pen-slash": "\u{e213}", + "pen-swirl": "\u{e214}", + "pen-to-square": "\u{f044}", + "edit": "\u{f044}", + "people": "\u{e216}", + "people-arrows": "\u{e068}", + "people-arrows-left-right": "\u{e068}", + "people-carry-box": "\u{f4ce}", + "people-carry": "\u{f4ce}", + "people-dress": "\u{e217}", + "people-dress-simple": "\u{e218}", + "people-group": "\u{e533}", + "people-line": "\u{e534}", + "people-pants": "\u{e219}", + "people-pants-simple": "\u{e21a}", + "people-pulling": "\u{e535}", + "people-robbery": "\u{e536}", + "people-roof": "\u{e537}", + "people-simple": "\u{e21b}", + "pepper": "\u{e432}", + "pepper-hot": "\u{f816}", + "perbyte": "\u{e083}", + "percent": "\u{25}", + "percentage": "\u{25}", + "period": "\u{2e}", + "periscope": "\u{f3da}", + "person": "\u{f183}", + "male": "\u{f183}", + "person-arrow-down-to-line": "\u{e538}", + "person-arrow-up-from-line": "\u{e539}", + "person-biking": "\u{f84a}", + "biking": "\u{f84a}", + "person-biking-mountain": "\u{f84b}", + "biking-mountain": "\u{f84b}", + "person-booth": "\u{f756}", + "person-breastfeeding": "\u{e53a}", + "person-burst": "\u{e53b}", + "person-cane": "\u{e53c}", + "person-carry-box": "\u{f4cf}", + "person-carry": "\u{f4cf}", + "person-chalkboard": "\u{e53d}", + "person-circle-check": "\u{e53e}", + "person-circle-exclamation": "\u{e53f}", + "person-circle-minus": "\u{e540}", + "person-circle-plus": "\u{e541}", + "person-circle-question": "\u{e542}", + "person-circle-xmark": "\u{e543}", + "person-digging": "\u{f85e}", + "digging": "\u{f85e}", + "person-dolly": "\u{f4d0}", + "person-dolly-empty": "\u{f4d1}", + "person-dots-from-line": "\u{f470}", + "diagnoses": "\u{f470}", + "person-dress": "\u{f182}", + "female": "\u{f182}", + "person-dress-burst": "\u{e544}", + "person-dress-fairy": "\u{e607}", + "person-dress-simple": "\u{e21c}", + "person-drowning": "\u{e545}", + "person-fairy": "\u{e608}", + "person-falling": "\u{e546}", + "person-falling-burst": "\u{e547}", + "person-from-portal": "\u{e023}", + "portal-exit": "\u{e023}", + "person-half-dress": "\u{e548}", + "person-harassing": "\u{e549}", + "person-hiking": "\u{f6ec}", + "hiking": "\u{f6ec}", + "person-military-pointing": "\u{e54a}", + "person-military-rifle": "\u{e54b}", + "person-military-to-person": "\u{e54c}", + "person-pinball": "\u{e21d}", + "person-praying": "\u{f683}", + "pray": "\u{f683}", + "person-pregnant": "\u{e31e}", + "person-rays": "\u{e54d}", + "person-rifle": "\u{e54e}", + "person-running": "\u{f70c}", + "running": "\u{f70c}", + "person-running-fast": "\u{e5ff}", + "person-seat": "\u{e21e}", + "person-seat-reclined": "\u{e21f}", + "person-shelter": "\u{e54f}", + "person-sign": "\u{f757}", + "person-simple": "\u{e220}", + "person-skating": "\u{f7c5}", + "skating": "\u{f7c5}", + "person-skiing": "\u{f7c9}", + "skiing": "\u{f7c9}", + "person-skiing-nordic": "\u{f7ca}", + "skiing-nordic": "\u{f7ca}", + "person-ski-jumping": "\u{f7c7}", + "ski-jump": "\u{f7c7}", + "person-ski-lift": "\u{f7c8}", + "ski-lift": "\u{f7c8}", + "person-sledding": "\u{f7cb}", + "sledding": "\u{f7cb}", + "person-snowboarding": "\u{f7ce}", + "snowboarding": "\u{f7ce}", + "person-snowmobiling": "\u{f7d1}", + "snowmobile": "\u{f7d1}", + "person-swimming": "\u{f5c4}", + "swimmer": "\u{f5c4}", + "person-through-window": "\u{e5a9}", + "person-to-door": "\u{e433}", + "person-to-portal": "\u{e022}", + "portal-enter": "\u{e022}", + "person-walking": "\u{f554}", + "walking": "\u{f554}", + "person-walking-arrow-loop-left": "\u{e551}", + "person-walking-arrow-right": "\u{e552}", + "person-walking-dashed-line-arrow-right": "\u{e553}", + "person-walking-luggage": "\u{e554}", + "person-walking-with-cane": "\u{f29d}", + "blind": "\u{f29d}", + "peseta-sign": "\u{e221}", + "peso-sign": "\u{e222}", + "phabricator": "\u{f3db}", + "phoenix-framework": "\u{f3dc}", + "phoenix-squadron": "\u{f511}", + "phone": "\u{f095}", + "phone-arrow-down-left": "\u{e223}", + "phone-arrow-down": "\u{e223}", + "phone-incoming": "\u{e223}", + "phone-arrow-right": "\u{e5be}", + "phone-arrow-up-right": "\u{e224}", + "phone-arrow-up": "\u{e224}", + "phone-outgoing": "\u{e224}", + "phone-flip": "\u{f879}", + "phone-alt": "\u{f879}", + "phone-hangup": "\u{e225}", + "phone-intercom": "\u{e434}", + "phone-missed": "\u{e226}", + "phone-office": "\u{f67d}", + "phone-plus": "\u{f4d2}", + "phone-rotary": "\u{f8d3}", + "phone-slash": "\u{f3dd}", + "phone-volume": "\u{f2a0}", + "volume-control-phone": "\u{f2a0}", + "phone-xmark": "\u{e227}", + "photo-film": "\u{f87c}", + "photo-video": "\u{f87c}", + "photo-film-music": "\u{e228}", + "php": "\u{f457}", + "pi": "\u{f67e}", + "piano": "\u{f8d4}", + "piano-keyboard": "\u{f8d5}", + "pickaxe": "\u{e5bf}", + "pickleball": "\u{e435}", + "pie": "\u{f705}", + "pied-piper": "\u{f2ae}", + "pied-piper-alt": "\u{f1a8}", + "pied-piper-hat": "\u{f4e5}", + "pied-piper-pp": "\u{f1a7}", + "pig": "\u{f706}", + "piggy-bank": "\u{f4d3}", + "pills": "\u{f484}", + "pinata": "\u{e3c3}", + "pinball": "\u{e229}", + "pineapple": "\u{e31f}", + "pinterest": "\u{f0d2}", + "pinterest-p": "\u{f231}", + "pipe": "\u{7c}", + "pipe-circle-check": "\u{e436}", + "pipe-collar": "\u{e437}", + "pipe-section": "\u{e438}", + "pipe-smoking": "\u{e3c4}", + "pipe-valve": "\u{e439}", + "pix": "\u{e43a}", + "pixiv": "\u{e640}", + "pizza": "\u{f817}", + "pizza-slice": "\u{f818}", + "place-of-worship": "\u{f67f}", + "plane": "\u{f072}", + "plane-arrival": "\u{f5af}", + "plane-circle-check": "\u{e555}", + "plane-circle-exclamation": "\u{e556}", + "plane-circle-xmark": "\u{e557}", + "plane-departure": "\u{f5b0}", + "plane-engines": "\u{f3de}", + "plane-alt": "\u{f3de}", + "plane-lock": "\u{e558}", + "plane-prop": "\u{e22b}", + "plane-slash": "\u{e069}", + "plane-tail": "\u{e22c}", + "planet-moon": "\u{e01f}", + "planet-ringed": "\u{e020}", + "plane-up": "\u{e22d}", + "plane-up-slash": "\u{e22e}", + "plant-wilt": "\u{e5aa}", + "plate-utensils": "\u{e43b}", + "plate-wheat": "\u{e55a}", + "play": "\u{f04b}", + "play-pause": "\u{e22f}", + "playstation": "\u{f3df}", + "plug": "\u{f1e6}", + "plug-circle-bolt": "\u{e55b}", + "plug-circle-check": "\u{e55c}", + "plug-circle-exclamation": "\u{e55d}", + "plug-circle-minus": "\u{e55e}", + "plug-circle-plus": "\u{e55f}", + "plug-circle-xmark": "\u{e560}", + "plus": "\u{2b}", + "add": "\u{2b}", + "plus-large": "\u{e59e}", + "plus-minus": "\u{e43c}", + "podcast": "\u{f2ce}", + "podium": "\u{f680}", + "podium-star": "\u{f758}", + "police-box": "\u{e021}", + "poll-people": "\u{f759}", + "pompebled": "\u{e43d}", + "poo": "\u{f2fe}", + "pool-8-ball": "\u{e3c5}", + "poop": "\u{f619}", + "poo-storm": "\u{f75a}", + "poo-bolt": "\u{f75a}", + "popcorn": "\u{f819}", + "popsicle": "\u{e43e}", + "potato": "\u{e440}", + "pot-food": "\u{e43f}", + "power-off": "\u{f011}", + "prescription": "\u{f5b1}", + "prescription-bottle": "\u{f485}", + "prescription-bottle-medical": "\u{f486}", + "prescription-bottle-alt": "\u{f486}", + "prescription-bottle-pill": "\u{e5c0}", + "presentation-screen": "\u{f685}", + "presentation": "\u{f685}", + "pretzel": "\u{e441}", + "print": "\u{f02f}", + "print-magnifying-glass": "\u{f81a}", + "print-search": "\u{f81a}", + "print-slash": "\u{f686}", + "product-hunt": "\u{f288}", + "projector": "\u{f8d6}", + "pump": "\u{e442}", + "pumpkin": "\u{f707}", + "pump-medical": "\u{e06a}", + "pump-soap": "\u{e06b}", + "pushed": "\u{f3e1}", + "puzzle": "\u{e443}", + "puzzle-piece": "\u{f12e}", + "puzzle-piece-simple": "\u{e231}", + "puzzle-piece-alt": "\u{e231}", + "python": "\u{f3e2}", + "q": "\u{51}", + "qq": "\u{f1d6}", + "qrcode": "\u{f029}", + "question": "\u{3f}", + "quinscape": "\u{f459}", + "quora": "\u{f2c4}", + "quote-left": "\u{f10d}", + "quote-left-alt": "\u{f10d}", + "quote-right": "\u{f10e}", + "quote-right-alt": "\u{f10e}", + "quotes": "\u{e234}", + "r": "\u{52}", + "rabbit": "\u{f708}", + "rabbit-running": "\u{f709}", + "rabbit-fast": "\u{f709}", + "raccoon": "\u{e613}", + "racquet": "\u{f45a}", + "radar": "\u{e024}", + "radiation": "\u{f7b9}", + "radio": "\u{f8d7}", + "radio-tuner": "\u{f8d8}", + "radio-alt": "\u{f8d8}", + "rainbow": "\u{f75b}", + "raindrops": "\u{f75c}", + "ram": "\u{f70a}", + "ramp-loading": "\u{f4d4}", + "ranking-star": "\u{e561}", + "raspberry-pi": "\u{f7bb}", + "ravelry": "\u{f2d9}", + "raygun": "\u{e025}", + "react": "\u{f41b}", + "reacteurope": "\u{f75d}", + "readme": "\u{f4d5}", + "rebel": "\u{f1d0}", + "receipt": "\u{f543}", + "record-vinyl": "\u{f8d9}", + "rectangle": "\u{f2fa}", + "rectangle-landscape": "\u{f2fa}", + "rectangle-ad": "\u{f641}", + "ad": "\u{f641}", + "rectangle-barcode": "\u{f463}", + "barcode-alt": "\u{f463}", + "rectangle-code": "\u{e322}", + "rectangle-history": "\u{e4a2}", + "rectangle-history-circle-plus": "\u{e4a3}", + "rectangle-history-circle-user": "\u{e4a4}", + "rectangle-list": "\u{f022}", + "list-alt": "\u{f022}", + "rectangle-pro": "\u{e235}", + "pro": "\u{e235}", + "rectangles-mixed": "\u{e323}", + "rectangle-terminal": "\u{e236}", + "rectangle-vertical": "\u{f2fb}", + "rectangle-portrait": "\u{f2fb}", + "rectangle-vertical-history": "\u{e237}", + "rectangle-wide": "\u{f2fc}", + "rectangle-xmark": "\u{f410}", + "rectangle-times": "\u{f410}", + "times-rectangle": "\u{f410}", + "window-close": "\u{f410}", + "recycle": "\u{f1b8}", + "reddit": "\u{f1a1}", + "reddit-alien": "\u{f281}", + "redhat": "\u{f7bc}", + "red-river": "\u{f3e3}", + "reel": "\u{e238}", + "reflect-both": "\u{e66f}", + "reflect-horizontal": "\u{e664}", + "reflect-vertical": "\u{e665}", + "refrigerator": "\u{e026}", + "registered": "\u{f25d}", + "renren": "\u{f18b}", + "repeat": "\u{f363}", + "repeat-1": "\u{f365}", + "reply": "\u{f3e5}", + "mail-reply": "\u{f3e5}", + "reply-all": "\u{f122}", + "mail-reply-all": "\u{f122}", + "reply-clock": "\u{e239}", + "reply-time": "\u{e239}", + "replyd": "\u{f3e6}", + "republican": "\u{f75e}", + "researchgate": "\u{f4f8}", + "resolving": "\u{f3e7}", + "restroom": "\u{f7bd}", + "restroom-simple": "\u{e23a}", + "retweet": "\u{f079}", + "rev": "\u{f5b2}", + "rhombus": "\u{e23b}", + "ribbon": "\u{f4d6}", + "right": "\u{f356}", + "arrow-alt-right": "\u{f356}", + "right-from-bracket": "\u{f2f5}", + "sign-out-alt": "\u{f2f5}", + "right-from-line": "\u{f347}", + "arrow-alt-from-left": "\u{f347}", + "right-left": "\u{f362}", + "exchange-alt": "\u{f362}", + "right-left-large": "\u{e5e1}", + "right-long": "\u{f30b}", + "long-arrow-alt-right": "\u{f30b}", + "right-long-to-line": "\u{e444}", + "right-to-bracket": "\u{f2f6}", + "sign-in-alt": "\u{f2f6}", + "right-to-line": "\u{f34c}", + "arrow-alt-to-right": "\u{f34c}", + "ring": "\u{f70b}", + "ring-diamond": "\u{e5ab}", + "rings-wedding": "\u{f81b}", + "road": "\u{f018}", + "road-barrier": "\u{e562}", + "road-bridge": "\u{e563}", + "road-circle-check": "\u{e564}", + "road-circle-exclamation": "\u{e565}", + "road-circle-xmark": "\u{e566}", + "road-lock": "\u{e567}", + "road-spikes": "\u{e568}", + "robot": "\u{f544}", + "robot-astromech": "\u{e2d2}", + "rocket": "\u{f135}", + "rocketchat": "\u{f3e8}", + "rocket-launch": "\u{e027}", + "rockrms": "\u{f3e9}", + "roller-coaster": "\u{e324}", + "rotate": "\u{f2f1}", + "sync-alt": "\u{f2f1}", + "rotate-exclamation": "\u{e23c}", + "rotate-left": "\u{f2ea}", + "rotate-back": "\u{f2ea}", + "rotate-backward": "\u{f2ea}", + "undo-alt": "\u{f2ea}", + "rotate-reverse": "\u{e631}", + "rotate-right": "\u{f2f9}", + "redo-alt": "\u{f2f9}", + "rotate-forward": "\u{f2f9}", + "route": "\u{f4d7}", + "route-highway": "\u{f61a}", + "route-interstate": "\u{f61b}", + "router": "\u{f8da}", + "r-project": "\u{f4f7}", + "rss": "\u{f09e}", + "feed": "\u{f09e}", + "ruble-sign": "\u{f158}", + "rouble": "\u{f158}", + "rub": "\u{f158}", + "ruble": "\u{f158}", + "rug": "\u{e569}", + "rugby-ball": "\u{e3c6}", + "ruler": "\u{f545}", + "ruler-combined": "\u{f546}", + "ruler-horizontal": "\u{f547}", + "ruler-triangle": "\u{f61c}", + "ruler-vertical": "\u{f548}", + "rupee-sign": "\u{f156}", + "rupee": "\u{f156}", + "rupiah-sign": "\u{e23d}", + "rust": "\u{e07a}", + "rv": "\u{f7be}", + "s": "\u{53}", + "sack": "\u{f81c}", + "sack-dollar": "\u{f81d}", + "sack-xmark": "\u{e56a}", + "safari": "\u{f267}", + "sailboat": "\u{e445}", + "salad": "\u{f81e}", + "bowl-salad": "\u{f81e}", + "salesforce": "\u{f83b}", + "salt-shaker": "\u{e446}", + "sandwich": "\u{f81f}", + "sass": "\u{f41e}", + "satellite": "\u{f7bf}", + "satellite-dish": "\u{f7c0}", + "sausage": "\u{f820}", + "saxophone": "\u{f8dc}", + "saxophone-fire": "\u{f8db}", + "sax-hot": "\u{f8db}", + "scale-balanced": "\u{f24e}", + "balance-scale": "\u{f24e}", + "scale-unbalanced": "\u{f515}", + "balance-scale-left": "\u{f515}", + "scale-unbalanced-flip": "\u{f516}", + "balance-scale-right": "\u{f516}", + "scalpel": "\u{f61d}", + "scalpel-line-dashed": "\u{f61e}", + "scalpel-path": "\u{f61e}", + "scanner-gun": "\u{f488}", + "scanner": "\u{f488}", + "scanner-image": "\u{f8f3}", + "scanner-keyboard": "\u{f489}", + "scanner-touchscreen": "\u{f48a}", + "scarecrow": "\u{f70d}", + "scarf": "\u{f7c1}", + "schlix": "\u{f3ea}", + "school": "\u{f549}", + "school-circle-check": "\u{e56b}", + "school-circle-exclamation": "\u{e56c}", + "school-circle-xmark": "\u{e56d}", + "school-flag": "\u{e56e}", + "school-lock": "\u{e56f}", + "scissors": "\u{f0c4}", + "cut": "\u{f0c4}", + "screencast": "\u{e23e}", + "screenpal": "\u{e570}", + "screen-users": "\u{f63d}", + "users-class": "\u{f63d}", + "screwdriver": "\u{f54a}", + "screwdriver-wrench": "\u{f7d9}", + "tools": "\u{f7d9}", + "scribble": "\u{e23f}", + "scribd": "\u{f28a}", + "scroll": "\u{f70e}", + "scroll-old": "\u{f70f}", + "scroll-torah": "\u{f6a0}", + "torah": "\u{f6a0}", + "scrubber": "\u{f2f8}", + "scythe": "\u{f710}", + "sd-card": "\u{f7c2}", + "sd-cards": "\u{e240}", + "seal": "\u{e241}", + "seal-exclamation": "\u{e242}", + "seal-question": "\u{e243}", + "searchengin": "\u{f3eb}", + "seat-airline": "\u{e244}", + "section": "\u{e447}", + "seedling": "\u{f4d8}", + "sprout": "\u{f4d8}", + "sellcast": "\u{f2da}", + "sellsy": "\u{f213}", + "semicolon": "\u{3b}", + "send-back": "\u{f87e}", + "send-backward": "\u{f87f}", + "sensor": "\u{e028}", + "sensor-cloud": "\u{e02c}", + "sensor-smoke": "\u{e02c}", + "sensor-fire": "\u{e02a}", + "sensor-on": "\u{e02b}", + "sensor-triangle-exclamation": "\u{e029}", + "sensor-alert": "\u{e029}", + "server": "\u{f233}", + "servicestack": "\u{f3ec}", + "shapes": "\u{f61f}", + "triangle-circle-square": "\u{f61f}", + "share": "\u{f064}", + "mail-forward": "\u{f064}", + "share-all": "\u{f367}", + "share-from-square": "\u{f14d}", + "share-square": "\u{f14d}", + "share-nodes": "\u{f1e0}", + "share-alt": "\u{f1e0}", + "sheep": "\u{f711}", + "sheet-plastic": "\u{e571}", + "shekel-sign": "\u{f20b}", + "ils": "\u{f20b}", + "shekel": "\u{f20b}", + "sheqel": "\u{f20b}", + "sheqel-sign": "\u{f20b}", + "shelves": "\u{f480}", + "inventory": "\u{f480}", + "shelves-empty": "\u{e246}", + "shield": "\u{f132}", + "shield-blank": "\u{f132}", + "shield-cat": "\u{e572}", + "shield-check": "\u{f2f7}", + "shield-cross": "\u{f712}", + "shield-dog": "\u{e573}", + "shield-exclamation": "\u{e247}", + "shield-halved": "\u{f3ed}", + "shield-alt": "\u{f3ed}", + "shield-heart": "\u{e574}", + "shield-keyhole": "\u{e248}", + "shield-minus": "\u{e249}", + "shield-plus": "\u{e24a}", + "shield-quartered": "\u{e575}", + "shield-slash": "\u{e24b}", + "shield-virus": "\u{e06c}", + "shield-xmark": "\u{e24c}", + "shield-times": "\u{e24c}", + "ship": "\u{f21a}", + "shirt": "\u{f553}", + "t-shirt": "\u{f553}", + "tshirt": "\u{f553}", + "shirt-long-sleeve": "\u{e3c7}", + "shirt-running": "\u{e3c8}", + "shirtsinbulk": "\u{f214}", + "shirt-tank-top": "\u{e3c9}", + "shish-kebab": "\u{f821}", + "shoelace": "\u{e60c}", + "shoe-prints": "\u{f54b}", + "shop": "\u{f54f}", + "store-alt": "\u{f54f}", + "shopify": "\u{e057}", + "shop-lock": "\u{e4a5}", + "shop-slash": "\u{e070}", + "store-alt-slash": "\u{e070}", + "shopware": "\u{f5b5}", + "shovel": "\u{f713}", + "shovel-snow": "\u{f7c3}", + "shower": "\u{f2cc}", + "shower-down": "\u{e24d}", + "shower-alt": "\u{e24d}", + "shredder": "\u{f68a}", + "shrimp": "\u{e448}", + "shuffle": "\u{f074}", + "random": "\u{f074}", + "shutters": "\u{e449}", + "shuttlecock": "\u{f45b}", + "shuttle-space": "\u{f197}", + "space-shuttle": "\u{f197}", + "sickle": "\u{f822}", + "sidebar": "\u{e24e}", + "sidebar-flip": "\u{e24f}", + "sigma": "\u{f68b}", + "signal": "\u{f012}", + "signal-5": "\u{f012}", + "signal-perfect": "\u{f012}", + "signal-bars": "\u{f690}", + "signal-alt": "\u{f690}", + "signal-alt-4": "\u{f690}", + "signal-bars-strong": "\u{f690}", + "signal-bars-fair": "\u{f692}", + "signal-alt-2": "\u{f692}", + "signal-bars-good": "\u{f693}", + "signal-alt-3": "\u{f693}", + "signal-bars-slash": "\u{f694}", + "signal-alt-slash": "\u{f694}", + "signal-bars-weak": "\u{f691}", + "signal-alt-1": "\u{f691}", + "signal-fair": "\u{f68d}", + "signal-2": "\u{f68d}", + "signal-good": "\u{f68e}", + "signal-3": "\u{f68e}", + "signal-messenger": "\u{e663}", + "signal-slash": "\u{f695}", + "signal-stream": "\u{f8dd}", + "signal-stream-slash": "\u{e250}", + "signal-strong": "\u{f68f}", + "signal-4": "\u{f68f}", + "signal-weak": "\u{f68c}", + "signal-1": "\u{f68c}", + "signature": "\u{f5b7}", + "signature-lock": "\u{e3ca}", + "signature-slash": "\u{e3cb}", + "sign-hanging": "\u{f4d9}", + "sign": "\u{f4d9}", + "sign-post": "\u{e624}", + "sign-posts": "\u{e625}", + "sign-posts-wrench": "\u{e626}", + "signs-post": "\u{f277}", + "map-signs": "\u{f277}", + "sim-card": "\u{f7c4}", + "sim-cards": "\u{e251}", + "simplybuilt": "\u{f215}", + "sink": "\u{e06d}", + "siren": "\u{e02d}", + "siren-on": "\u{e02e}", + "sistrix": "\u{f3ee}", + "sitemap": "\u{f0e8}", + "sith": "\u{f512}", + "sitrox": "\u{e44a}", + "skeleton": "\u{f620}", + "skeleton-ribs": "\u{e5cb}", + "sketch": "\u{f7c6}", + "ski-boot": "\u{e3cc}", + "ski-boot-ski": "\u{e3cd}", + "skull": "\u{f54c}", + "skull-cow": "\u{f8de}", + "skull-crossbones": "\u{f714}", + "skyatlas": "\u{f216}", + "skype": "\u{f17e}", + "slack": "\u{f198}", + "slack-hash": "\u{f198}", + "slash": "\u{f715}", + "slash-back": "\u{5c}", + "slash-forward": "\u{2f}", + "sleigh": "\u{f7cc}", + "slider": "\u{e252}", + "sliders": "\u{f1de}", + "sliders-h": "\u{f1de}", + "sliders-simple": "\u{e253}", + "sliders-up": "\u{f3f1}", + "sliders-v": "\u{f3f1}", + "slideshare": "\u{f1e7}", + "slot-machine": "\u{e3ce}", + "smog": "\u{f75f}", + "smoke": "\u{f760}", + "smoking": "\u{f48d}", + "snake": "\u{f716}", + "snapchat": "\u{f2ab}", + "snapchat-ghost": "\u{f2ab}", + "snooze": "\u{f880}", + "zzz": "\u{f880}", + "snow-blowing": "\u{f761}", + "snowflake": "\u{f2dc}", + "snowflake-droplets": "\u{e5c1}", + "snowflakes": "\u{f7cf}", + "snowman": "\u{f7d0}", + "snowman-head": "\u{f79b}", + "frosty-head": "\u{f79b}", + "snowplow": "\u{f7d2}", + "soap": "\u{e06e}", + "socks": "\u{f696}", + "soft-serve": "\u{e400}", + "creemee": "\u{e400}", + "solar-panel": "\u{f5ba}", + "solar-system": "\u{e02f}", + "sort": "\u{f0dc}", + "unsorted": "\u{f0dc}", + "sort-down": "\u{f0dd}", + "sort-desc": "\u{f0dd}", + "sort-up": "\u{f0de}", + "sort-asc": "\u{f0de}", + "soundcloud": "\u{f1be}", + "sourcetree": "\u{f7d3}", + "spa": "\u{f5bb}", + "space-awesome": "\u{e5ac}", + "space-station-moon": "\u{e033}", + "space-station-moon-construction": "\u{e034}", + "space-station-moon-alt": "\u{e034}", + "spade": "\u{f2f4}", + "spaghetti-monster-flying": "\u{f67b}", + "pastafarianism": "\u{f67b}", + "sparkle": "\u{e5d6}", + "sparkles": "\u{f890}", + "speakap": "\u{f3f3}", + "speaker": "\u{f8df}", + "speaker-deck": "\u{f83c}", + "speakers": "\u{f8e0}", + "spell-check": "\u{f891}", + "spider": "\u{f717}", + "spider-black-widow": "\u{f718}", + "spider-web": "\u{f719}", + "spinner": "\u{f110}", + "spinner-scale": "\u{e62a}", + "spinner-third": "\u{f3f4}", + "split": "\u{e254}", + "splotch": "\u{f5bc}", + "spoon": "\u{f2e5}", + "utensil-spoon": "\u{f2e5}", + "sportsball": "\u{e44b}", + "spotify": "\u{f1bc}", + "spray-can": "\u{f5bd}", + "spray-can-sparkles": "\u{f5d0}", + "air-freshener": "\u{f5d0}", + "sprinkler": "\u{e035}", + "sprinkler-ceiling": "\u{e44c}", + "square": "\u{f0c8}", + "square-0": "\u{e255}", + "square-1": "\u{e256}", + "square-2": "\u{e257}", + "square-3": "\u{e258}", + "square-4": "\u{e259}", + "square-5": "\u{e25a}", + "square-6": "\u{e25b}", + "square-7": "\u{e25c}", + "square-8": "\u{e25d}", + "square-9": "\u{e25e}", + "square-a": "\u{e25f}", + "square-a-lock": "\u{e44d}", + "square-ampersand": "\u{e260}", + "square-arrow-down": "\u{f339}", + "arrow-square-down": "\u{f339}", + "square-arrow-down-left": "\u{e261}", + "square-arrow-down-right": "\u{e262}", + "square-arrow-left": "\u{f33a}", + "arrow-square-left": "\u{f33a}", + "square-arrow-right": "\u{f33b}", + "arrow-square-right": "\u{f33b}", + "square-arrow-up": "\u{f33c}", + "arrow-square-up": "\u{f33c}", + "square-arrow-up-left": "\u{e263}", + "square-arrow-up-right": "\u{f14c}", + "external-link-square": "\u{f14c}", + "square-b": "\u{e264}", + "square-behance": "\u{f1b5}", + "behance-square": "\u{f1b5}", + "square-bolt": "\u{e265}", + "square-c": "\u{e266}", + "square-caret-down": "\u{f150}", + "caret-square-down": "\u{f150}", + "square-caret-left": "\u{f191}", + "caret-square-left": "\u{f191}", + "square-caret-right": "\u{f152}", + "caret-square-right": "\u{f152}", + "square-caret-up": "\u{f151}", + "caret-square-up": "\u{f151}", + "square-check": "\u{f14a}", + "check-square": "\u{f14a}", + "square-chevron-down": "\u{f329}", + "chevron-square-down": "\u{f329}", + "square-chevron-left": "\u{f32a}", + "chevron-square-left": "\u{f32a}", + "square-chevron-right": "\u{f32b}", + "chevron-square-right": "\u{f32b}", + "square-chevron-up": "\u{f32c}", + "chevron-square-up": "\u{f32c}", + "square-code": "\u{e267}", + "square-d": "\u{e268}", + "square-dashed": "\u{e269}", + "square-dashed-circle-plus": "\u{e5c2}", + "square-divide": "\u{e26a}", + "square-dollar": "\u{f2e9}", + "dollar-square": "\u{f2e9}", + "usd-square": "\u{f2e9}", + "square-down": "\u{f350}", + "arrow-alt-square-down": "\u{f350}", + "square-down-left": "\u{e26b}", + "square-down-right": "\u{e26c}", + "square-dribbble": "\u{f397}", + "dribbble-square": "\u{f397}", + "square-e": "\u{e26d}", + "square-ellipsis": "\u{e26e}", + "square-ellipsis-vertical": "\u{e26f}", + "square-envelope": "\u{f199}", + "envelope-square": "\u{f199}", + "square-exclamation": "\u{f321}", + "exclamation-square": "\u{f321}", + "square-f": "\u{e270}", + "square-facebook": "\u{f082}", + "facebook-square": "\u{f082}", + "square-font-awesome": "\u{e5ad}", + "square-font-awesome-stroke": "\u{f35c}", + "font-awesome-alt": "\u{f35c}", + "square-fragile": "\u{f49b}", + "box-fragile": "\u{f49b}", + "square-wine-glass-crack": "\u{f49b}", + "square-full": "\u{f45c}", + "square-g": "\u{e271}", + "square-git": "\u{f1d2}", + "git-square": "\u{f1d2}", + "square-github": "\u{f092}", + "github-square": "\u{f092}", + "square-gitlab": "\u{e5ae}", + "gitlab-square": "\u{e5ae}", + "square-google-plus": "\u{f0d4}", + "google-plus-square": "\u{f0d4}", + "square-h": "\u{f0fd}", + "h-square": "\u{f0fd}", + "square-hacker-news": "\u{f3af}", + "hacker-news-square": "\u{f3af}", + "square-heart": "\u{f4c8}", + "heart-square": "\u{f4c8}", + "square-i": "\u{e272}", + "square-info": "\u{f30f}", + "info-square": "\u{f30f}", + "square-instagram": "\u{e055}", + "instagram-square": "\u{e055}", + "square-j": "\u{e273}", + "square-js": "\u{f3b9}", + "js-square": "\u{f3b9}", + "square-k": "\u{e274}", + "square-kanban": "\u{e488}", + "square-l": "\u{e275}", + "square-lastfm": "\u{f203}", + "lastfm-square": "\u{f203}", + "square-left": "\u{f351}", + "arrow-alt-square-left": "\u{f351}", + "square-letterboxd": "\u{e62e}", + "square-list": "\u{e489}", + "square-m": "\u{e276}", + "square-minus": "\u{f146}", + "minus-square": "\u{f146}", + "square-n": "\u{e277}", + "square-nfi": "\u{e576}", + "square-o": "\u{e278}", + "square-odnoklassniki": "\u{f264}", + "odnoklassniki-square": "\u{f264}", + "square-p": "\u{e279}", + "square-parking": "\u{f540}", + "parking": "\u{f540}", + "square-parking-slash": "\u{f617}", + "parking-slash": "\u{f617}", + "square-pen": "\u{f14b}", + "pen-square": "\u{f14b}", + "pencil-square": "\u{f14b}", + "square-person-confined": "\u{e577}", + "square-phone": "\u{f098}", + "phone-square": "\u{f098}", + "square-phone-flip": "\u{f87b}", + "phone-square-alt": "\u{f87b}", + "square-phone-hangup": "\u{e27a}", + "phone-square-down": "\u{e27a}", + "square-pied-piper": "\u{e01e}", + "pied-piper-square": "\u{e01e}", + "square-pinterest": "\u{f0d3}", + "pinterest-square": "\u{f0d3}", + "square-plus": "\u{f0fe}", + "plus-square": "\u{f0fe}", + "square-poll-horizontal": "\u{f682}", + "poll-h": "\u{f682}", + "square-poll-vertical": "\u{f681}", + "poll": "\u{f681}", + "square-q": "\u{e27b}", + "square-quarters": "\u{e44e}", + "square-question": "\u{f2fd}", + "question-square": "\u{f2fd}", + "square-quote": "\u{e329}", + "square-r": "\u{e27c}", + "square-reddit": "\u{f1a2}", + "reddit-square": "\u{f1a2}", + "square-right": "\u{f352}", + "arrow-alt-square-right": "\u{f352}", + "square-ring": "\u{e44f}", + "square-root": "\u{f697}", + "square-root-variable": "\u{f698}", + "square-root-alt": "\u{f698}", + "square-rss": "\u{f143}", + "rss-square": "\u{f143}", + "square-s": "\u{e27d}", + "square-share-nodes": "\u{f1e1}", + "share-alt-square": "\u{f1e1}", + "square-sliders": "\u{f3f0}", + "sliders-h-square": "\u{f3f0}", + "square-sliders-vertical": "\u{f3f2}", + "sliders-v-square": "\u{f3f2}", + "square-small": "\u{e27e}", + "square-snapchat": "\u{f2ad}", + "snapchat-square": "\u{f2ad}", + "squarespace": "\u{f5be}", + "square-star": "\u{e27f}", + "square-steam": "\u{f1b7}", + "steam-square": "\u{f1b7}", + "square-t": "\u{e280}", + "square-terminal": "\u{e32a}", + "square-this-way-up": "\u{f49f}", + "box-up": "\u{f49f}", + "square-threads": "\u{e619}", + "square-tumblr": "\u{f174}", + "tumblr-square": "\u{f174}", + "square-twitter": "\u{f081}", + "twitter-square": "\u{f081}", + "square-u": "\u{e281}", + "square-up": "\u{f353}", + "arrow-alt-square-up": "\u{f353}", + "square-up-left": "\u{e282}", + "square-up-right": "\u{f360}", + "external-link-square-alt": "\u{f360}", + "square-upwork": "\u{e67c}", + "square-user": "\u{e283}", + "square-v": "\u{e284}", + "square-viadeo": "\u{f2aa}", + "viadeo-square": "\u{f2aa}", + "square-vimeo": "\u{f194}", + "vimeo-square": "\u{f194}", + "square-virus": "\u{e578}", + "square-w": "\u{e285}", + "square-web-awesome": "\u{e683}", + "square-web-awesome-stroke": "\u{e684}", + "square-whatsapp": "\u{f40c}", + "whatsapp-square": "\u{f40c}", + "square-x": "\u{e286}", + "square-xing": "\u{f169}", + "xing-square": "\u{f169}", + "square-xmark": "\u{f2d3}", + "times-square": "\u{f2d3}", + "xmark-square": "\u{f2d3}", + "square-x-twitter": "\u{e61a}", + "square-y": "\u{e287}", + "square-youtube": "\u{f431}", + "youtube-square": "\u{f431}", + "square-z": "\u{e288}", + "squid": "\u{e450}", + "squirrel": "\u{f71a}", + "stack-exchange": "\u{f18d}", + "stack-overflow": "\u{f16c}", + "stackpath": "\u{f842}", + "staff": "\u{f71b}", + "staff-snake": "\u{e579}", + "rod-asclepius": "\u{e579}", + "rod-snake": "\u{e579}", + "staff-aesculapius": "\u{e579}", + "stairs": "\u{e289}", + "stamp": "\u{f5bf}", + "standard-definition": "\u{e28a}", + "rectangle-sd": "\u{e28a}", + "stapler": "\u{e5af}", + "star": "\u{f005}", + "star-and-crescent": "\u{f699}", + "star-christmas": "\u{f7d4}", + "star-exclamation": "\u{f2f3}", + "starfighter": "\u{e037}", + "starfighter-twin-ion-engine": "\u{e038}", + "starfighter-alt": "\u{e038}", + "starfighter-twin-ion-engine-advanced": "\u{e28e}", + "starfighter-alt-advanced": "\u{e28e}", + "star-half": "\u{f089}", + "star-half-stroke": "\u{f5c0}", + "star-half-alt": "\u{f5c0}", + "star-of-david": "\u{f69a}", + "star-of-life": "\u{f621}", + "stars": "\u{f762}", + "star-sharp": "\u{e28b}", + "star-sharp-half": "\u{e28c}", + "star-sharp-half-stroke": "\u{e28d}", + "star-sharp-half-alt": "\u{e28d}", + "starship": "\u{e039}", + "starship-freighter": "\u{e03a}", + "star-shooting": "\u{e036}", + "staylinked": "\u{f3f5}", + "steak": "\u{f824}", + "steam": "\u{f1b6}", + "steam-symbol": "\u{f3f6}", + "steering-wheel": "\u{f622}", + "sterling-sign": "\u{f154}", + "gbp": "\u{f154}", + "pound-sign": "\u{f154}", + "stethoscope": "\u{f0f1}", + "sticker-mule": "\u{f3f7}", + "stocking": "\u{f7d5}", + "stomach": "\u{f623}", + "stop": "\u{f04d}", + "stopwatch": "\u{f2f2}", + "stopwatch-20": "\u{e06f}", + "store": "\u{f54e}", + "store-lock": "\u{e4a6}", + "store-slash": "\u{e071}", + "strava": "\u{f428}", + "strawberry": "\u{e32b}", + "street-view": "\u{f21d}", + "stretcher": "\u{f825}", + "strikethrough": "\u{f0cc}", + "stripe": "\u{f429}", + "stripe-s": "\u{f42a}", + "stroopwafel": "\u{f551}", + "stubber": "\u{e5c7}", + "studiovinari": "\u{f3f8}", + "stumbleupon": "\u{f1a4}", + "stumbleupon-circle": "\u{f1a3}", + "subscript": "\u{f12c}", + "subtitles": "\u{e60f}", + "subtitles-slash": "\u{e610}", + "suitcase": "\u{f0f2}", + "suitcase-medical": "\u{f0fa}", + "medkit": "\u{f0fa}", + "suitcase-rolling": "\u{f5c1}", + "sun": "\u{f185}", + "sun-bright": "\u{e28f}", + "sun-alt": "\u{e28f}", + "sun-cloud": "\u{f763}", + "sun-dust": "\u{f764}", + "sunglasses": "\u{f892}", + "sun-haze": "\u{f765}", + "sun-plant-wilt": "\u{e57a}", + "sunrise": "\u{f766}", + "sunset": "\u{f767}", + "superpowers": "\u{f2dd}", + "superscript": "\u{f12b}", + "supple": "\u{f3f9}", + "suse": "\u{f7d6}", + "sushi": "\u{e48a}", + "nigiri": "\u{e48a}", + "sushi-roll": "\u{e48b}", + "maki-roll": "\u{e48b}", + "makizushi": "\u{e48b}", + "swap": "\u{e609}", + "swap-arrows": "\u{e60a}", + "swatchbook": "\u{f5c3}", + "swift": "\u{f8e1}", + "sword": "\u{f71c}", + "sword-laser": "\u{e03b}", + "sword-laser-alt": "\u{e03c}", + "swords": "\u{f71d}", + "swords-laser": "\u{e03d}", + "symbols": "\u{f86e}", + "icons-alt": "\u{f86e}", + "symfony": "\u{f83d}", + "synagogue": "\u{f69b}", + "syringe": "\u{f48e}", + "t": "\u{54}", + "table": "\u{f0ce}", + "table-cells": "\u{f00a}", + "th": "\u{f00a}", + "table-cells-column-lock": "\u{e678}", + "table-cells-column-unlock": "\u{e690}", + "table-cells-large": "\u{f009}", + "th-large": "\u{f009}", + "table-cells-lock": "\u{e679}", + "table-cells-row-lock": "\u{e67a}", + "table-cells-row-unlock": "\u{e691}", + "table-cells-unlock": "\u{e692}", + "table-columns": "\u{f0db}", + "columns": "\u{f0db}", + "table-layout": "\u{e290}", + "table-list": "\u{f00b}", + "th-list": "\u{f00b}", + "table-picnic": "\u{e32d}", + "table-pivot": "\u{e291}", + "table-rows": "\u{e292}", + "rows": "\u{e292}", + "tablet": "\u{f3fb}", + "tablet-android": "\u{f3fb}", + "tablet-button": "\u{f10a}", + "table-tennis-paddle-ball": "\u{f45d}", + "ping-pong-paddle-ball": "\u{f45d}", + "table-tennis": "\u{f45d}", + "table-tree": "\u{e293}", + "tablet-rugged": "\u{f48f}", + "tablets": "\u{f490}", + "tablet-screen": "\u{f3fc}", + "tablet-android-alt": "\u{f3fc}", + "tablet-screen-button": "\u{f3fa}", + "tablet-alt": "\u{f3fa}", + "tachograph-digital": "\u{f566}", + "digital-tachograph": "\u{f566}", + "taco": "\u{f826}", + "tag": "\u{f02b}", + "tags": "\u{f02c}", + "tally": "\u{f69c}", + "tally-5": "\u{f69c}", + "tally-1": "\u{e294}", + "tally-2": "\u{e295}", + "tally-3": "\u{e296}", + "tally-4": "\u{e297}", + "tamale": "\u{e451}", + "tank-water": "\u{e452}", + "tape": "\u{f4db}", + "tarp": "\u{e57b}", + "tarp-droplet": "\u{e57c}", + "taxi": "\u{f1ba}", + "cab": "\u{f1ba}", + "taxi-bus": "\u{e298}", + "teamspeak": "\u{f4f9}", + "teddy-bear": "\u{e3cf}", + "teeth": "\u{f62e}", + "teeth-open": "\u{f62f}", + "telegram": "\u{f2c6}", + "telegram-plane": "\u{f2c6}", + "telescope": "\u{e03e}", + "temperature-arrow-down": "\u{e03f}", + "temperature-down": "\u{e03f}", + "temperature-arrow-up": "\u{e040}", + "temperature-up": "\u{e040}", + "temperature-empty": "\u{f2cb}", + "temperature-0": "\u{f2cb}", + "thermometer-0": "\u{f2cb}", + "thermometer-empty": "\u{f2cb}", + "temperature-full": "\u{f2c7}", + "temperature-4": "\u{f2c7}", + "thermometer-4": "\u{f2c7}", + "thermometer-full": "\u{f2c7}", + "temperature-half": "\u{f2c9}", + "temperature-2": "\u{f2c9}", + "thermometer-2": "\u{f2c9}", + "thermometer-half": "\u{f2c9}", + "temperature-high": "\u{f769}", + "temperature-list": "\u{e299}", + "temperature-low": "\u{f76b}", + "temperature-quarter": "\u{f2ca}", + "temperature-1": "\u{f2ca}", + "thermometer-1": "\u{f2ca}", + "thermometer-quarter": "\u{f2ca}", + "temperature-snow": "\u{f768}", + "temperature-frigid": "\u{f768}", + "temperature-sun": "\u{f76a}", + "temperature-hot": "\u{f76a}", + "temperature-three-quarters": "\u{f2c8}", + "temperature-3": "\u{f2c8}", + "thermometer-3": "\u{f2c8}", + "thermometer-three-quarters": "\u{f2c8}", + "tencent-weibo": "\u{f1d5}", + "tenge-sign": "\u{f7d7}", + "tenge": "\u{f7d7}", + "tennis-ball": "\u{f45e}", + "tent": "\u{e57d}", + "tent-arrow-down-to-line": "\u{e57e}", + "tent-arrow-left-right": "\u{e57f}", + "tent-arrows-down": "\u{e581}", + "tent-arrow-turn-left": "\u{e580}", + "tent-double-peak": "\u{e627}", + "tents": "\u{e582}", + "terminal": "\u{f120}", + "text": "\u{f893}", + "text-height": "\u{f034}", + "text-size": "\u{f894}", + "text-slash": "\u{f87d}", + "remove-format": "\u{f87d}", + "text-width": "\u{f035}", + "themeco": "\u{f5c6}", + "themeisle": "\u{f2b2}", + "the-red-yeti": "\u{f69d}", + "thermometer": "\u{f491}", + "theta": "\u{f69e}", + "think-peaks": "\u{f731}", + "thought-bubble": "\u{e32e}", + "threads": "\u{e618}", + "thumbs-down": "\u{f165}", + "thumbs-up": "\u{f164}", + "thumbtack": "\u{f08d}", + "thumb-tack": "\u{f08d}", + "thumbtack-slash": "\u{e68f}", + "thumb-tack-slash": "\u{e68f}", + "tick": "\u{e32f}", + "ticket": "\u{f145}", + "ticket-airline": "\u{e29a}", + "ticket-perforated-plane": "\u{e29a}", + "ticket-plane": "\u{e29a}", + "ticket-perforated": "\u{e63e}", + "tickets": "\u{e658}", + "tickets-airline": "\u{e29b}", + "tickets-perforated-plane": "\u{e29b}", + "tickets-plane": "\u{e29b}", + "ticket-simple": "\u{f3ff}", + "ticket-alt": "\u{f3ff}", + "tickets-perforated": "\u{e63f}", + "tickets-simple": "\u{e659}", + "tiktok": "\u{e07b}", + "tilde": "\u{7e}", + "timeline": "\u{e29c}", + "timeline-arrow": "\u{e29d}", + "timer": "\u{e29e}", + "tire": "\u{f631}", + "tire-flat": "\u{f632}", + "tire-pressure-warning": "\u{f633}", + "tire-rugged": "\u{f634}", + "toggle-large-off": "\u{e5b0}", + "toggle-large-on": "\u{e5b1}", + "toggle-off": "\u{f204}", + "toggle-on": "\u{f205}", + "toilet": "\u{f7d8}", + "toilet-paper": "\u{f71e}", + "toilet-paper-blank": "\u{f71f}", + "toilet-paper-alt": "\u{f71f}", + "toilet-paper-blank-under": "\u{e29f}", + "toilet-paper-reverse-alt": "\u{e29f}", + "toilet-paper-check": "\u{e5b2}", + "toilet-paper-slash": "\u{e072}", + "toilet-paper-under": "\u{e2a0}", + "toilet-paper-reverse": "\u{e2a0}", + "toilet-paper-under-slash": "\u{e2a1}", + "toilet-paper-reverse-slash": "\u{e2a1}", + "toilet-paper-xmark": "\u{e5b3}", + "toilet-portable": "\u{e583}", + "toilets-portable": "\u{e584}", + "tomato": "\u{e330}", + "tombstone": "\u{f720}", + "tombstone-blank": "\u{f721}", + "tombstone-alt": "\u{f721}", + "toolbox": "\u{f552}", + "tooth": "\u{f5c9}", + "toothbrush": "\u{f635}", + "torii-gate": "\u{f6a1}", + "tornado": "\u{f76f}", + "tower-broadcast": "\u{f519}", + "broadcast-tower": "\u{f519}", + "tower-cell": "\u{e585}", + "tower-control": "\u{e2a2}", + "tower-observation": "\u{e586}", + "tractor": "\u{f722}", + "trade-federation": "\u{f513}", + "trademark": "\u{f25c}", + "traffic-cone": "\u{f636}", + "traffic-light": "\u{f637}", + "traffic-light-go": "\u{f638}", + "traffic-light-slow": "\u{f639}", + "traffic-light-stop": "\u{f63a}", + "trailer": "\u{e041}", + "train": "\u{f238}", + "train-subway": "\u{f239}", + "subway": "\u{f239}", + "train-subway-tunnel": "\u{e2a3}", + "subway-tunnel": "\u{e2a3}", + "train-track": "\u{e453}", + "train-tram": "\u{e5b4}", + "train-tunnel": "\u{e454}", + "transformer-bolt": "\u{e2a4}", + "transgender": "\u{f225}", + "transgender-alt": "\u{f225}", + "transporter": "\u{e042}", + "transporter-1": "\u{e043}", + "transporter-2": "\u{e044}", + "transporter-3": "\u{e045}", + "transporter-4": "\u{e2a5}", + "transporter-5": "\u{e2a6}", + "transporter-6": "\u{e2a7}", + "transporter-7": "\u{e2a8}", + "transporter-empty": "\u{e046}", + "trash": "\u{f1f8}", + "trash-arrow-up": "\u{f829}", + "trash-restore": "\u{f829}", + "trash-can": "\u{f2ed}", + "trash-alt": "\u{f2ed}", + "trash-can-arrow-up": "\u{f82a}", + "trash-restore-alt": "\u{f82a}", + "trash-can-check": "\u{e2a9}", + "trash-can-clock": "\u{e2aa}", + "trash-can-list": "\u{e2ab}", + "trash-can-plus": "\u{e2ac}", + "trash-can-slash": "\u{e2ad}", + "trash-alt-slash": "\u{e2ad}", + "trash-can-undo": "\u{f896}", + "trash-can-arrow-turn-left": "\u{f896}", + "trash-undo-alt": "\u{f896}", + "trash-can-xmark": "\u{e2ae}", + "trash-check": "\u{e2af}", + "trash-clock": "\u{e2b0}", + "trash-list": "\u{e2b1}", + "trash-plus": "\u{e2b2}", + "trash-slash": "\u{e2b3}", + "trash-undo": "\u{f895}", + "trash-arrow-turn-left": "\u{f895}", + "trash-xmark": "\u{e2b4}", + "treasure-chest": "\u{f723}", + "tree": "\u{f1bb}", + "tree-christmas": "\u{f7db}", + "tree-city": "\u{e587}", + "tree-deciduous": "\u{f400}", + "tree-alt": "\u{f400}", + "tree-decorated": "\u{f7dc}", + "tree-large": "\u{f7dd}", + "tree-palm": "\u{f82b}", + "trees": "\u{f724}", + "trello": "\u{f181}", + "t-rex": "\u{e629}", + "triangle": "\u{f2ec}", + "triangle-exclamation": "\u{f071}", + "exclamation-triangle": "\u{f071}", + "warning": "\u{f071}", + "triangle-instrument": "\u{f8e2}", + "triangle-music": "\u{f8e2}", + "triangle-person-digging": "\u{f85d}", + "construction": "\u{f85d}", + "tricycle": "\u{e5c3}", + "tricycle-adult": "\u{e5c4}", + "trillium": "\u{e588}", + "trophy": "\u{f091}", + "trophy-star": "\u{f2eb}", + "trophy-alt": "\u{f2eb}", + "trowel": "\u{e589}", + "trowel-bricks": "\u{e58a}", + "truck": "\u{f0d1}", + "truck-arrow-right": "\u{e58b}", + "truck-bolt": "\u{e3d0}", + "truck-clock": "\u{f48c}", + "shipping-timed": "\u{f48c}", + "truck-container": "\u{f4dc}", + "truck-container-empty": "\u{e2b5}", + "truck-droplet": "\u{e58c}", + "truck-fast": "\u{f48b}", + "shipping-fast": "\u{f48b}", + "truck-field": "\u{e58d}", + "truck-field-un": "\u{e58e}", + "truck-fire": "\u{e65a}", + "truck-flatbed": "\u{e2b6}", + "truck-front": "\u{e2b7}", + "truck-ladder": "\u{e657}", + "truck-medical": "\u{f0f9}", + "ambulance": "\u{f0f9}", + "truck-monster": "\u{f63b}", + "truck-moving": "\u{f4df}", + "truck-pickup": "\u{f63c}", + "truck-plane": "\u{e58f}", + "truck-plow": "\u{f7de}", + "truck-ramp": "\u{f4e0}", + "truck-ramp-box": "\u{f4de}", + "truck-loading": "\u{f4de}", + "truck-ramp-couch": "\u{f4dd}", + "truck-couch": "\u{f4dd}", + "truck-tow": "\u{e2b8}", + "truck-utensils": "\u{e628}", + "trumpet": "\u{f8e3}", + "tty": "\u{f1e4}", + "teletype": "\u{f1e4}", + "tty-answer": "\u{e2b9}", + "teletype-answer": "\u{e2b9}", + "tugrik-sign": "\u{e2ba}", + "tumblr": "\u{f173}", + "turkey": "\u{f725}", + "turkish-lira-sign": "\u{e2bb}", + "try": "\u{e2bb}", + "turkish-lira": "\u{e2bb}", + "turn-down": "\u{f3be}", + "level-down-alt": "\u{f3be}", + "turn-down-left": "\u{e331}", + "turn-down-right": "\u{e455}", + "turn-left": "\u{e636}", + "turn-left-down": "\u{e637}", + "turn-left-up": "\u{e638}", + "turn-right": "\u{e639}", + "turntable": "\u{f8e4}", + "turn-up": "\u{f3bf}", + "level-up-alt": "\u{f3bf}", + "turtle": "\u{f726}", + "tv": "\u{f26c}", + "television": "\u{f26c}", + "tv-alt": "\u{f26c}", + "tv-music": "\u{f8e6}", + "tv-retro": "\u{f401}", + "twitch": "\u{f1e8}", + "twitter": "\u{f099}", + "typewriter": "\u{f8e7}", + "typo3": "\u{f42b}", + "u": "\u{55}", + "uber": "\u{f402}", + "ubuntu": "\u{f7df}", + "ufo": "\u{e047}", + "ufo-beam": "\u{e048}", + "uikit": "\u{f403}", + "umbraco": "\u{f8e8}", + "umbrella": "\u{f0e9}", + "umbrella-beach": "\u{f5ca}", + "umbrella-simple": "\u{e2bc}", + "umbrella-alt": "\u{e2bc}", + "uncharted": "\u{e084}", + "underline": "\u{f0cd}", + "unicorn": "\u{f727}", + "uniform-martial-arts": "\u{e3d1}", + "union": "\u{f6a2}", + "uniregistry": "\u{f404}", + "unity": "\u{e049}", + "universal-access": "\u{f29a}", + "unlock": "\u{f09c}", + "unlock-keyhole": "\u{f13e}", + "unlock-alt": "\u{f13e}", + "unsplash": "\u{e07c}", + "untappd": "\u{f405}", + "up": "\u{f357}", + "arrow-alt-up": "\u{f357}", + "up-down": "\u{f338}", + "arrows-alt-v": "\u{f338}", + "up-down-left-right": "\u{f0b2}", + "arrows-alt": "\u{f0b2}", + "up-from-bracket": "\u{e590}", + "up-from-dotted-line": "\u{e456}", + "up-from-line": "\u{f346}", + "arrow-alt-from-bottom": "\u{f346}", + "up-left": "\u{e2bd}", + "upload": "\u{f093}", + "up-long": "\u{f30c}", + "long-arrow-alt-up": "\u{f30c}", + "up-right": "\u{e2be}", + "up-right-and-down-left-from-center": "\u{f424}", + "expand-alt": "\u{f424}", + "up-right-from-square": "\u{f35d}", + "external-link-alt": "\u{f35d}", + "ups": "\u{f7e0}", + "up-to-bracket": "\u{e66e}", + "up-to-dotted-line": "\u{e457}", + "up-to-line": "\u{f34d}", + "arrow-alt-to-top": "\u{f34d}", + "upwork": "\u{e641}", + "usb": "\u{f287}", + "usb-drive": "\u{f8e9}", + "user": "\u{f007}", + "user-alien": "\u{e04a}", + "user-astronaut": "\u{f4fb}", + "user-beard-bolt": "\u{e689}", + "user-bounty-hunter": "\u{e2bf}", + "user-check": "\u{f4fc}", + "user-chef": "\u{e3d2}", + "user-clock": "\u{f4fd}", + "user-cowboy": "\u{f8ea}", + "user-crown": "\u{f6a4}", + "user-doctor": "\u{f0f0}", + "user-md": "\u{f0f0}", + "user-doctor-hair": "\u{e458}", + "user-doctor-hair-long": "\u{e459}", + "user-doctor-message": "\u{f82e}", + "user-md-chat": "\u{f82e}", + "user-gear": "\u{f4fe}", + "user-cog": "\u{f4fe}", + "user-graduate": "\u{f501}", + "user-group": "\u{f500}", + "user-friends": "\u{f500}", + "user-group-crown": "\u{f6a5}", + "users-crown": "\u{f6a5}", + "user-group-simple": "\u{e603}", + "user-hair": "\u{e45a}", + "user-hair-buns": "\u{e3d3}", + "user-hair-long": "\u{e45b}", + "user-hair-mullet": "\u{e45c}", + "business-front": "\u{e45c}", + "party-back": "\u{e45c}", + "trian-balbot": "\u{e45c}", + "user-headset": "\u{f82d}", + "user-helmet-safety": "\u{f82c}", + "user-construction": "\u{f82c}", + "user-hard-hat": "\u{f82c}", + "user-hoodie": "\u{e68a}", + "user-injured": "\u{f728}", + "user-large": "\u{f406}", + "user-alt": "\u{f406}", + "user-large-slash": "\u{f4fa}", + "user-alt-slash": "\u{f4fa}", + "user-lock": "\u{f502}", + "user-magnifying-glass": "\u{e5c5}", + "user-minus": "\u{f503}", + "user-music": "\u{f8eb}", + "user-ninja": "\u{f504}", + "user-nurse": "\u{f82f}", + "user-nurse-hair": "\u{e45d}", + "user-nurse-hair-long": "\u{e45e}", + "user-pen": "\u{f4ff}", + "user-edit": "\u{f4ff}", + "user-pilot": "\u{e2c0}", + "user-pilot-tie": "\u{e2c1}", + "user-plus": "\u{f234}", + "user-police": "\u{e333}", + "user-police-tie": "\u{e334}", + "user-robot": "\u{e04b}", + "user-robot-xmarks": "\u{e4a7}", + "users": "\u{f0c0}", + "users-between-lines": "\u{e591}", + "user-secret": "\u{f21b}", + "users-gear": "\u{f509}", + "users-cog": "\u{f509}", + "user-shakespeare": "\u{e2c2}", + "user-shield": "\u{f505}", + "user-slash": "\u{f506}", + "users-line": "\u{e592}", + "users-medical": "\u{f830}", + "users-rays": "\u{e593}", + "users-rectangle": "\u{e594}", + "users-slash": "\u{e073}", + "users-viewfinder": "\u{e595}", + "user-tag": "\u{f507}", + "user-tie": "\u{f508}", + "user-tie-hair": "\u{e45f}", + "user-tie-hair-long": "\u{e460}", + "user-unlock": "\u{e058}", + "user-visor": "\u{e04c}", + "user-vneck": "\u{e461}", + "user-vneck-hair": "\u{e462}", + "user-vneck-hair-long": "\u{e463}", + "user-xmark": "\u{f235}", + "user-times": "\u{f235}", + "usps": "\u{f7e1}", + "ussunnah": "\u{f407}", + "utensils": "\u{f2e7}", + "cutlery": "\u{f2e7}", + "utensils-slash": "\u{e464}", + "utility-pole": "\u{e2c3}", + "utility-pole-double": "\u{e2c4}", + "v": "\u{56}", + "vaadin": "\u{f408}", + "vacuum": "\u{e04d}", + "vacuum-robot": "\u{e04e}", + "value-absolute": "\u{f6a6}", + "van-shuttle": "\u{f5b6}", + "shuttle-van": "\u{f5b6}", + "vault": "\u{e2c5}", + "vector-circle": "\u{e2c6}", + "vector-polygon": "\u{e2c7}", + "vector-square": "\u{f5cb}", + "vent-damper": "\u{e465}", + "venus": "\u{f221}", + "venus-double": "\u{f226}", + "venus-mars": "\u{f228}", + "vest": "\u{e085}", + "vest-patches": "\u{e086}", + "viacoin": "\u{f237}", + "viadeo": "\u{f2a9}", + "vial": "\u{f492}", + "vial-circle-check": "\u{e596}", + "vials": "\u{f493}", + "vial-virus": "\u{e597}", + "viber": "\u{f409}", + "video": "\u{f03d}", + "video-camera": "\u{f03d}", + "video-arrow-down-left": "\u{e2c8}", + "video-arrow-up-right": "\u{e2c9}", + "video-plus": "\u{f4e1}", + "video-slash": "\u{f4e2}", + "vihara": "\u{f6a7}", + "vimeo": "\u{f40a}", + "vimeo-v": "\u{f27d}", + "vine": "\u{f1ca}", + "violin": "\u{f8ed}", + "virus": "\u{e074}", + "virus-covid": "\u{e4a8}", + "virus-covid-slash": "\u{e4a9}", + "viruses": "\u{e076}", + "virus-slash": "\u{e075}", + "vk": "\u{f189}", + "vnv": "\u{f40b}", + "voicemail": "\u{f897}", + "volcano": "\u{f770}", + "volleyball": "\u{f45f}", + "volleyball-ball": "\u{f45f}", + "volume": "\u{f6a8}", + "volume-medium": "\u{f6a8}", + "volume-high": "\u{f028}", + "volume-up": "\u{f028}", + "volume-low": "\u{f027}", + "volume-down": "\u{f027}", + "volume-off": "\u{f026}", + "volume-slash": "\u{f2e2}", + "volume-xmark": "\u{f6a9}", + "volume-mute": "\u{f6a9}", + "volume-times": "\u{f6a9}", + "vr-cardboard": "\u{f729}", + "vuejs": "\u{f41f}", + "w": "\u{57}", + "waffle": "\u{e466}", + "wagon-covered": "\u{f8ee}", + "walker": "\u{f831}", + "walkie-talkie": "\u{f8ef}", + "wallet": "\u{f555}", + "wand": "\u{f72a}", + "wand-magic": "\u{f0d0}", + "magic": "\u{f0d0}", + "wand-magic-sparkles": "\u{e2ca}", + "magic-wand-sparkles": "\u{e2ca}", + "wand-sparkles": "\u{f72b}", + "warehouse": "\u{f494}", + "warehouse-full": "\u{f495}", + "warehouse-alt": "\u{f495}", + "washing-machine": "\u{f898}", + "washer": "\u{f898}", + "watch": "\u{f2e1}", + "watch-apple": "\u{e2cb}", + "watch-calculator": "\u{f8f0}", + "watch-fitness": "\u{f63e}", + "watchman-monitoring": "\u{e087}", + "watch-smart": "\u{e2cc}", + "water": "\u{f773}", + "water-arrow-down": "\u{f774}", + "water-lower": "\u{f774}", + "water-arrow-up": "\u{f775}", + "water-rise": "\u{f775}", + "water-ladder": "\u{f5c5}", + "ladder-water": "\u{f5c5}", + "swimming-pool": "\u{f5c5}", + "watermelon-slice": "\u{e337}", + "wave": "\u{e65b}", + "waveform": "\u{f8f1}", + "waveform-lines": "\u{f8f2}", + "waveform-path": "\u{f8f2}", + "wave-pulse": "\u{f5f8}", + "heart-rate": "\u{f5f8}", + "wave-sine": "\u{f899}", + "wave-square": "\u{f83e}", + "waves-sine": "\u{e65d}", + "wave-triangle": "\u{f89a}", + "waze": "\u{f83f}", + "web-awesome": "\u{e682}", + "webflow": "\u{e65c}", + "webhook": "\u{e5d5}", + "weebly": "\u{f5cc}", + "weibo": "\u{f18a}", + "weight-hanging": "\u{f5cd}", + "weight-scale": "\u{f496}", + "weight": "\u{f496}", + "weixin": "\u{f1d7}", + "whale": "\u{f72c}", + "whatsapp": "\u{f232}", + "wheat": "\u{f72d}", + "wheat-awn": "\u{e2cd}", + "wheat-alt": "\u{e2cd}", + "wheat-awn-circle-exclamation": "\u{e598}", + "wheat-awn-slash": "\u{e338}", + "wheat-slash": "\u{e339}", + "wheelchair": "\u{f193}", + "wheelchair-move": "\u{e2ce}", + "wheelchair-alt": "\u{e2ce}", + "whiskey-glass": "\u{f7a0}", + "glass-whiskey": "\u{f7a0}", + "whiskey-glass-ice": "\u{f7a1}", + "glass-whiskey-rocks": "\u{f7a1}", + "whistle": "\u{f460}", + "whmcs": "\u{f40d}", + "wifi": "\u{f1eb}", + "wifi-3": "\u{f1eb}", + "wifi-strong": "\u{f1eb}", + "wifi-exclamation": "\u{e2cf}", + "wifi-fair": "\u{f6ab}", + "wifi-2": "\u{f6ab}", + "wifi-slash": "\u{f6ac}", + "wifi-weak": "\u{f6aa}", + "wifi-1": "\u{f6aa}", + "wikipedia-w": "\u{f266}", + "wind": "\u{f72e}", + "window": "\u{f40e}", + "window-flip": "\u{f40f}", + "window-alt": "\u{f40f}", + "window-frame": "\u{e04f}", + "window-frame-open": "\u{e050}", + "window-maximize": "\u{f2d0}", + "window-minimize": "\u{f2d1}", + "window-restore": "\u{f2d2}", + "windows": "\u{f17a}", + "windsock": "\u{f777}", + "wind-turbine": "\u{f89b}", + "wind-warning": "\u{f776}", + "wind-circle-exclamation": "\u{f776}", + "wine-bottle": "\u{f72f}", + "wine-glass": "\u{f4e3}", + "wine-glass-crack": "\u{f4bb}", + "fragile": "\u{f4bb}", + "wine-glass-empty": "\u{f5ce}", + "wine-glass-alt": "\u{f5ce}", + "wirsindhandwerk": "\u{e2d0}", + "wsh": "\u{e2d0}", + "wix": "\u{f5cf}", + "wizards-of-the-coast": "\u{f730}", + "wodu": "\u{e088}", + "wolf-pack-battalion": "\u{f514}", + "won-sign": "\u{f159}", + "krw": "\u{f159}", + "won": "\u{f159}", + "wordpress": "\u{f19a}", + "wordpress-simple": "\u{f411}", + "worm": "\u{e599}", + "wpbeginner": "\u{f297}", + "wpexplorer": "\u{f2de}", + "wpforms": "\u{f298}", + "wpressr": "\u{f3e4}", + "rendact": "\u{f3e4}", + "wreath": "\u{f7e2}", + "wreath-laurel": "\u{e5d2}", + "wrench": "\u{f0ad}", + "wrench-simple": "\u{e2d1}", + "x": "\u{58}", + "xbox": "\u{f412}", + "xing": "\u{f168}", + "xmark": "\u{f00d}", + "close": "\u{f00d}", + "multiply": "\u{f00d}", + "remove": "\u{f00d}", + "times": "\u{f00d}", + "xmark-large": "\u{e59b}", + "xmarks-lines": "\u{e59a}", + "xmark-to-slot": "\u{f771}", + "times-to-slot": "\u{f771}", + "vote-nay": "\u{f771}", + "x-ray": "\u{f497}", + "x-twitter": "\u{e61b}", + "y": "\u{59}", + "yahoo": "\u{f19e}", + "yammer": "\u{f840}", + "yandex": "\u{f413}", + "yandex-international": "\u{f414}", + "yarn": "\u{f7e3}", + "y-combinator": "\u{f23b}", + "yelp": "\u{f1e9}", + "yen-sign": "\u{f157}", + "cny": "\u{f157}", + "jpy": "\u{f157}", + "rmb": "\u{f157}", + "yen": "\u{f157}", + "yin-yang": "\u{f6ad}", + "yoast": "\u{f2b1}", + "youtube": "\u{f167}", + "z": "\u{5a}", + "zhihu": "\u{f63f}", +) +#let fa-0 = fa-icon.with("\u{30}") +#let fa-00 = fa-icon.with("\u{e467}") +#let fa-1 = fa-icon.with("\u{31}") +#let fa-2 = fa-icon.with("\u{32}") +#let fa-3 = fa-icon.with("\u{33}") +#let fa-360-degrees = fa-icon.with("\u{e2dc}") +#let fa-4 = fa-icon.with("\u{34}") +#let fa-42-group = fa-icon.with("\u{e080}") +#let fa-innosoft = fa-icon.with("\u{e080}") +#let fa-5 = fa-icon.with("\u{35}") +#let fa-500px = fa-icon.with("\u{f26e}") +#let fa-6 = fa-icon.with("\u{36}") +#let fa-7 = fa-icon.with("\u{37}") +#let fa-8 = fa-icon.with("\u{38}") +#let fa-9 = fa-icon.with("\u{39}") +#let fa-a = fa-icon.with("\u{41}") +#let fa-abacus = fa-icon.with("\u{f640}") +#let fa-accent-grave = fa-icon.with("\u{60}") +#let fa-accessible-icon = fa-icon.with("\u{f368}") +#let fa-accusoft = fa-icon.with("\u{f369}") +#let fa-acorn = fa-icon.with("\u{f6ae}") +#let fa-address-book = fa-icon.with("\u{f2b9}") +#let fa-contact-book = fa-icon.with("\u{f2b9}") +#let fa-address-card = fa-icon.with("\u{f2bb}") +#let fa-contact-card = fa-icon.with("\u{f2bb}") +#let fa-vcard = fa-icon.with("\u{f2bb}") +#let fa-adn = fa-icon.with("\u{f170}") +#let fa-adversal = fa-icon.with("\u{f36a}") +#let fa-affiliatetheme = fa-icon.with("\u{f36b}") +#let fa-airbnb = fa-icon.with("\u{f834}") +#let fa-air-conditioner = fa-icon.with("\u{f8f4}") +#let fa-airplay = fa-icon.with("\u{e089}") +#let fa-alarm-clock = fa-icon.with("\u{f34e}") +#let fa-alarm-exclamation = fa-icon.with("\u{f843}") +#let fa-alarm-plus = fa-icon.with("\u{f844}") +#let fa-alarm-snooze = fa-icon.with("\u{f845}") +#let fa-album = fa-icon.with("\u{f89f}") +#let fa-album-circle-plus = fa-icon.with("\u{e48c}") +#let fa-album-circle-user = fa-icon.with("\u{e48d}") +#let fa-album-collection = fa-icon.with("\u{f8a0}") +#let fa-album-collection-circle-plus = fa-icon.with("\u{e48e}") +#let fa-album-collection-circle-user = fa-icon.with("\u{e48f}") +#let fa-algolia = fa-icon.with("\u{f36c}") +#let fa-alicorn = fa-icon.with("\u{f6b0}") +#let fa-alien = fa-icon.with("\u{f8f5}") +#let fa-alien-8bit = fa-icon.with("\u{f8f6}") +#let fa-alien-monster = fa-icon.with("\u{f8f6}") +#let fa-align-center = fa-icon.with("\u{f037}") +#let fa-align-justify = fa-icon.with("\u{f039}") +#let fa-align-left = fa-icon.with("\u{f036}") +#let fa-align-right = fa-icon.with("\u{f038}") +#let fa-align-slash = fa-icon.with("\u{f846}") +#let fa-alipay = fa-icon.with("\u{f642}") +#let fa-alt = fa-icon.with("\u{e08a}") +#let fa-amazon = fa-icon.with("\u{f270}") +#let fa-amazon-pay = fa-icon.with("\u{f42c}") +#let fa-amilia = fa-icon.with("\u{f36d}") +#let fa-ampersand = fa-icon.with("\u{26}") +#let fa-amp-guitar = fa-icon.with("\u{f8a1}") +#let fa-anchor = fa-icon.with("\u{f13d}") +#let fa-anchor-circle-check = fa-icon.with("\u{e4aa}") +#let fa-anchor-circle-exclamation = fa-icon.with("\u{e4ab}") +#let fa-anchor-circle-xmark = fa-icon.with("\u{e4ac}") +#let fa-anchor-lock = fa-icon.with("\u{e4ad}") +#let fa-android = fa-icon.with("\u{f17b}") +#let fa-angel = fa-icon.with("\u{f779}") +#let fa-angellist = fa-icon.with("\u{f209}") +#let fa-angle = fa-icon.with("\u{e08c}") +#let fa-angle-90 = fa-icon.with("\u{e08d}") +#let fa-angle-down = fa-icon.with("\u{f107}") +#let fa-angle-left = fa-icon.with("\u{f104}") +#let fa-angle-right = fa-icon.with("\u{f105}") +#let fa-angles-down = fa-icon.with("\u{f103}") +#let fa-angle-double-down = fa-icon.with("\u{f103}") +#let fa-angles-left = fa-icon.with("\u{f100}") +#let fa-angle-double-left = fa-icon.with("\u{f100}") +#let fa-angles-right = fa-icon.with("\u{f101}") +#let fa-angle-double-right = fa-icon.with("\u{f101}") +#let fa-angles-up = fa-icon.with("\u{f102}") +#let fa-angle-double-up = fa-icon.with("\u{f102}") +#let fa-angles-up-down = fa-icon.with("\u{e60d}") +#let fa-angle-up = fa-icon.with("\u{f106}") +#let fa-angrycreative = fa-icon.with("\u{f36e}") +#let fa-angular = fa-icon.with("\u{f420}") +#let fa-ankh = fa-icon.with("\u{f644}") +#let fa-ant = fa-icon.with("\u{e680}") +#let fa-apartment = fa-icon.with("\u{e468}") +#let fa-aperture = fa-icon.with("\u{e2df}") +#let fa-apostrophe = fa-icon.with("\u{27}") +#let fa-apper = fa-icon.with("\u{f371}") +#let fa-apple = fa-icon.with("\u{f179}") +#let fa-apple-core = fa-icon.with("\u{e08f}") +#let fa-apple-pay = fa-icon.with("\u{f415}") +#let fa-apple-whole = fa-icon.with("\u{f5d1}") +#let fa-apple-alt = fa-icon.with("\u{f5d1}") +#let fa-app-store = fa-icon.with("\u{f36f}") +#let fa-app-store-ios = fa-icon.with("\u{f370}") +#let fa-archway = fa-icon.with("\u{f557}") +#let fa-arrow-down = fa-icon.with("\u{f063}") +#let fa-arrow-down-1-9 = fa-icon.with("\u{f162}") +#let fa-sort-numeric-asc = fa-icon.with("\u{f162}") +#let fa-sort-numeric-down = fa-icon.with("\u{f162}") +#let fa-arrow-down-9-1 = fa-icon.with("\u{f886}") +#let fa-sort-numeric-desc = fa-icon.with("\u{f886}") +#let fa-sort-numeric-down-alt = fa-icon.with("\u{f886}") +#let fa-arrow-down-arrow-up = fa-icon.with("\u{f883}") +#let fa-sort-alt = fa-icon.with("\u{f883}") +#let fa-arrow-down-a-z = fa-icon.with("\u{f15d}") +#let fa-sort-alpha-asc = fa-icon.with("\u{f15d}") +#let fa-sort-alpha-down = fa-icon.with("\u{f15d}") +#let fa-arrow-down-big-small = fa-icon.with("\u{f88c}") +#let fa-sort-size-down = fa-icon.with("\u{f88c}") +#let fa-arrow-down-from-arc = fa-icon.with("\u{e614}") +#let fa-arrow-down-from-bracket = fa-icon.with("\u{e667}") +#let fa-arrow-down-from-dotted-line = fa-icon.with("\u{e090}") +#let fa-arrow-down-from-line = fa-icon.with("\u{f345}") +#let fa-arrow-from-top = fa-icon.with("\u{f345}") +#let fa-arrow-down-left = fa-icon.with("\u{e091}") +#let fa-arrow-down-left-and-arrow-up-right-to-center = fa-icon.with("\u{e092}") +#let fa-arrow-down-long = fa-icon.with("\u{f175}") +#let fa-long-arrow-down = fa-icon.with("\u{f175}") +#let fa-arrow-down-right = fa-icon.with("\u{e093}") +#let fa-arrow-down-short-wide = fa-icon.with("\u{f884}") +#let fa-sort-amount-desc = fa-icon.with("\u{f884}") +#let fa-sort-amount-down-alt = fa-icon.with("\u{f884}") +#let fa-arrow-down-small-big = fa-icon.with("\u{f88d}") +#let fa-sort-size-down-alt = fa-icon.with("\u{f88d}") +#let fa-arrow-down-square-triangle = fa-icon.with("\u{f889}") +#let fa-sort-shapes-down-alt = fa-icon.with("\u{f889}") +#let fa-arrow-down-to-arc = fa-icon.with("\u{e4ae}") +#let fa-arrow-down-to-bracket = fa-icon.with("\u{e094}") +#let fa-arrow-down-to-dotted-line = fa-icon.with("\u{e095}") +#let fa-arrow-down-to-line = fa-icon.with("\u{f33d}") +#let fa-arrow-to-bottom = fa-icon.with("\u{f33d}") +#let fa-arrow-down-to-square = fa-icon.with("\u{e096}") +#let fa-arrow-down-triangle-square = fa-icon.with("\u{f888}") +#let fa-sort-shapes-down = fa-icon.with("\u{f888}") +#let fa-arrow-down-up-across-line = fa-icon.with("\u{e4af}") +#let fa-arrow-down-up-lock = fa-icon.with("\u{e4b0}") +#let fa-arrow-down-wide-short = fa-icon.with("\u{f160}") +#let fa-sort-amount-asc = fa-icon.with("\u{f160}") +#let fa-sort-amount-down = fa-icon.with("\u{f160}") +#let fa-arrow-down-z-a = fa-icon.with("\u{f881}") +#let fa-sort-alpha-desc = fa-icon.with("\u{f881}") +#let fa-sort-alpha-down-alt = fa-icon.with("\u{f881}") +#let fa-arrow-left = fa-icon.with("\u{f060}") +#let fa-arrow-left-from-arc = fa-icon.with("\u{e615}") +#let fa-arrow-left-from-bracket = fa-icon.with("\u{e668}") +#let fa-arrow-left-from-line = fa-icon.with("\u{f344}") +#let fa-arrow-from-right = fa-icon.with("\u{f344}") +#let fa-arrow-left-long = fa-icon.with("\u{f177}") +#let fa-long-arrow-left = fa-icon.with("\u{f177}") +#let fa-arrow-left-long-to-line = fa-icon.with("\u{e3d4}") +#let fa-arrow-left-to-arc = fa-icon.with("\u{e616}") +#let fa-arrow-left-to-bracket = fa-icon.with("\u{e669}") +#let fa-arrow-left-to-line = fa-icon.with("\u{f33e}") +#let fa-arrow-to-left = fa-icon.with("\u{f33e}") +#let fa-arrow-pointer = fa-icon.with("\u{f245}") +#let fa-mouse-pointer = fa-icon.with("\u{f245}") +#let fa-arrow-progress = fa-icon.with("\u{e5df}") +#let fa-arrow-right = fa-icon.with("\u{f061}") +#let fa-arrow-right-arrow-left = fa-icon.with("\u{f0ec}") +#let fa-exchange = fa-icon.with("\u{f0ec}") +#let fa-arrow-right-from-arc = fa-icon.with("\u{e4b1}") +#let fa-arrow-right-from-bracket = fa-icon.with("\u{f08b}") +#let fa-sign-out = fa-icon.with("\u{f08b}") +#let fa-arrow-right-from-line = fa-icon.with("\u{f343}") +#let fa-arrow-from-left = fa-icon.with("\u{f343}") +#let fa-arrow-right-long = fa-icon.with("\u{f178}") +#let fa-long-arrow-right = fa-icon.with("\u{f178}") +#let fa-arrow-right-long-to-line = fa-icon.with("\u{e3d5}") +#let fa-arrow-right-to-arc = fa-icon.with("\u{e4b2}") +#let fa-arrow-right-to-bracket = fa-icon.with("\u{f090}") +#let fa-sign-in = fa-icon.with("\u{f090}") +#let fa-arrow-right-to-city = fa-icon.with("\u{e4b3}") +#let fa-arrow-right-to-line = fa-icon.with("\u{f340}") +#let fa-arrow-to-right = fa-icon.with("\u{f340}") +#let fa-arrow-rotate-left = fa-icon.with("\u{f0e2}") +#let fa-arrow-left-rotate = fa-icon.with("\u{f0e2}") +#let fa-arrow-rotate-back = fa-icon.with("\u{f0e2}") +#let fa-arrow-rotate-backward = fa-icon.with("\u{f0e2}") +#let fa-undo = fa-icon.with("\u{f0e2}") +#let fa-arrow-rotate-right = fa-icon.with("\u{f01e}") +#let fa-arrow-right-rotate = fa-icon.with("\u{f01e}") +#let fa-arrow-rotate-forward = fa-icon.with("\u{f01e}") +#let fa-redo = fa-icon.with("\u{f01e}") +#let fa-arrows-cross = fa-icon.with("\u{e0a2}") +#let fa-arrows-down-to-line = fa-icon.with("\u{e4b8}") +#let fa-arrows-down-to-people = fa-icon.with("\u{e4b9}") +#let fa-arrows-from-dotted-line = fa-icon.with("\u{e0a3}") +#let fa-arrows-from-line = fa-icon.with("\u{e0a4}") +#let fa-arrows-left-right = fa-icon.with("\u{f07e}") +#let fa-arrows-h = fa-icon.with("\u{f07e}") +#let fa-arrows-left-right-to-line = fa-icon.with("\u{e4ba}") +#let fa-arrows-maximize = fa-icon.with("\u{f31d}") +#let fa-expand-arrows = fa-icon.with("\u{f31d}") +#let fa-arrows-minimize = fa-icon.with("\u{e0a5}") +#let fa-compress-arrows = fa-icon.with("\u{e0a5}") +#let fa-arrows-repeat = fa-icon.with("\u{f364}") +#let fa-repeat-alt = fa-icon.with("\u{f364}") +#let fa-arrows-repeat-1 = fa-icon.with("\u{f366}") +#let fa-repeat-1-alt = fa-icon.with("\u{f366}") +#let fa-arrows-retweet = fa-icon.with("\u{f361}") +#let fa-retweet-alt = fa-icon.with("\u{f361}") +#let fa-arrows-rotate = fa-icon.with("\u{f021}") +#let fa-refresh = fa-icon.with("\u{f021}") +#let fa-sync = fa-icon.with("\u{f021}") +#let fa-arrows-rotate-reverse = fa-icon.with("\u{e630}") +#let fa-arrows-spin = fa-icon.with("\u{e4bb}") +#let fa-arrows-split-up-and-left = fa-icon.with("\u{e4bc}") +#let fa-arrows-to-circle = fa-icon.with("\u{e4bd}") +#let fa-arrows-to-dot = fa-icon.with("\u{e4be}") +#let fa-arrows-to-dotted-line = fa-icon.with("\u{e0a6}") +#let fa-arrows-to-eye = fa-icon.with("\u{e4bf}") +#let fa-arrows-to-line = fa-icon.with("\u{e0a7}") +#let fa-arrows-turn-right = fa-icon.with("\u{e4c0}") +#let fa-arrows-turn-to-dots = fa-icon.with("\u{e4c1}") +#let fa-arrows-up-down = fa-icon.with("\u{f07d}") +#let fa-arrows-v = fa-icon.with("\u{f07d}") +#let fa-arrows-up-down-left-right = fa-icon.with("\u{f047}") +#let fa-arrows = fa-icon.with("\u{f047}") +#let fa-arrows-up-to-line = fa-icon.with("\u{e4c2}") +#let fa-arrow-trend-down = fa-icon.with("\u{e097}") +#let fa-arrow-trend-up = fa-icon.with("\u{e098}") +#let fa-arrow-turn-down = fa-icon.with("\u{f149}") +#let fa-level-down = fa-icon.with("\u{f149}") +#let fa-arrow-turn-down-left = fa-icon.with("\u{e2e1}") +#let fa-arrow-turn-down-right = fa-icon.with("\u{e3d6}") +#let fa-arrow-turn-left = fa-icon.with("\u{e632}") +#let fa-arrow-turn-left-down = fa-icon.with("\u{e633}") +#let fa-arrow-turn-left-up = fa-icon.with("\u{e634}") +#let fa-arrow-turn-right = fa-icon.with("\u{e635}") +#let fa-arrow-turn-up = fa-icon.with("\u{f148}") +#let fa-level-up = fa-icon.with("\u{f148}") +#let fa-arrow-up = fa-icon.with("\u{f062}") +#let fa-arrow-up-1-9 = fa-icon.with("\u{f163}") +#let fa-sort-numeric-up = fa-icon.with("\u{f163}") +#let fa-arrow-up-9-1 = fa-icon.with("\u{f887}") +#let fa-sort-numeric-up-alt = fa-icon.with("\u{f887}") +#let fa-arrow-up-arrow-down = fa-icon.with("\u{e099}") +#let fa-sort-up-down = fa-icon.with("\u{e099}") +#let fa-arrow-up-a-z = fa-icon.with("\u{f15e}") +#let fa-sort-alpha-up = fa-icon.with("\u{f15e}") +#let fa-arrow-up-big-small = fa-icon.with("\u{f88e}") +#let fa-sort-size-up = fa-icon.with("\u{f88e}") +#let fa-arrow-up-from-arc = fa-icon.with("\u{e4b4}") +#let fa-arrow-up-from-bracket = fa-icon.with("\u{e09a}") +#let fa-arrow-up-from-dotted-line = fa-icon.with("\u{e09b}") +#let fa-arrow-up-from-ground-water = fa-icon.with("\u{e4b5}") +#let fa-arrow-up-from-line = fa-icon.with("\u{f342}") +#let fa-arrow-from-bottom = fa-icon.with("\u{f342}") +#let fa-arrow-up-from-square = fa-icon.with("\u{e09c}") +#let fa-arrow-up-from-water-pump = fa-icon.with("\u{e4b6}") +#let fa-arrow-up-left = fa-icon.with("\u{e09d}") +#let fa-arrow-up-left-from-circle = fa-icon.with("\u{e09e}") +#let fa-arrow-up-long = fa-icon.with("\u{f176}") +#let fa-long-arrow-up = fa-icon.with("\u{f176}") +#let fa-arrow-up-right = fa-icon.with("\u{e09f}") +#let fa-arrow-up-right-and-arrow-down-left-from-center = fa-icon.with("\u{e0a0}") +#let fa-arrow-up-right-dots = fa-icon.with("\u{e4b7}") +#let fa-arrow-up-right-from-square = fa-icon.with("\u{f08e}") +#let fa-external-link = fa-icon.with("\u{f08e}") +#let fa-arrow-up-short-wide = fa-icon.with("\u{f885}") +#let fa-sort-amount-up-alt = fa-icon.with("\u{f885}") +#let fa-arrow-up-small-big = fa-icon.with("\u{f88f}") +#let fa-sort-size-up-alt = fa-icon.with("\u{f88f}") +#let fa-arrow-up-square-triangle = fa-icon.with("\u{f88b}") +#let fa-sort-shapes-up-alt = fa-icon.with("\u{f88b}") +#let fa-arrow-up-to-arc = fa-icon.with("\u{e617}") +#let fa-arrow-up-to-bracket = fa-icon.with("\u{e66a}") +#let fa-arrow-up-to-dotted-line = fa-icon.with("\u{e0a1}") +#let fa-arrow-up-to-line = fa-icon.with("\u{f341}") +#let fa-arrow-to-top = fa-icon.with("\u{f341}") +#let fa-arrow-up-triangle-square = fa-icon.with("\u{f88a}") +#let fa-sort-shapes-up = fa-icon.with("\u{f88a}") +#let fa-arrow-up-wide-short = fa-icon.with("\u{f161}") +#let fa-sort-amount-up = fa-icon.with("\u{f161}") +#let fa-arrow-up-z-a = fa-icon.with("\u{f882}") +#let fa-sort-alpha-up-alt = fa-icon.with("\u{f882}") +#let fa-artstation = fa-icon.with("\u{f77a}") +#let fa-asterisk = fa-icon.with("\u{2a}") +#let fa-asymmetrik = fa-icon.with("\u{f372}") +#let fa-at = fa-icon.with("\u{40}") +#let fa-atlassian = fa-icon.with("\u{f77b}") +#let fa-atom = fa-icon.with("\u{f5d2}") +#let fa-atom-simple = fa-icon.with("\u{f5d3}") +#let fa-atom-alt = fa-icon.with("\u{f5d3}") +#let fa-audible = fa-icon.with("\u{f373}") +#let fa-audio-description = fa-icon.with("\u{f29e}") +#let fa-audio-description-slash = fa-icon.with("\u{e0a8}") +#let fa-austral-sign = fa-icon.with("\u{e0a9}") +#let fa-autoprefixer = fa-icon.with("\u{f41c}") +#let fa-avianex = fa-icon.with("\u{f374}") +#let fa-aviato = fa-icon.with("\u{f421}") +#let fa-avocado = fa-icon.with("\u{e0aa}") +#let fa-award = fa-icon.with("\u{f559}") +#let fa-award-simple = fa-icon.with("\u{e0ab}") +#let fa-aws = fa-icon.with("\u{f375}") +#let fa-axe = fa-icon.with("\u{f6b2}") +#let fa-axe-battle = fa-icon.with("\u{f6b3}") +#let fa-b = fa-icon.with("\u{42}") +#let fa-baby = fa-icon.with("\u{f77c}") +#let fa-baby-carriage = fa-icon.with("\u{f77d}") +#let fa-carriage-baby = fa-icon.with("\u{f77d}") +#let fa-backpack = fa-icon.with("\u{f5d4}") +#let fa-backward = fa-icon.with("\u{f04a}") +#let fa-backward-fast = fa-icon.with("\u{f049}") +#let fa-fast-backward = fa-icon.with("\u{f049}") +#let fa-backward-step = fa-icon.with("\u{f048}") +#let fa-step-backward = fa-icon.with("\u{f048}") +#let fa-bacon = fa-icon.with("\u{f7e5}") +#let fa-bacteria = fa-icon.with("\u{e059}") +#let fa-bacterium = fa-icon.with("\u{e05a}") +#let fa-badge = fa-icon.with("\u{f335}") +#let fa-badge-check = fa-icon.with("\u{f336}") +#let fa-badge-dollar = fa-icon.with("\u{f645}") +#let fa-badge-percent = fa-icon.with("\u{f646}") +#let fa-badger-honey = fa-icon.with("\u{f6b4}") +#let fa-badge-sheriff = fa-icon.with("\u{f8a2}") +#let fa-badminton = fa-icon.with("\u{e33a}") +#let fa-bagel = fa-icon.with("\u{e3d7}") +#let fa-bag-seedling = fa-icon.with("\u{e5f2}") +#let fa-bag-shopping = fa-icon.with("\u{f290}") +#let fa-shopping-bag = fa-icon.with("\u{f290}") +#let fa-bag-shopping-minus = fa-icon.with("\u{e650}") +#let fa-bag-shopping-plus = fa-icon.with("\u{e651}") +#let fa-bags-shopping = fa-icon.with("\u{f847}") +#let fa-baguette = fa-icon.with("\u{e3d8}") +#let fa-bahai = fa-icon.with("\u{f666}") +#let fa-haykal = fa-icon.with("\u{f666}") +#let fa-baht-sign = fa-icon.with("\u{e0ac}") +#let fa-balloon = fa-icon.with("\u{e2e3}") +#let fa-balloons = fa-icon.with("\u{e2e4}") +#let fa-ballot = fa-icon.with("\u{f732}") +#let fa-ballot-check = fa-icon.with("\u{f733}") +#let fa-ball-pile = fa-icon.with("\u{f77e}") +#let fa-ban = fa-icon.with("\u{f05e}") +#let fa-cancel = fa-icon.with("\u{f05e}") +#let fa-banana = fa-icon.with("\u{e2e5}") +#let fa-ban-bug = fa-icon.with("\u{f7f9}") +#let fa-debug = fa-icon.with("\u{f7f9}") +#let fa-bandage = fa-icon.with("\u{f462}") +#let fa-band-aid = fa-icon.with("\u{f462}") +#let fa-bandcamp = fa-icon.with("\u{f2d5}") +#let fa-bangladeshi-taka-sign = fa-icon.with("\u{e2e6}") +#let fa-banjo = fa-icon.with("\u{f8a3}") +#let fa-ban-parking = fa-icon.with("\u{f616}") +#let fa-parking-circle-slash = fa-icon.with("\u{f616}") +#let fa-ban-smoking = fa-icon.with("\u{f54d}") +#let fa-smoking-ban = fa-icon.with("\u{f54d}") +#let fa-barcode = fa-icon.with("\u{f02a}") +#let fa-barcode-read = fa-icon.with("\u{f464}") +#let fa-barcode-scan = fa-icon.with("\u{f465}") +#let fa-bars = fa-icon.with("\u{f0c9}") +#let fa-navicon = fa-icon.with("\u{f0c9}") +#let fa-bars-filter = fa-icon.with("\u{e0ad}") +#let fa-bars-progress = fa-icon.with("\u{f828}") +#let fa-tasks-alt = fa-icon.with("\u{f828}") +#let fa-bars-sort = fa-icon.with("\u{e0ae}") +#let fa-bars-staggered = fa-icon.with("\u{f550}") +#let fa-reorder = fa-icon.with("\u{f550}") +#let fa-stream = fa-icon.with("\u{f550}") +#let fa-baseball = fa-icon.with("\u{f433}") +#let fa-baseball-ball = fa-icon.with("\u{f433}") +#let fa-baseball-bat-ball = fa-icon.with("\u{f432}") +#let fa-basketball = fa-icon.with("\u{f434}") +#let fa-basketball-ball = fa-icon.with("\u{f434}") +#let fa-basketball-hoop = fa-icon.with("\u{f435}") +#let fa-basket-shopping = fa-icon.with("\u{f291}") +#let fa-shopping-basket = fa-icon.with("\u{f291}") +#let fa-basket-shopping-minus = fa-icon.with("\u{e652}") +#let fa-basket-shopping-plus = fa-icon.with("\u{e653}") +#let fa-basket-shopping-simple = fa-icon.with("\u{e0af}") +#let fa-shopping-basket-alt = fa-icon.with("\u{e0af}") +#let fa-bat = fa-icon.with("\u{f6b5}") +#let fa-bath = fa-icon.with("\u{f2cd}") +#let fa-bathtub = fa-icon.with("\u{f2cd}") +#let fa-battery-bolt = fa-icon.with("\u{f376}") +#let fa-battery-empty = fa-icon.with("\u{f244}") +#let fa-battery-0 = fa-icon.with("\u{f244}") +#let fa-battery-exclamation = fa-icon.with("\u{e0b0}") +#let fa-battery-full = fa-icon.with("\u{f240}") +#let fa-battery = fa-icon.with("\u{f240}") +#let fa-battery-5 = fa-icon.with("\u{f240}") +#let fa-battery-half = fa-icon.with("\u{f242}") +#let fa-battery-3 = fa-icon.with("\u{f242}") +#let fa-battery-low = fa-icon.with("\u{e0b1}") +#let fa-battery-1 = fa-icon.with("\u{e0b1}") +#let fa-battery-quarter = fa-icon.with("\u{f243}") +#let fa-battery-2 = fa-icon.with("\u{f243}") +#let fa-battery-slash = fa-icon.with("\u{f377}") +#let fa-battery-three-quarters = fa-icon.with("\u{f241}") +#let fa-battery-4 = fa-icon.with("\u{f241}") +#let fa-battle-net = fa-icon.with("\u{f835}") +#let fa-bed = fa-icon.with("\u{f236}") +#let fa-bed-bunk = fa-icon.with("\u{f8f8}") +#let fa-bed-empty = fa-icon.with("\u{f8f9}") +#let fa-bed-front = fa-icon.with("\u{f8f7}") +#let fa-bed-alt = fa-icon.with("\u{f8f7}") +#let fa-bed-pulse = fa-icon.with("\u{f487}") +#let fa-procedures = fa-icon.with("\u{f487}") +#let fa-bee = fa-icon.with("\u{e0b2}") +#let fa-beer-mug = fa-icon.with("\u{e0b3}") +#let fa-beer-foam = fa-icon.with("\u{e0b3}") +#let fa-beer-mug-empty = fa-icon.with("\u{f0fc}") +#let fa-beer = fa-icon.with("\u{f0fc}") +#let fa-behance = fa-icon.with("\u{f1b4}") +#let fa-bell = fa-icon.with("\u{f0f3}") +#let fa-bell-concierge = fa-icon.with("\u{f562}") +#let fa-concierge-bell = fa-icon.with("\u{f562}") +#let fa-bell-exclamation = fa-icon.with("\u{f848}") +#let fa-bell-on = fa-icon.with("\u{f8fa}") +#let fa-bell-plus = fa-icon.with("\u{f849}") +#let fa-bell-ring = fa-icon.with("\u{e62c}") +#let fa-bells = fa-icon.with("\u{f77f}") +#let fa-bell-school = fa-icon.with("\u{f5d5}") +#let fa-bell-school-slash = fa-icon.with("\u{f5d6}") +#let fa-bell-slash = fa-icon.with("\u{f1f6}") +#let fa-bench-tree = fa-icon.with("\u{e2e7}") +#let fa-bezier-curve = fa-icon.with("\u{f55b}") +#let fa-bicycle = fa-icon.with("\u{f206}") +#let fa-bilibili = fa-icon.with("\u{e3d9}") +#let fa-billboard = fa-icon.with("\u{e5cd}") +#let fa-bimobject = fa-icon.with("\u{f378}") +#let fa-binary = fa-icon.with("\u{e33b}") +#let fa-binary-circle-check = fa-icon.with("\u{e33c}") +#let fa-binary-lock = fa-icon.with("\u{e33d}") +#let fa-binary-slash = fa-icon.with("\u{e33e}") +#let fa-bin-bottles = fa-icon.with("\u{e5f5}") +#let fa-bin-bottles-recycle = fa-icon.with("\u{e5f6}") +#let fa-binoculars = fa-icon.with("\u{f1e5}") +#let fa-bin-recycle = fa-icon.with("\u{e5f7}") +#let fa-biohazard = fa-icon.with("\u{f780}") +#let fa-bird = fa-icon.with("\u{e469}") +#let fa-bitbucket = fa-icon.with("\u{f171}") +#let fa-bitcoin = fa-icon.with("\u{f379}") +#let fa-bitcoin-sign = fa-icon.with("\u{e0b4}") +#let fa-bity = fa-icon.with("\u{f37a}") +#let fa-blackberry = fa-icon.with("\u{f37b}") +#let fa-black-tie = fa-icon.with("\u{f27e}") +#let fa-blanket = fa-icon.with("\u{f498}") +#let fa-blanket-fire = fa-icon.with("\u{e3da}") +#let fa-blender = fa-icon.with("\u{f517}") +#let fa-blender-phone = fa-icon.with("\u{f6b6}") +#let fa-blinds = fa-icon.with("\u{f8fb}") +#let fa-blinds-open = fa-icon.with("\u{f8fc}") +#let fa-blinds-raised = fa-icon.with("\u{f8fd}") +#let fa-block = fa-icon.with("\u{e46a}") +#let fa-block-brick = fa-icon.with("\u{e3db}") +#let fa-wall-brick = fa-icon.with("\u{e3db}") +#let fa-block-brick-fire = fa-icon.with("\u{e3dc}") +#let fa-firewall = fa-icon.with("\u{e3dc}") +#let fa-block-question = fa-icon.with("\u{e3dd}") +#let fa-block-quote = fa-icon.with("\u{e0b5}") +#let fa-blog = fa-icon.with("\u{f781}") +#let fa-blogger = fa-icon.with("\u{f37c}") +#let fa-blogger-b = fa-icon.with("\u{f37d}") +#let fa-blueberries = fa-icon.with("\u{e2e8}") +#let fa-bluesky = fa-icon.with("\u{e671}") +#let fa-bluetooth = fa-icon.with("\u{f293}") +#let fa-bluetooth-b = fa-icon.with("\u{f294}") +#let fa-bold = fa-icon.with("\u{f032}") +#let fa-bolt = fa-icon.with("\u{f0e7}") +#let fa-zap = fa-icon.with("\u{f0e7}") +#let fa-bolt-auto = fa-icon.with("\u{e0b6}") +#let fa-bolt-lightning = fa-icon.with("\u{e0b7}") +#let fa-bolt-slash = fa-icon.with("\u{e0b8}") +#let fa-bomb = fa-icon.with("\u{f1e2}") +#let fa-bone = fa-icon.with("\u{f5d7}") +#let fa-bone-break = fa-icon.with("\u{f5d8}") +#let fa-bong = fa-icon.with("\u{f55c}") +#let fa-book = fa-icon.with("\u{f02d}") +#let fa-book-arrow-right = fa-icon.with("\u{e0b9}") +#let fa-book-arrow-up = fa-icon.with("\u{e0ba}") +#let fa-book-atlas = fa-icon.with("\u{f558}") +#let fa-atlas = fa-icon.with("\u{f558}") +#let fa-book-bible = fa-icon.with("\u{f647}") +#let fa-bible = fa-icon.with("\u{f647}") +#let fa-book-blank = fa-icon.with("\u{f5d9}") +#let fa-book-alt = fa-icon.with("\u{f5d9}") +#let fa-book-bookmark = fa-icon.with("\u{e0bb}") +#let fa-book-circle-arrow-right = fa-icon.with("\u{e0bc}") +#let fa-book-circle-arrow-up = fa-icon.with("\u{e0bd}") +#let fa-book-copy = fa-icon.with("\u{e0be}") +#let fa-book-font = fa-icon.with("\u{e0bf}") +#let fa-book-heart = fa-icon.with("\u{f499}") +#let fa-book-journal-whills = fa-icon.with("\u{f66a}") +#let fa-journal-whills = fa-icon.with("\u{f66a}") +#let fa-bookmark = fa-icon.with("\u{f02e}") +#let fa-bookmark-slash = fa-icon.with("\u{e0c2}") +#let fa-book-medical = fa-icon.with("\u{f7e6}") +#let fa-book-open = fa-icon.with("\u{f518}") +#let fa-book-open-cover = fa-icon.with("\u{e0c0}") +#let fa-book-open-alt = fa-icon.with("\u{e0c0}") +#let fa-book-open-reader = fa-icon.with("\u{f5da}") +#let fa-book-reader = fa-icon.with("\u{f5da}") +#let fa-book-quran = fa-icon.with("\u{f687}") +#let fa-quran = fa-icon.with("\u{f687}") +#let fa-books = fa-icon.with("\u{f5db}") +#let fa-book-section = fa-icon.with("\u{e0c1}") +#let fa-book-law = fa-icon.with("\u{e0c1}") +#let fa-book-skull = fa-icon.with("\u{f6b7}") +#let fa-book-dead = fa-icon.with("\u{f6b7}") +#let fa-books-medical = fa-icon.with("\u{f7e8}") +#let fa-book-sparkles = fa-icon.with("\u{f6b8}") +#let fa-book-spells = fa-icon.with("\u{f6b8}") +#let fa-book-tanakh = fa-icon.with("\u{f827}") +#let fa-tanakh = fa-icon.with("\u{f827}") +#let fa-book-user = fa-icon.with("\u{f7e7}") +#let fa-boombox = fa-icon.with("\u{f8a5}") +#let fa-boot = fa-icon.with("\u{f782}") +#let fa-booth-curtain = fa-icon.with("\u{f734}") +#let fa-boot-heeled = fa-icon.with("\u{e33f}") +#let fa-bootstrap = fa-icon.with("\u{f836}") +#let fa-border-all = fa-icon.with("\u{f84c}") +#let fa-border-bottom = fa-icon.with("\u{f84d}") +#let fa-border-bottom-right = fa-icon.with("\u{f854}") +#let fa-border-style-alt = fa-icon.with("\u{f854}") +#let fa-border-center-h = fa-icon.with("\u{f89c}") +#let fa-border-center-v = fa-icon.with("\u{f89d}") +#let fa-border-inner = fa-icon.with("\u{f84e}") +#let fa-border-left = fa-icon.with("\u{f84f}") +#let fa-border-none = fa-icon.with("\u{f850}") +#let fa-border-outer = fa-icon.with("\u{f851}") +#let fa-border-right = fa-icon.with("\u{f852}") +#let fa-border-top = fa-icon.with("\u{f855}") +#let fa-border-top-left = fa-icon.with("\u{f853}") +#let fa-border-style = fa-icon.with("\u{f853}") +#let fa-bore-hole = fa-icon.with("\u{e4c3}") +#let fa-bots = fa-icon.with("\u{e340}") +#let fa-bottle-baby = fa-icon.with("\u{e673}") +#let fa-bottle-droplet = fa-icon.with("\u{e4c4}") +#let fa-bottle-water = fa-icon.with("\u{e4c5}") +#let fa-bow-arrow = fa-icon.with("\u{f6b9}") +#let fa-bowl-chopsticks = fa-icon.with("\u{e2e9}") +#let fa-bowl-chopsticks-noodles = fa-icon.with("\u{e2ea}") +#let fa-bowl-food = fa-icon.with("\u{e4c6}") +#let fa-bowl-hot = fa-icon.with("\u{f823}") +#let fa-soup = fa-icon.with("\u{f823}") +#let fa-bowling-ball = fa-icon.with("\u{f436}") +#let fa-bowling-ball-pin = fa-icon.with("\u{e0c3}") +#let fa-bowling-pins = fa-icon.with("\u{f437}") +#let fa-bowl-rice = fa-icon.with("\u{e2eb}") +#let fa-bowl-scoop = fa-icon.with("\u{e3de}") +#let fa-bowl-shaved-ice = fa-icon.with("\u{e3de}") +#let fa-bowl-scoops = fa-icon.with("\u{e3df}") +#let fa-bowl-soft-serve = fa-icon.with("\u{e46b}") +#let fa-bowl-spoon = fa-icon.with("\u{e3e0}") +#let fa-box = fa-icon.with("\u{f466}") +#let fa-box-archive = fa-icon.with("\u{f187}") +#let fa-archive = fa-icon.with("\u{f187}") +#let fa-box-ballot = fa-icon.with("\u{f735}") +#let fa-box-check = fa-icon.with("\u{f467}") +#let fa-box-circle-check = fa-icon.with("\u{e0c4}") +#let fa-box-dollar = fa-icon.with("\u{f4a0}") +#let fa-box-usd = fa-icon.with("\u{f4a0}") +#let fa-boxes-packing = fa-icon.with("\u{e4c7}") +#let fa-boxes-stacked = fa-icon.with("\u{f468}") +#let fa-boxes = fa-icon.with("\u{f468}") +#let fa-boxes-alt = fa-icon.with("\u{f468}") +#let fa-box-heart = fa-icon.with("\u{f49d}") +#let fa-boxing-glove = fa-icon.with("\u{f438}") +#let fa-glove-boxing = fa-icon.with("\u{f438}") +#let fa-box-open = fa-icon.with("\u{f49e}") +#let fa-box-open-full = fa-icon.with("\u{f49c}") +#let fa-box-full = fa-icon.with("\u{f49c}") +#let fa-box-taped = fa-icon.with("\u{f49a}") +#let fa-box-alt = fa-icon.with("\u{f49a}") +#let fa-box-tissue = fa-icon.with("\u{e05b}") +#let fa-bracket-curly = fa-icon.with("\u{7b}") +#let fa-bracket-curly-left = fa-icon.with("\u{7b}") +#let fa-bracket-curly-right = fa-icon.with("\u{7d}") +#let fa-bracket-round = fa-icon.with("\u{28}") +#let fa-parenthesis = fa-icon.with("\u{28}") +#let fa-bracket-round-right = fa-icon.with("\u{29}") +#let fa-brackets-curly = fa-icon.with("\u{f7ea}") +#let fa-bracket-square = fa-icon.with("\u{5b}") +#let fa-bracket = fa-icon.with("\u{5b}") +#let fa-bracket-left = fa-icon.with("\u{5b}") +#let fa-bracket-square-right = fa-icon.with("\u{5d}") +#let fa-brackets-round = fa-icon.with("\u{e0c5}") +#let fa-parentheses = fa-icon.with("\u{e0c5}") +#let fa-brackets-square = fa-icon.with("\u{f7e9}") +#let fa-brackets = fa-icon.with("\u{f7e9}") +#let fa-braille = fa-icon.with("\u{f2a1}") +#let fa-brain = fa-icon.with("\u{f5dc}") +#let fa-brain-arrow-curved-right = fa-icon.with("\u{f677}") +#let fa-mind-share = fa-icon.with("\u{f677}") +#let fa-brain-circuit = fa-icon.with("\u{e0c6}") +#let fa-brake-warning = fa-icon.with("\u{e0c7}") +#let fa-brave = fa-icon.with("\u{e63c}") +#let fa-brave-reverse = fa-icon.with("\u{e63d}") +#let fa-brazilian-real-sign = fa-icon.with("\u{e46c}") +#let fa-bread-loaf = fa-icon.with("\u{f7eb}") +#let fa-bread-slice = fa-icon.with("\u{f7ec}") +#let fa-bread-slice-butter = fa-icon.with("\u{e3e1}") +#let fa-bridge = fa-icon.with("\u{e4c8}") +#let fa-bridge-circle-check = fa-icon.with("\u{e4c9}") +#let fa-bridge-circle-exclamation = fa-icon.with("\u{e4ca}") +#let fa-bridge-circle-xmark = fa-icon.with("\u{e4cb}") +#let fa-bridge-lock = fa-icon.with("\u{e4cc}") +#let fa-bridge-suspension = fa-icon.with("\u{e4cd}") +#let fa-bridge-water = fa-icon.with("\u{e4ce}") +#let fa-briefcase = fa-icon.with("\u{f0b1}") +#let fa-briefcase-arrow-right = fa-icon.with("\u{e2f2}") +#let fa-briefcase-blank = fa-icon.with("\u{e0c8}") +#let fa-briefcase-medical = fa-icon.with("\u{f469}") +#let fa-brightness = fa-icon.with("\u{e0c9}") +#let fa-brightness-low = fa-icon.with("\u{e0ca}") +#let fa-bring-forward = fa-icon.with("\u{f856}") +#let fa-bring-front = fa-icon.with("\u{f857}") +#let fa-broccoli = fa-icon.with("\u{e3e2}") +#let fa-broom = fa-icon.with("\u{f51a}") +#let fa-broom-ball = fa-icon.with("\u{f458}") +#let fa-quidditch = fa-icon.with("\u{f458}") +#let fa-quidditch-broom-ball = fa-icon.with("\u{f458}") +#let fa-broom-wide = fa-icon.with("\u{e5d1}") +#let fa-browser = fa-icon.with("\u{f37e}") +#let fa-browsers = fa-icon.with("\u{e0cb}") +#let fa-brush = fa-icon.with("\u{f55d}") +#let fa-btc = fa-icon.with("\u{f15a}") +#let fa-bucket = fa-icon.with("\u{e4cf}") +#let fa-buffer = fa-icon.with("\u{f837}") +#let fa-bug = fa-icon.with("\u{f188}") +#let fa-bugs = fa-icon.with("\u{e4d0}") +#let fa-bug-slash = fa-icon.with("\u{e490}") +#let fa-building = fa-icon.with("\u{f1ad}") +#let fa-building-circle-arrow-right = fa-icon.with("\u{e4d1}") +#let fa-building-circle-check = fa-icon.with("\u{e4d2}") +#let fa-building-circle-exclamation = fa-icon.with("\u{e4d3}") +#let fa-building-circle-xmark = fa-icon.with("\u{e4d4}") +#let fa-building-columns = fa-icon.with("\u{f19c}") +#let fa-bank = fa-icon.with("\u{f19c}") +#let fa-institution = fa-icon.with("\u{f19c}") +#let fa-museum = fa-icon.with("\u{f19c}") +#let fa-university = fa-icon.with("\u{f19c}") +#let fa-building-flag = fa-icon.with("\u{e4d5}") +#let fa-building-lock = fa-icon.with("\u{e4d6}") +#let fa-building-magnifying-glass = fa-icon.with("\u{e61c}") +#let fa-building-memo = fa-icon.with("\u{e61e}") +#let fa-building-ngo = fa-icon.with("\u{e4d7}") +#let fa-buildings = fa-icon.with("\u{e0cc}") +#let fa-building-shield = fa-icon.with("\u{e4d8}") +#let fa-building-un = fa-icon.with("\u{e4d9}") +#let fa-building-user = fa-icon.with("\u{e4da}") +#let fa-building-wheat = fa-icon.with("\u{e4db}") +#let fa-bulldozer = fa-icon.with("\u{e655}") +#let fa-bullhorn = fa-icon.with("\u{f0a1}") +#let fa-bullseye = fa-icon.with("\u{f140}") +#let fa-bullseye-arrow = fa-icon.with("\u{f648}") +#let fa-bullseye-pointer = fa-icon.with("\u{f649}") +#let fa-buoy = fa-icon.with("\u{e5b5}") +#let fa-buoy-mooring = fa-icon.with("\u{e5b6}") +#let fa-burger = fa-icon.with("\u{f805}") +#let fa-hamburger = fa-icon.with("\u{f805}") +#let fa-burger-cheese = fa-icon.with("\u{f7f1}") +#let fa-cheeseburger = fa-icon.with("\u{f7f1}") +#let fa-burger-fries = fa-icon.with("\u{e0cd}") +#let fa-burger-glass = fa-icon.with("\u{e0ce}") +#let fa-burger-lettuce = fa-icon.with("\u{e3e3}") +#let fa-burger-soda = fa-icon.with("\u{f858}") +#let fa-buromobelexperte = fa-icon.with("\u{f37f}") +#let fa-burrito = fa-icon.with("\u{f7ed}") +#let fa-burst = fa-icon.with("\u{e4dc}") +#let fa-bus = fa-icon.with("\u{f207}") +#let fa-business-time = fa-icon.with("\u{f64a}") +#let fa-briefcase-clock = fa-icon.with("\u{f64a}") +#let fa-bus-school = fa-icon.with("\u{f5dd}") +#let fa-bus-simple = fa-icon.with("\u{f55e}") +#let fa-bus-alt = fa-icon.with("\u{f55e}") +#let fa-butter = fa-icon.with("\u{e3e4}") +#let fa-buy-n-large = fa-icon.with("\u{f8a6}") +#let fa-buysellads = fa-icon.with("\u{f20d}") +#let fa-c = fa-icon.with("\u{43}") +#let fa-cabin = fa-icon.with("\u{e46d}") +#let fa-cabinet-filing = fa-icon.with("\u{f64b}") +#let fa-cable-car = fa-icon.with("\u{f7da}") +#let fa-tram = fa-icon.with("\u{f7da}") +#let fa-cactus = fa-icon.with("\u{f8a7}") +#let fa-caduceus = fa-icon.with("\u{e681}") +#let fa-cake-candles = fa-icon.with("\u{f1fd}") +#let fa-birthday-cake = fa-icon.with("\u{f1fd}") +#let fa-cake = fa-icon.with("\u{f1fd}") +#let fa-cake-slice = fa-icon.with("\u{e3e5}") +#let fa-shortcake = fa-icon.with("\u{e3e5}") +#let fa-calculator = fa-icon.with("\u{f1ec}") +#let fa-calculator-simple = fa-icon.with("\u{f64c}") +#let fa-calculator-alt = fa-icon.with("\u{f64c}") +#let fa-calendar = fa-icon.with("\u{f133}") +#let fa-calendar-arrow-down = fa-icon.with("\u{e0d0}") +#let fa-calendar-download = fa-icon.with("\u{e0d0}") +#let fa-calendar-arrow-up = fa-icon.with("\u{e0d1}") +#let fa-calendar-upload = fa-icon.with("\u{e0d1}") +#let fa-calendar-check = fa-icon.with("\u{f274}") +#let fa-calendar-circle-exclamation = fa-icon.with("\u{e46e}") +#let fa-calendar-circle-minus = fa-icon.with("\u{e46f}") +#let fa-calendar-circle-plus = fa-icon.with("\u{e470}") +#let fa-calendar-circle-user = fa-icon.with("\u{e471}") +#let fa-calendar-clock = fa-icon.with("\u{e0d2}") +#let fa-calendar-time = fa-icon.with("\u{e0d2}") +#let fa-calendar-day = fa-icon.with("\u{f783}") +#let fa-calendar-days = fa-icon.with("\u{f073}") +#let fa-calendar-alt = fa-icon.with("\u{f073}") +#let fa-calendar-exclamation = fa-icon.with("\u{f334}") +#let fa-calendar-heart = fa-icon.with("\u{e0d3}") +#let fa-calendar-image = fa-icon.with("\u{e0d4}") +#let fa-calendar-lines = fa-icon.with("\u{e0d5}") +#let fa-calendar-note = fa-icon.with("\u{e0d5}") +#let fa-calendar-lines-pen = fa-icon.with("\u{e472}") +#let fa-calendar-minus = fa-icon.with("\u{f272}") +#let fa-calendar-pen = fa-icon.with("\u{f333}") +#let fa-calendar-edit = fa-icon.with("\u{f333}") +#let fa-calendar-plus = fa-icon.with("\u{f271}") +#let fa-calendar-range = fa-icon.with("\u{e0d6}") +#let fa-calendars = fa-icon.with("\u{e0d7}") +#let fa-calendar-star = fa-icon.with("\u{f736}") +#let fa-calendar-users = fa-icon.with("\u{e5e2}") +#let fa-calendar-week = fa-icon.with("\u{f784}") +#let fa-calendar-xmark = fa-icon.with("\u{f273}") +#let fa-calendar-times = fa-icon.with("\u{f273}") +#let fa-camcorder = fa-icon.with("\u{f8a8}") +#let fa-video-handheld = fa-icon.with("\u{f8a8}") +#let fa-camera = fa-icon.with("\u{f030}") +#let fa-camera-alt = fa-icon.with("\u{f030}") +#let fa-camera-cctv = fa-icon.with("\u{f8ac}") +#let fa-cctv = fa-icon.with("\u{f8ac}") +#let fa-camera-movie = fa-icon.with("\u{f8a9}") +#let fa-camera-polaroid = fa-icon.with("\u{f8aa}") +#let fa-camera-retro = fa-icon.with("\u{f083}") +#let fa-camera-rotate = fa-icon.with("\u{e0d8}") +#let fa-camera-security = fa-icon.with("\u{f8fe}") +#let fa-camera-home = fa-icon.with("\u{f8fe}") +#let fa-camera-slash = fa-icon.with("\u{e0d9}") +#let fa-camera-viewfinder = fa-icon.with("\u{e0da}") +#let fa-screenshot = fa-icon.with("\u{e0da}") +#let fa-camera-web = fa-icon.with("\u{f832}") +#let fa-webcam = fa-icon.with("\u{f832}") +#let fa-camera-web-slash = fa-icon.with("\u{f833}") +#let fa-webcam-slash = fa-icon.with("\u{f833}") +#let fa-campfire = fa-icon.with("\u{f6ba}") +#let fa-campground = fa-icon.with("\u{f6bb}") +#let fa-canadian-maple-leaf = fa-icon.with("\u{f785}") +#let fa-candle-holder = fa-icon.with("\u{f6bc}") +#let fa-candy = fa-icon.with("\u{e3e7}") +#let fa-candy-bar = fa-icon.with("\u{e3e8}") +#let fa-chocolate-bar = fa-icon.with("\u{e3e8}") +#let fa-candy-cane = fa-icon.with("\u{f786}") +#let fa-candy-corn = fa-icon.with("\u{f6bd}") +#let fa-can-food = fa-icon.with("\u{e3e6}") +#let fa-cannabis = fa-icon.with("\u{f55f}") +#let fa-cannon = fa-icon.with("\u{e642}") +#let fa-capsules = fa-icon.with("\u{f46b}") +#let fa-car = fa-icon.with("\u{f1b9}") +#let fa-automobile = fa-icon.with("\u{f1b9}") +#let fa-caravan = fa-icon.with("\u{f8ff}") +#let fa-caravan-simple = fa-icon.with("\u{e000}") +#let fa-caravan-alt = fa-icon.with("\u{e000}") +#let fa-car-battery = fa-icon.with("\u{f5df}") +#let fa-battery-car = fa-icon.with("\u{f5df}") +#let fa-car-bolt = fa-icon.with("\u{e341}") +#let fa-car-building = fa-icon.with("\u{f859}") +#let fa-car-bump = fa-icon.with("\u{f5e0}") +#let fa-car-burst = fa-icon.with("\u{f5e1}") +#let fa-car-crash = fa-icon.with("\u{f5e1}") +#let fa-car-bus = fa-icon.with("\u{f85a}") +#let fa-car-circle-bolt = fa-icon.with("\u{e342}") +#let fa-card-club = fa-icon.with("\u{e3e9}") +#let fa-card-diamond = fa-icon.with("\u{e3ea}") +#let fa-card-heart = fa-icon.with("\u{e3eb}") +#let fa-cards = fa-icon.with("\u{e3ed}") +#let fa-cards-blank = fa-icon.with("\u{e4df}") +#let fa-card-spade = fa-icon.with("\u{e3ec}") +#let fa-caret-down = fa-icon.with("\u{f0d7}") +#let fa-caret-left = fa-icon.with("\u{f0d9}") +#let fa-caret-right = fa-icon.with("\u{f0da}") +#let fa-caret-up = fa-icon.with("\u{f0d8}") +#let fa-car-garage = fa-icon.with("\u{f5e2}") +#let fa-car-mirrors = fa-icon.with("\u{e343}") +#let fa-car-on = fa-icon.with("\u{e4dd}") +#let fa-car-rear = fa-icon.with("\u{f5de}") +#let fa-car-alt = fa-icon.with("\u{f5de}") +#let fa-carrot = fa-icon.with("\u{f787}") +#let fa-cars = fa-icon.with("\u{f85b}") +#let fa-car-side = fa-icon.with("\u{f5e4}") +#let fa-car-side-bolt = fa-icon.with("\u{e344}") +#let fa-cart-arrow-down = fa-icon.with("\u{f218}") +#let fa-cart-arrow-up = fa-icon.with("\u{e3ee}") +#let fa-cart-circle-arrow-down = fa-icon.with("\u{e3ef}") +#let fa-cart-circle-arrow-up = fa-icon.with("\u{e3f0}") +#let fa-cart-circle-check = fa-icon.with("\u{e3f1}") +#let fa-cart-circle-exclamation = fa-icon.with("\u{e3f2}") +#let fa-cart-circle-plus = fa-icon.with("\u{e3f3}") +#let fa-cart-circle-xmark = fa-icon.with("\u{e3f4}") +#let fa-cart-flatbed = fa-icon.with("\u{f474}") +#let fa-dolly-flatbed = fa-icon.with("\u{f474}") +#let fa-cart-flatbed-boxes = fa-icon.with("\u{f475}") +#let fa-dolly-flatbed-alt = fa-icon.with("\u{f475}") +#let fa-cart-flatbed-empty = fa-icon.with("\u{f476}") +#let fa-dolly-flatbed-empty = fa-icon.with("\u{f476}") +#let fa-cart-flatbed-suitcase = fa-icon.with("\u{f59d}") +#let fa-luggage-cart = fa-icon.with("\u{f59d}") +#let fa-car-tilt = fa-icon.with("\u{f5e5}") +#let fa-cart-minus = fa-icon.with("\u{e0db}") +#let fa-cart-plus = fa-icon.with("\u{f217}") +#let fa-cart-shopping = fa-icon.with("\u{f07a}") +#let fa-shopping-cart = fa-icon.with("\u{f07a}") +#let fa-cart-shopping-fast = fa-icon.with("\u{e0dc}") +#let fa-car-tunnel = fa-icon.with("\u{e4de}") +#let fa-cart-xmark = fa-icon.with("\u{e0dd}") +#let fa-car-wash = fa-icon.with("\u{f5e6}") +#let fa-car-wrench = fa-icon.with("\u{f5e3}") +#let fa-car-mechanic = fa-icon.with("\u{f5e3}") +#let fa-cash-register = fa-icon.with("\u{f788}") +#let fa-cassette-betamax = fa-icon.with("\u{f8a4}") +#let fa-betamax = fa-icon.with("\u{f8a4}") +#let fa-cassette-tape = fa-icon.with("\u{f8ab}") +#let fa-cassette-vhs = fa-icon.with("\u{f8ec}") +#let fa-vhs = fa-icon.with("\u{f8ec}") +#let fa-castle = fa-icon.with("\u{e0de}") +#let fa-cat = fa-icon.with("\u{f6be}") +#let fa-cat-space = fa-icon.with("\u{e001}") +#let fa-cauldron = fa-icon.with("\u{f6bf}") +#let fa-cc-amazon-pay = fa-icon.with("\u{f42d}") +#let fa-cc-amex = fa-icon.with("\u{f1f3}") +#let fa-cc-apple-pay = fa-icon.with("\u{f416}") +#let fa-cc-diners-club = fa-icon.with("\u{f24c}") +#let fa-cc-discover = fa-icon.with("\u{f1f2}") +#let fa-cc-jcb = fa-icon.with("\u{f24b}") +#let fa-cc-mastercard = fa-icon.with("\u{f1f1}") +#let fa-cc-paypal = fa-icon.with("\u{f1f4}") +#let fa-cc-stripe = fa-icon.with("\u{f1f5}") +#let fa-cc-visa = fa-icon.with("\u{f1f0}") +#let fa-cedi-sign = fa-icon.with("\u{e0df}") +#let fa-centercode = fa-icon.with("\u{f380}") +#let fa-centos = fa-icon.with("\u{f789}") +#let fa-cent-sign = fa-icon.with("\u{e3f5}") +#let fa-certificate = fa-icon.with("\u{f0a3}") +#let fa-chair = fa-icon.with("\u{f6c0}") +#let fa-chair-office = fa-icon.with("\u{f6c1}") +#let fa-chalkboard = fa-icon.with("\u{f51b}") +#let fa-blackboard = fa-icon.with("\u{f51b}") +#let fa-chalkboard-user = fa-icon.with("\u{f51c}") +#let fa-chalkboard-teacher = fa-icon.with("\u{f51c}") +#let fa-champagne-glass = fa-icon.with("\u{f79e}") +#let fa-glass-champagne = fa-icon.with("\u{f79e}") +#let fa-champagne-glasses = fa-icon.with("\u{f79f}") +#let fa-glass-cheers = fa-icon.with("\u{f79f}") +#let fa-charging-station = fa-icon.with("\u{f5e7}") +#let fa-chart-area = fa-icon.with("\u{f1fe}") +#let fa-area-chart = fa-icon.with("\u{f1fe}") +#let fa-chart-bar = fa-icon.with("\u{f080}") +#let fa-bar-chart = fa-icon.with("\u{f080}") +#let fa-chart-bullet = fa-icon.with("\u{e0e1}") +#let fa-chart-candlestick = fa-icon.with("\u{e0e2}") +#let fa-chart-column = fa-icon.with("\u{e0e3}") +#let fa-chart-gantt = fa-icon.with("\u{e0e4}") +#let fa-chart-kanban = fa-icon.with("\u{e64f}") +#let fa-chart-line = fa-icon.with("\u{f201}") +#let fa-line-chart = fa-icon.with("\u{f201}") +#let fa-chart-line-down = fa-icon.with("\u{f64d}") +#let fa-chart-line-up = fa-icon.with("\u{e0e5}") +#let fa-chart-line-up-down = fa-icon.with("\u{e5d7}") +#let fa-chart-mixed = fa-icon.with("\u{f643}") +#let fa-analytics = fa-icon.with("\u{f643}") +#let fa-chart-mixed-up-circle-currency = fa-icon.with("\u{e5d8}") +#let fa-chart-mixed-up-circle-dollar = fa-icon.with("\u{e5d9}") +#let fa-chart-network = fa-icon.with("\u{f78a}") +#let fa-chart-pie = fa-icon.with("\u{f200}") +#let fa-pie-chart = fa-icon.with("\u{f200}") +#let fa-chart-pie-simple = fa-icon.with("\u{f64e}") +#let fa-chart-pie-alt = fa-icon.with("\u{f64e}") +#let fa-chart-pie-simple-circle-currency = fa-icon.with("\u{e604}") +#let fa-chart-pie-simple-circle-dollar = fa-icon.with("\u{e605}") +#let fa-chart-pyramid = fa-icon.with("\u{e0e6}") +#let fa-chart-radar = fa-icon.with("\u{e0e7}") +#let fa-chart-scatter = fa-icon.with("\u{f7ee}") +#let fa-chart-scatter-3d = fa-icon.with("\u{e0e8}") +#let fa-chart-scatter-bubble = fa-icon.with("\u{e0e9}") +#let fa-chart-simple = fa-icon.with("\u{e473}") +#let fa-chart-simple-horizontal = fa-icon.with("\u{e474}") +#let fa-chart-tree-map = fa-icon.with("\u{e0ea}") +#let fa-chart-user = fa-icon.with("\u{f6a3}") +#let fa-user-chart = fa-icon.with("\u{f6a3}") +#let fa-chart-waterfall = fa-icon.with("\u{e0eb}") +#let fa-check = fa-icon.with("\u{f00c}") +#let fa-check-double = fa-icon.with("\u{f560}") +#let fa-check-to-slot = fa-icon.with("\u{f772}") +#let fa-vote-yea = fa-icon.with("\u{f772}") +#let fa-cheese = fa-icon.with("\u{f7ef}") +#let fa-cheese-swiss = fa-icon.with("\u{f7f0}") +#let fa-cherries = fa-icon.with("\u{e0ec}") +#let fa-chess = fa-icon.with("\u{f439}") +#let fa-chess-bishop = fa-icon.with("\u{f43a}") +#let fa-chess-bishop-piece = fa-icon.with("\u{f43b}") +#let fa-chess-bishop-alt = fa-icon.with("\u{f43b}") +#let fa-chess-board = fa-icon.with("\u{f43c}") +#let fa-chess-clock = fa-icon.with("\u{f43d}") +#let fa-chess-clock-flip = fa-icon.with("\u{f43e}") +#let fa-chess-clock-alt = fa-icon.with("\u{f43e}") +#let fa-chess-king = fa-icon.with("\u{f43f}") +#let fa-chess-king-piece = fa-icon.with("\u{f440}") +#let fa-chess-king-alt = fa-icon.with("\u{f440}") +#let fa-chess-knight = fa-icon.with("\u{f441}") +#let fa-chess-knight-piece = fa-icon.with("\u{f442}") +#let fa-chess-knight-alt = fa-icon.with("\u{f442}") +#let fa-chess-pawn = fa-icon.with("\u{f443}") +#let fa-chess-pawn-piece = fa-icon.with("\u{f444}") +#let fa-chess-pawn-alt = fa-icon.with("\u{f444}") +#let fa-chess-queen = fa-icon.with("\u{f445}") +#let fa-chess-queen-piece = fa-icon.with("\u{f446}") +#let fa-chess-queen-alt = fa-icon.with("\u{f446}") +#let fa-chess-rook = fa-icon.with("\u{f447}") +#let fa-chess-rook-piece = fa-icon.with("\u{f448}") +#let fa-chess-rook-alt = fa-icon.with("\u{f448}") +#let fa-chestnut = fa-icon.with("\u{e3f6}") +#let fa-chevron-down = fa-icon.with("\u{f078}") +#let fa-chevron-left = fa-icon.with("\u{f053}") +#let fa-chevron-right = fa-icon.with("\u{f054}") +#let fa-chevrons-down = fa-icon.with("\u{f322}") +#let fa-chevron-double-down = fa-icon.with("\u{f322}") +#let fa-chevrons-left = fa-icon.with("\u{f323}") +#let fa-chevron-double-left = fa-icon.with("\u{f323}") +#let fa-chevrons-right = fa-icon.with("\u{f324}") +#let fa-chevron-double-right = fa-icon.with("\u{f324}") +#let fa-chevrons-up = fa-icon.with("\u{f325}") +#let fa-chevron-double-up = fa-icon.with("\u{f325}") +#let fa-chevron-up = fa-icon.with("\u{f077}") +#let fa-chf-sign = fa-icon.with("\u{e602}") +#let fa-child = fa-icon.with("\u{f1ae}") +#let fa-child-combatant = fa-icon.with("\u{e4e0}") +#let fa-child-rifle = fa-icon.with("\u{e4e0}") +#let fa-child-dress = fa-icon.with("\u{e59c}") +#let fa-child-reaching = fa-icon.with("\u{e59d}") +#let fa-children = fa-icon.with("\u{e4e1}") +#let fa-chimney = fa-icon.with("\u{f78b}") +#let fa-chopsticks = fa-icon.with("\u{e3f7}") +#let fa-chrome = fa-icon.with("\u{f268}") +#let fa-chromecast = fa-icon.with("\u{f838}") +#let fa-church = fa-icon.with("\u{f51d}") +#let fa-circle = fa-icon.with("\u{f111}") +#let fa-circle-0 = fa-icon.with("\u{e0ed}") +#let fa-circle-1 = fa-icon.with("\u{e0ee}") +#let fa-circle-2 = fa-icon.with("\u{e0ef}") +#let fa-circle-3 = fa-icon.with("\u{e0f0}") +#let fa-circle-4 = fa-icon.with("\u{e0f1}") +#let fa-circle-5 = fa-icon.with("\u{e0f2}") +#let fa-circle-6 = fa-icon.with("\u{e0f3}") +#let fa-circle-7 = fa-icon.with("\u{e0f4}") +#let fa-circle-8 = fa-icon.with("\u{e0f5}") +#let fa-circle-9 = fa-icon.with("\u{e0f6}") +#let fa-circle-a = fa-icon.with("\u{e0f7}") +#let fa-circle-ampersand = fa-icon.with("\u{e0f8}") +#let fa-circle-arrow-down = fa-icon.with("\u{f0ab}") +#let fa-arrow-circle-down = fa-icon.with("\u{f0ab}") +#let fa-circle-arrow-down-left = fa-icon.with("\u{e0f9}") +#let fa-circle-arrow-down-right = fa-icon.with("\u{e0fa}") +#let fa-circle-arrow-left = fa-icon.with("\u{f0a8}") +#let fa-arrow-circle-left = fa-icon.with("\u{f0a8}") +#let fa-circle-arrow-right = fa-icon.with("\u{f0a9}") +#let fa-arrow-circle-right = fa-icon.with("\u{f0a9}") +#let fa-circle-arrow-up = fa-icon.with("\u{f0aa}") +#let fa-arrow-circle-up = fa-icon.with("\u{f0aa}") +#let fa-circle-arrow-up-left = fa-icon.with("\u{e0fb}") +#let fa-circle-arrow-up-right = fa-icon.with("\u{e0fc}") +#let fa-circle-b = fa-icon.with("\u{e0fd}") +#let fa-circle-bolt = fa-icon.with("\u{e0fe}") +#let fa-circle-bookmark = fa-icon.with("\u{e100}") +#let fa-bookmark-circle = fa-icon.with("\u{e100}") +#let fa-circle-book-open = fa-icon.with("\u{e0ff}") +#let fa-book-circle = fa-icon.with("\u{e0ff}") +#let fa-circle-c = fa-icon.with("\u{e101}") +#let fa-circle-calendar = fa-icon.with("\u{e102}") +#let fa-calendar-circle = fa-icon.with("\u{e102}") +#let fa-circle-camera = fa-icon.with("\u{e103}") +#let fa-camera-circle = fa-icon.with("\u{e103}") +#let fa-circle-caret-down = fa-icon.with("\u{f32d}") +#let fa-caret-circle-down = fa-icon.with("\u{f32d}") +#let fa-circle-caret-left = fa-icon.with("\u{f32e}") +#let fa-caret-circle-left = fa-icon.with("\u{f32e}") +#let fa-circle-caret-right = fa-icon.with("\u{f330}") +#let fa-caret-circle-right = fa-icon.with("\u{f330}") +#let fa-circle-caret-up = fa-icon.with("\u{f331}") +#let fa-caret-circle-up = fa-icon.with("\u{f331}") +#let fa-circle-check = fa-icon.with("\u{f058}") +#let fa-check-circle = fa-icon.with("\u{f058}") +#let fa-circle-chevron-down = fa-icon.with("\u{f13a}") +#let fa-chevron-circle-down = fa-icon.with("\u{f13a}") +#let fa-circle-chevron-left = fa-icon.with("\u{f137}") +#let fa-chevron-circle-left = fa-icon.with("\u{f137}") +#let fa-circle-chevron-right = fa-icon.with("\u{f138}") +#let fa-chevron-circle-right = fa-icon.with("\u{f138}") +#let fa-circle-chevron-up = fa-icon.with("\u{f139}") +#let fa-chevron-circle-up = fa-icon.with("\u{f139}") +#let fa-circle-d = fa-icon.with("\u{e104}") +#let fa-circle-dashed = fa-icon.with("\u{e105}") +#let fa-circle-divide = fa-icon.with("\u{e106}") +#let fa-circle-dollar = fa-icon.with("\u{f2e8}") +#let fa-dollar-circle = fa-icon.with("\u{f2e8}") +#let fa-usd-circle = fa-icon.with("\u{f2e8}") +#let fa-circle-dollar-to-slot = fa-icon.with("\u{f4b9}") +#let fa-donate = fa-icon.with("\u{f4b9}") +#let fa-circle-dot = fa-icon.with("\u{f192}") +#let fa-dot-circle = fa-icon.with("\u{f192}") +#let fa-circle-down = fa-icon.with("\u{f358}") +#let fa-arrow-alt-circle-down = fa-icon.with("\u{f358}") +#let fa-circle-down-left = fa-icon.with("\u{e107}") +#let fa-circle-down-right = fa-icon.with("\u{e108}") +#let fa-circle-e = fa-icon.with("\u{e109}") +#let fa-circle-ellipsis = fa-icon.with("\u{e10a}") +#let fa-circle-ellipsis-vertical = fa-icon.with("\u{e10b}") +#let fa-circle-envelope = fa-icon.with("\u{e10c}") +#let fa-envelope-circle = fa-icon.with("\u{e10c}") +#let fa-circle-euro = fa-icon.with("\u{e5ce}") +#let fa-circle-exclamation = fa-icon.with("\u{f06a}") +#let fa-exclamation-circle = fa-icon.with("\u{f06a}") +#let fa-circle-exclamation-check = fa-icon.with("\u{e10d}") +#let fa-circle-f = fa-icon.with("\u{e10e}") +#let fa-circle-g = fa-icon.with("\u{e10f}") +#let fa-circle-gf = fa-icon.with("\u{e67f}") +#let fa-circle-h = fa-icon.with("\u{f47e}") +#let fa-hospital-symbol = fa-icon.with("\u{f47e}") +#let fa-circle-half = fa-icon.with("\u{e110}") +#let fa-circle-half-stroke = fa-icon.with("\u{f042}") +#let fa-adjust = fa-icon.with("\u{f042}") +#let fa-circle-heart = fa-icon.with("\u{f4c7}") +#let fa-heart-circle = fa-icon.with("\u{f4c7}") +#let fa-circle-i = fa-icon.with("\u{e111}") +#let fa-circle-info = fa-icon.with("\u{f05a}") +#let fa-info-circle = fa-icon.with("\u{f05a}") +#let fa-circle-j = fa-icon.with("\u{e112}") +#let fa-circle-k = fa-icon.with("\u{e113}") +#let fa-circle-l = fa-icon.with("\u{e114}") +#let fa-circle-left = fa-icon.with("\u{f359}") +#let fa-arrow-alt-circle-left = fa-icon.with("\u{f359}") +#let fa-circle-location-arrow = fa-icon.with("\u{f602}") +#let fa-location-circle = fa-icon.with("\u{f602}") +#let fa-circle-m = fa-icon.with("\u{e115}") +#let fa-circle-microphone = fa-icon.with("\u{e116}") +#let fa-microphone-circle = fa-icon.with("\u{e116}") +#let fa-circle-microphone-lines = fa-icon.with("\u{e117}") +#let fa-microphone-circle-alt = fa-icon.with("\u{e117}") +#let fa-circle-minus = fa-icon.with("\u{f056}") +#let fa-minus-circle = fa-icon.with("\u{f056}") +#let fa-circle-n = fa-icon.with("\u{e118}") +#let fa-circle-nodes = fa-icon.with("\u{e4e2}") +#let fa-circle-notch = fa-icon.with("\u{f1ce}") +#let fa-circle-o = fa-icon.with("\u{e119}") +#let fa-circle-p = fa-icon.with("\u{e11a}") +#let fa-circle-parking = fa-icon.with("\u{f615}") +#let fa-parking-circle = fa-icon.with("\u{f615}") +#let fa-circle-pause = fa-icon.with("\u{f28b}") +#let fa-pause-circle = fa-icon.with("\u{f28b}") +#let fa-circle-phone = fa-icon.with("\u{e11b}") +#let fa-phone-circle = fa-icon.with("\u{e11b}") +#let fa-circle-phone-flip = fa-icon.with("\u{e11c}") +#let fa-phone-circle-alt = fa-icon.with("\u{e11c}") +#let fa-circle-phone-hangup = fa-icon.with("\u{e11d}") +#let fa-phone-circle-down = fa-icon.with("\u{e11d}") +#let fa-circle-play = fa-icon.with("\u{f144}") +#let fa-play-circle = fa-icon.with("\u{f144}") +#let fa-circle-plus = fa-icon.with("\u{f055}") +#let fa-plus-circle = fa-icon.with("\u{f055}") +#let fa-circle-q = fa-icon.with("\u{e11e}") +#let fa-circle-quarter = fa-icon.with("\u{e11f}") +#let fa-circle-quarters = fa-icon.with("\u{e3f8}") +#let fa-circle-quarter-stroke = fa-icon.with("\u{e5d3}") +#let fa-circle-question = fa-icon.with("\u{f059}") +#let fa-question-circle = fa-icon.with("\u{f059}") +#let fa-circle-r = fa-icon.with("\u{e120}") +#let fa-circle-radiation = fa-icon.with("\u{f7ba}") +#let fa-radiation-alt = fa-icon.with("\u{f7ba}") +#let fa-circle-right = fa-icon.with("\u{f35a}") +#let fa-arrow-alt-circle-right = fa-icon.with("\u{f35a}") +#let fa-circle-s = fa-icon.with("\u{e121}") +#let fa-circle-small = fa-icon.with("\u{e122}") +#let fa-circle-sort = fa-icon.with("\u{e030}") +#let fa-sort-circle = fa-icon.with("\u{e030}") +#let fa-circle-sort-down = fa-icon.with("\u{e031}") +#let fa-sort-circle-down = fa-icon.with("\u{e031}") +#let fa-circle-sort-up = fa-icon.with("\u{e032}") +#let fa-sort-circle-up = fa-icon.with("\u{e032}") +#let fa-circles-overlap = fa-icon.with("\u{e600}") +#let fa-circle-star = fa-icon.with("\u{e123}") +#let fa-star-circle = fa-icon.with("\u{e123}") +#let fa-circle-sterling = fa-icon.with("\u{e5cf}") +#let fa-circle-stop = fa-icon.with("\u{f28d}") +#let fa-stop-circle = fa-icon.with("\u{f28d}") +#let fa-circle-t = fa-icon.with("\u{e124}") +#let fa-circle-three-quarters = fa-icon.with("\u{e125}") +#let fa-circle-three-quarters-stroke = fa-icon.with("\u{e5d4}") +#let fa-circle-trash = fa-icon.with("\u{e126}") +#let fa-trash-circle = fa-icon.with("\u{e126}") +#let fa-circle-u = fa-icon.with("\u{e127}") +#let fa-circle-up = fa-icon.with("\u{f35b}") +#let fa-arrow-alt-circle-up = fa-icon.with("\u{f35b}") +#let fa-circle-up-left = fa-icon.with("\u{e128}") +#let fa-circle-up-right = fa-icon.with("\u{e129}") +#let fa-circle-user = fa-icon.with("\u{f2bd}") +#let fa-user-circle = fa-icon.with("\u{f2bd}") +#let fa-circle-v = fa-icon.with("\u{e12a}") +#let fa-circle-video = fa-icon.with("\u{e12b}") +#let fa-video-circle = fa-icon.with("\u{e12b}") +#let fa-circle-w = fa-icon.with("\u{e12c}") +#let fa-circle-waveform-lines = fa-icon.with("\u{e12d}") +#let fa-waveform-circle = fa-icon.with("\u{e12d}") +#let fa-circle-wifi = fa-icon.with("\u{e67d}") +#let fa-circle-wifi-circle-wifi = fa-icon.with("\u{e67e}") +#let fa-circle-wifi-group = fa-icon.with("\u{e67e}") +#let fa-circle-x = fa-icon.with("\u{e12e}") +#let fa-circle-xmark = fa-icon.with("\u{f057}") +#let fa-times-circle = fa-icon.with("\u{f057}") +#let fa-xmark-circle = fa-icon.with("\u{f057}") +#let fa-circle-y = fa-icon.with("\u{e12f}") +#let fa-circle-yen = fa-icon.with("\u{e5d0}") +#let fa-circle-z = fa-icon.with("\u{e130}") +#let fa-citrus = fa-icon.with("\u{e2f4}") +#let fa-citrus-slice = fa-icon.with("\u{e2f5}") +#let fa-city = fa-icon.with("\u{f64f}") +#let fa-clapperboard = fa-icon.with("\u{e131}") +#let fa-clapperboard-play = fa-icon.with("\u{e132}") +#let fa-clarinet = fa-icon.with("\u{f8ad}") +#let fa-claw-marks = fa-icon.with("\u{f6c2}") +#let fa-clipboard = fa-icon.with("\u{f328}") +#let fa-clipboard-check = fa-icon.with("\u{f46c}") +#let fa-clipboard-list = fa-icon.with("\u{f46d}") +#let fa-clipboard-list-check = fa-icon.with("\u{f737}") +#let fa-clipboard-medical = fa-icon.with("\u{e133}") +#let fa-clipboard-prescription = fa-icon.with("\u{f5e8}") +#let fa-clipboard-question = fa-icon.with("\u{e4e3}") +#let fa-clipboard-user = fa-icon.with("\u{f7f3}") +#let fa-clock = fa-icon.with("\u{f017}") +#let fa-clock-four = fa-icon.with("\u{f017}") +#let fa-clock-desk = fa-icon.with("\u{e134}") +#let fa-clock-eight = fa-icon.with("\u{e345}") +#let fa-clock-eight-thirty = fa-icon.with("\u{e346}") +#let fa-clock-eleven = fa-icon.with("\u{e347}") +#let fa-clock-eleven-thirty = fa-icon.with("\u{e348}") +#let fa-clock-five = fa-icon.with("\u{e349}") +#let fa-clock-five-thirty = fa-icon.with("\u{e34a}") +#let fa-clock-four-thirty = fa-icon.with("\u{e34b}") +#let fa-clock-nine = fa-icon.with("\u{e34c}") +#let fa-clock-nine-thirty = fa-icon.with("\u{e34d}") +#let fa-clock-one = fa-icon.with("\u{e34e}") +#let fa-clock-one-thirty = fa-icon.with("\u{e34f}") +#let fa-clock-rotate-left = fa-icon.with("\u{f1da}") +#let fa-history = fa-icon.with("\u{f1da}") +#let fa-clock-seven = fa-icon.with("\u{e350}") +#let fa-clock-seven-thirty = fa-icon.with("\u{e351}") +#let fa-clock-six = fa-icon.with("\u{e352}") +#let fa-clock-six-thirty = fa-icon.with("\u{e353}") +#let fa-clock-ten = fa-icon.with("\u{e354}") +#let fa-clock-ten-thirty = fa-icon.with("\u{e355}") +#let fa-clock-three = fa-icon.with("\u{e356}") +#let fa-clock-three-thirty = fa-icon.with("\u{e357}") +#let fa-clock-twelve = fa-icon.with("\u{e358}") +#let fa-clock-twelve-thirty = fa-icon.with("\u{e359}") +#let fa-clock-two = fa-icon.with("\u{e35a}") +#let fa-clock-two-thirty = fa-icon.with("\u{e35b}") +#let fa-clone = fa-icon.with("\u{f24d}") +#let fa-closed-captioning = fa-icon.with("\u{f20a}") +#let fa-closed-captioning-slash = fa-icon.with("\u{e135}") +#let fa-clothes-hanger = fa-icon.with("\u{e136}") +#let fa-cloud = fa-icon.with("\u{f0c2}") +#let fa-cloud-arrow-down = fa-icon.with("\u{f0ed}") +#let fa-cloud-download = fa-icon.with("\u{f0ed}") +#let fa-cloud-download-alt = fa-icon.with("\u{f0ed}") +#let fa-cloud-arrow-up = fa-icon.with("\u{f0ee}") +#let fa-cloud-upload = fa-icon.with("\u{f0ee}") +#let fa-cloud-upload-alt = fa-icon.with("\u{f0ee}") +#let fa-cloud-binary = fa-icon.with("\u{e601}") +#let fa-cloud-bolt = fa-icon.with("\u{f76c}") +#let fa-thunderstorm = fa-icon.with("\u{f76c}") +#let fa-cloud-bolt-moon = fa-icon.with("\u{f76d}") +#let fa-thunderstorm-moon = fa-icon.with("\u{f76d}") +#let fa-cloud-bolt-sun = fa-icon.with("\u{f76e}") +#let fa-thunderstorm-sun = fa-icon.with("\u{f76e}") +#let fa-cloud-check = fa-icon.with("\u{e35c}") +#let fa-cloud-drizzle = fa-icon.with("\u{f738}") +#let fa-cloud-exclamation = fa-icon.with("\u{e491}") +#let fa-cloudflare = fa-icon.with("\u{e07d}") +#let fa-cloud-fog = fa-icon.with("\u{f74e}") +#let fa-fog = fa-icon.with("\u{f74e}") +#let fa-cloud-hail = fa-icon.with("\u{f739}") +#let fa-cloud-hail-mixed = fa-icon.with("\u{f73a}") +#let fa-cloud-meatball = fa-icon.with("\u{f73b}") +#let fa-cloud-minus = fa-icon.with("\u{e35d}") +#let fa-cloud-moon = fa-icon.with("\u{f6c3}") +#let fa-cloud-moon-rain = fa-icon.with("\u{f73c}") +#let fa-cloud-music = fa-icon.with("\u{f8ae}") +#let fa-cloud-plus = fa-icon.with("\u{e35e}") +#let fa-cloud-question = fa-icon.with("\u{e492}") +#let fa-cloud-rain = fa-icon.with("\u{f73d}") +#let fa-cloud-rainbow = fa-icon.with("\u{f73e}") +#let fa-clouds = fa-icon.with("\u{f744}") +#let fa-cloudscale = fa-icon.with("\u{f383}") +#let fa-cloud-showers = fa-icon.with("\u{f73f}") +#let fa-cloud-showers-heavy = fa-icon.with("\u{f740}") +#let fa-cloud-showers-water = fa-icon.with("\u{e4e4}") +#let fa-cloud-slash = fa-icon.with("\u{e137}") +#let fa-cloud-sleet = fa-icon.with("\u{f741}") +#let fa-cloudsmith = fa-icon.with("\u{f384}") +#let fa-clouds-moon = fa-icon.with("\u{f745}") +#let fa-cloud-snow = fa-icon.with("\u{f742}") +#let fa-clouds-sun = fa-icon.with("\u{f746}") +#let fa-cloud-sun = fa-icon.with("\u{f6c4}") +#let fa-cloud-sun-rain = fa-icon.with("\u{f743}") +#let fa-cloudversify = fa-icon.with("\u{f385}") +#let fa-cloud-word = fa-icon.with("\u{e138}") +#let fa-cloud-xmark = fa-icon.with("\u{e35f}") +#let fa-clover = fa-icon.with("\u{e139}") +#let fa-club = fa-icon.with("\u{f327}") +#let fa-cmplid = fa-icon.with("\u{e360}") +#let fa-coconut = fa-icon.with("\u{e2f6}") +#let fa-code = fa-icon.with("\u{f121}") +#let fa-code-branch = fa-icon.with("\u{f126}") +#let fa-code-commit = fa-icon.with("\u{f386}") +#let fa-code-compare = fa-icon.with("\u{e13a}") +#let fa-code-fork = fa-icon.with("\u{e13b}") +#let fa-code-merge = fa-icon.with("\u{f387}") +#let fa-codepen = fa-icon.with("\u{f1cb}") +#let fa-code-pull-request = fa-icon.with("\u{e13c}") +#let fa-code-pull-request-closed = fa-icon.with("\u{e3f9}") +#let fa-code-pull-request-draft = fa-icon.with("\u{e3fa}") +#let fa-code-simple = fa-icon.with("\u{e13d}") +#let fa-codiepie = fa-icon.with("\u{f284}") +#let fa-coffee-bean = fa-icon.with("\u{e13e}") +#let fa-coffee-beans = fa-icon.with("\u{e13f}") +#let fa-coffee-pot = fa-icon.with("\u{e002}") +#let fa-coffin = fa-icon.with("\u{f6c6}") +#let fa-coffin-cross = fa-icon.with("\u{e051}") +#let fa-coin = fa-icon.with("\u{f85c}") +#let fa-coin-blank = fa-icon.with("\u{e3fb}") +#let fa-coin-front = fa-icon.with("\u{e3fc}") +#let fa-coins = fa-icon.with("\u{f51e}") +#let fa-coin-vertical = fa-icon.with("\u{e3fd}") +#let fa-colon = fa-icon.with("\u{3a}") +#let fa-colon-sign = fa-icon.with("\u{e140}") +#let fa-columns-3 = fa-icon.with("\u{e361}") +#let fa-comet = fa-icon.with("\u{e003}") +#let fa-comma = fa-icon.with("\u{2c}") +#let fa-command = fa-icon.with("\u{e142}") +#let fa-comment = fa-icon.with("\u{f075}") +#let fa-comment-arrow-down = fa-icon.with("\u{e143}") +#let fa-comment-arrow-up = fa-icon.with("\u{e144}") +#let fa-comment-arrow-up-right = fa-icon.with("\u{e145}") +#let fa-comment-captions = fa-icon.with("\u{e146}") +#let fa-comment-check = fa-icon.with("\u{f4ac}") +#let fa-comment-code = fa-icon.with("\u{e147}") +#let fa-comment-dollar = fa-icon.with("\u{f651}") +#let fa-comment-dots = fa-icon.with("\u{f4ad}") +#let fa-commenting = fa-icon.with("\u{f4ad}") +#let fa-comment-exclamation = fa-icon.with("\u{f4af}") +#let fa-comment-heart = fa-icon.with("\u{e5c8}") +#let fa-comment-image = fa-icon.with("\u{e148}") +#let fa-comment-lines = fa-icon.with("\u{f4b0}") +#let fa-comment-medical = fa-icon.with("\u{f7f5}") +#let fa-comment-middle = fa-icon.with("\u{e149}") +#let fa-comment-middle-top = fa-icon.with("\u{e14a}") +#let fa-comment-minus = fa-icon.with("\u{f4b1}") +#let fa-comment-music = fa-icon.with("\u{f8b0}") +#let fa-comment-pen = fa-icon.with("\u{f4ae}") +#let fa-comment-edit = fa-icon.with("\u{f4ae}") +#let fa-comment-plus = fa-icon.with("\u{f4b2}") +#let fa-comment-question = fa-icon.with("\u{e14b}") +#let fa-comment-quote = fa-icon.with("\u{e14c}") +#let fa-comments = fa-icon.with("\u{f086}") +#let fa-comments-dollar = fa-icon.with("\u{f653}") +#let fa-comment-slash = fa-icon.with("\u{f4b3}") +#let fa-comment-smile = fa-icon.with("\u{f4b4}") +#let fa-comment-sms = fa-icon.with("\u{f7cd}") +#let fa-sms = fa-icon.with("\u{f7cd}") +#let fa-comments-question = fa-icon.with("\u{e14e}") +#let fa-comments-question-check = fa-icon.with("\u{e14f}") +#let fa-comment-text = fa-icon.with("\u{e14d}") +#let fa-comment-xmark = fa-icon.with("\u{f4b5}") +#let fa-comment-times = fa-icon.with("\u{f4b5}") +#let fa-compact-disc = fa-icon.with("\u{f51f}") +#let fa-compass = fa-icon.with("\u{f14e}") +#let fa-compass-drafting = fa-icon.with("\u{f568}") +#let fa-drafting-compass = fa-icon.with("\u{f568}") +#let fa-compass-slash = fa-icon.with("\u{f5e9}") +#let fa-compress = fa-icon.with("\u{f066}") +#let fa-compress-wide = fa-icon.with("\u{f326}") +#let fa-computer = fa-icon.with("\u{e4e5}") +#let fa-computer-classic = fa-icon.with("\u{f8b1}") +#let fa-computer-mouse = fa-icon.with("\u{f8cc}") +#let fa-mouse = fa-icon.with("\u{f8cc}") +#let fa-computer-mouse-scrollwheel = fa-icon.with("\u{f8cd}") +#let fa-mouse-alt = fa-icon.with("\u{f8cd}") +#let fa-computer-speaker = fa-icon.with("\u{f8b2}") +#let fa-confluence = fa-icon.with("\u{f78d}") +#let fa-connectdevelop = fa-icon.with("\u{f20e}") +#let fa-container-storage = fa-icon.with("\u{f4b7}") +#let fa-contao = fa-icon.with("\u{f26d}") +#let fa-conveyor-belt = fa-icon.with("\u{f46e}") +#let fa-conveyor-belt-arm = fa-icon.with("\u{e5f8}") +#let fa-conveyor-belt-boxes = fa-icon.with("\u{f46f}") +#let fa-conveyor-belt-alt = fa-icon.with("\u{f46f}") +#let fa-conveyor-belt-empty = fa-icon.with("\u{e150}") +#let fa-cookie = fa-icon.with("\u{f563}") +#let fa-cookie-bite = fa-icon.with("\u{f564}") +#let fa-copy = fa-icon.with("\u{f0c5}") +#let fa-copyright = fa-icon.with("\u{f1f9}") +#let fa-corn = fa-icon.with("\u{f6c7}") +#let fa-corner = fa-icon.with("\u{e3fe}") +#let fa-cotton-bureau = fa-icon.with("\u{f89e}") +#let fa-couch = fa-icon.with("\u{f4b8}") +#let fa-court-sport = fa-icon.with("\u{e643}") +#let fa-cow = fa-icon.with("\u{f6c8}") +#let fa-cowbell = fa-icon.with("\u{f8b3}") +#let fa-cowbell-circle-plus = fa-icon.with("\u{f8b4}") +#let fa-cowbell-more = fa-icon.with("\u{f8b4}") +#let fa-cpanel = fa-icon.with("\u{f388}") +#let fa-crab = fa-icon.with("\u{e3ff}") +#let fa-crate-apple = fa-icon.with("\u{f6b1}") +#let fa-apple-crate = fa-icon.with("\u{f6b1}") +#let fa-crate-empty = fa-icon.with("\u{e151}") +#let fa-creative-commons = fa-icon.with("\u{f25e}") +#let fa-creative-commons-by = fa-icon.with("\u{f4e7}") +#let fa-creative-commons-nc = fa-icon.with("\u{f4e8}") +#let fa-creative-commons-nc-eu = fa-icon.with("\u{f4e9}") +#let fa-creative-commons-nc-jp = fa-icon.with("\u{f4ea}") +#let fa-creative-commons-nd = fa-icon.with("\u{f4eb}") +#let fa-creative-commons-pd = fa-icon.with("\u{f4ec}") +#let fa-creative-commons-pd-alt = fa-icon.with("\u{f4ed}") +#let fa-creative-commons-remix = fa-icon.with("\u{f4ee}") +#let fa-creative-commons-sa = fa-icon.with("\u{f4ef}") +#let fa-creative-commons-sampling = fa-icon.with("\u{f4f0}") +#let fa-creative-commons-sampling-plus = fa-icon.with("\u{f4f1}") +#let fa-creative-commons-share = fa-icon.with("\u{f4f2}") +#let fa-creative-commons-zero = fa-icon.with("\u{f4f3}") +#let fa-credit-card = fa-icon.with("\u{f09d}") +#let fa-credit-card-alt = fa-icon.with("\u{f09d}") +#let fa-credit-card-blank = fa-icon.with("\u{f389}") +#let fa-credit-card-front = fa-icon.with("\u{f38a}") +#let fa-cricket-bat-ball = fa-icon.with("\u{f449}") +#let fa-cricket = fa-icon.with("\u{f449}") +#let fa-critical-role = fa-icon.with("\u{f6c9}") +#let fa-croissant = fa-icon.with("\u{f7f6}") +#let fa-crop = fa-icon.with("\u{f125}") +#let fa-crop-simple = fa-icon.with("\u{f565}") +#let fa-crop-alt = fa-icon.with("\u{f565}") +#let fa-cross = fa-icon.with("\u{f654}") +#let fa-crosshairs = fa-icon.with("\u{f05b}") +#let fa-crosshairs-simple = fa-icon.with("\u{e59f}") +#let fa-crow = fa-icon.with("\u{f520}") +#let fa-crown = fa-icon.with("\u{f521}") +#let fa-crutch = fa-icon.with("\u{f7f7}") +#let fa-crutches = fa-icon.with("\u{f7f8}") +#let fa-cruzeiro-sign = fa-icon.with("\u{e152}") +#let fa-crystal-ball = fa-icon.with("\u{e362}") +#let fa-css3 = fa-icon.with("\u{f13c}") +#let fa-css3-alt = fa-icon.with("\u{f38b}") +#let fa-cube = fa-icon.with("\u{f1b2}") +#let fa-cubes = fa-icon.with("\u{f1b3}") +#let fa-cubes-stacked = fa-icon.with("\u{e4e6}") +#let fa-cucumber = fa-icon.with("\u{e401}") +#let fa-cupcake = fa-icon.with("\u{e402}") +#let fa-cup-straw = fa-icon.with("\u{e363}") +#let fa-cup-straw-swoosh = fa-icon.with("\u{e364}") +#let fa-cup-togo = fa-icon.with("\u{f6c5}") +#let fa-coffee-togo = fa-icon.with("\u{f6c5}") +#let fa-curling-stone = fa-icon.with("\u{f44a}") +#let fa-curling = fa-icon.with("\u{f44a}") +#let fa-custard = fa-icon.with("\u{e403}") +#let fa-cuttlefish = fa-icon.with("\u{f38c}") +#let fa-d = fa-icon.with("\u{44}") +#let fa-dagger = fa-icon.with("\u{f6cb}") +#let fa-dailymotion = fa-icon.with("\u{e052}") +#let fa-d-and-d = fa-icon.with("\u{f38d}") +#let fa-d-and-d-beyond = fa-icon.with("\u{f6ca}") +#let fa-dart-lang = fa-icon.with("\u{e693}") +#let fa-dash = fa-icon.with("\u{e404}") +#let fa-minus-large = fa-icon.with("\u{e404}") +#let fa-dashcube = fa-icon.with("\u{f210}") +#let fa-database = fa-icon.with("\u{f1c0}") +#let fa-debian = fa-icon.with("\u{e60b}") +#let fa-deer = fa-icon.with("\u{f78e}") +#let fa-deer-rudolph = fa-icon.with("\u{f78f}") +#let fa-deezer = fa-icon.with("\u{e077}") +#let fa-delete-left = fa-icon.with("\u{f55a}") +#let fa-backspace = fa-icon.with("\u{f55a}") +#let fa-delete-right = fa-icon.with("\u{e154}") +#let fa-delicious = fa-icon.with("\u{f1a5}") +#let fa-democrat = fa-icon.with("\u{f747}") +#let fa-deploydog = fa-icon.with("\u{f38e}") +#let fa-deskpro = fa-icon.with("\u{f38f}") +#let fa-desktop = fa-icon.with("\u{f390}") +#let fa-desktop-alt = fa-icon.with("\u{f390}") +#let fa-desktop-arrow-down = fa-icon.with("\u{e155}") +#let fa-dev = fa-icon.with("\u{f6cc}") +#let fa-deviantart = fa-icon.with("\u{f1bd}") +#let fa-dharmachakra = fa-icon.with("\u{f655}") +#let fa-dhl = fa-icon.with("\u{f790}") +#let fa-diagram-cells = fa-icon.with("\u{e475}") +#let fa-diagram-lean-canvas = fa-icon.with("\u{e156}") +#let fa-diagram-nested = fa-icon.with("\u{e157}") +#let fa-diagram-next = fa-icon.with("\u{e476}") +#let fa-diagram-predecessor = fa-icon.with("\u{e477}") +#let fa-diagram-previous = fa-icon.with("\u{e478}") +#let fa-diagram-project = fa-icon.with("\u{f542}") +#let fa-project-diagram = fa-icon.with("\u{f542}") +#let fa-diagram-sankey = fa-icon.with("\u{e158}") +#let fa-diagram-subtask = fa-icon.with("\u{e479}") +#let fa-diagram-successor = fa-icon.with("\u{e47a}") +#let fa-diagram-venn = fa-icon.with("\u{e15a}") +#let fa-dial = fa-icon.with("\u{e15b}") +#let fa-dial-med-high = fa-icon.with("\u{e15b}") +#let fa-dial-high = fa-icon.with("\u{e15c}") +#let fa-dial-low = fa-icon.with("\u{e15d}") +#let fa-dial-max = fa-icon.with("\u{e15e}") +#let fa-dial-med = fa-icon.with("\u{e15f}") +#let fa-dial-med-low = fa-icon.with("\u{e160}") +#let fa-dial-min = fa-icon.with("\u{e161}") +#let fa-dial-off = fa-icon.with("\u{e162}") +#let fa-diamond = fa-icon.with("\u{f219}") +#let fa-diamond-exclamation = fa-icon.with("\u{e405}") +#let fa-diamond-half = fa-icon.with("\u{e5b7}") +#let fa-diamond-half-stroke = fa-icon.with("\u{e5b8}") +#let fa-diamonds-4 = fa-icon.with("\u{e68b}") +#let fa-diamond-turn-right = fa-icon.with("\u{f5eb}") +#let fa-directions = fa-icon.with("\u{f5eb}") +#let fa-diaspora = fa-icon.with("\u{f791}") +#let fa-dice = fa-icon.with("\u{f522}") +#let fa-dice-d10 = fa-icon.with("\u{f6cd}") +#let fa-dice-d12 = fa-icon.with("\u{f6ce}") +#let fa-dice-d20 = fa-icon.with("\u{f6cf}") +#let fa-dice-d4 = fa-icon.with("\u{f6d0}") +#let fa-dice-d6 = fa-icon.with("\u{f6d1}") +#let fa-dice-d8 = fa-icon.with("\u{f6d2}") +#let fa-dice-five = fa-icon.with("\u{f523}") +#let fa-dice-four = fa-icon.with("\u{f524}") +#let fa-dice-one = fa-icon.with("\u{f525}") +#let fa-dice-six = fa-icon.with("\u{f526}") +#let fa-dice-three = fa-icon.with("\u{f527}") +#let fa-dice-two = fa-icon.with("\u{f528}") +#let fa-digg = fa-icon.with("\u{f1a6}") +#let fa-digital-ocean = fa-icon.with("\u{f391}") +#let fa-dinosaur = fa-icon.with("\u{e5fe}") +#let fa-diploma = fa-icon.with("\u{f5ea}") +#let fa-scroll-ribbon = fa-icon.with("\u{f5ea}") +#let fa-disc-drive = fa-icon.with("\u{f8b5}") +#let fa-discord = fa-icon.with("\u{f392}") +#let fa-discourse = fa-icon.with("\u{f393}") +#let fa-disease = fa-icon.with("\u{f7fa}") +#let fa-display = fa-icon.with("\u{e163}") +#let fa-display-arrow-down = fa-icon.with("\u{e164}") +#let fa-display-chart-up = fa-icon.with("\u{e5e3}") +#let fa-display-chart-up-circle-currency = fa-icon.with("\u{e5e5}") +#let fa-display-chart-up-circle-dollar = fa-icon.with("\u{e5e6}") +#let fa-display-code = fa-icon.with("\u{e165}") +#let fa-desktop-code = fa-icon.with("\u{e165}") +#let fa-display-medical = fa-icon.with("\u{e166}") +#let fa-desktop-medical = fa-icon.with("\u{e166}") +#let fa-display-slash = fa-icon.with("\u{e2fa}") +#let fa-desktop-slash = fa-icon.with("\u{e2fa}") +#let fa-distribute-spacing-horizontal = fa-icon.with("\u{e365}") +#let fa-distribute-spacing-vertical = fa-icon.with("\u{e366}") +#let fa-ditto = fa-icon.with("\u{22}") +#let fa-divide = fa-icon.with("\u{f529}") +#let fa-dna = fa-icon.with("\u{f471}") +#let fa-dochub = fa-icon.with("\u{f394}") +#let fa-docker = fa-icon.with("\u{f395}") +#let fa-dog = fa-icon.with("\u{f6d3}") +#let fa-dog-leashed = fa-icon.with("\u{f6d4}") +#let fa-dollar-sign = fa-icon.with("\u{24}") +#let fa-dollar = fa-icon.with("\u{24}") +#let fa-usd = fa-icon.with("\u{24}") +#let fa-dolly = fa-icon.with("\u{f472}") +#let fa-dolly-box = fa-icon.with("\u{f472}") +#let fa-dolly-empty = fa-icon.with("\u{f473}") +#let fa-dolphin = fa-icon.with("\u{e168}") +#let fa-dong-sign = fa-icon.with("\u{e169}") +#let fa-do-not-enter = fa-icon.with("\u{f5ec}") +#let fa-donut = fa-icon.with("\u{e406}") +#let fa-doughnut = fa-icon.with("\u{e406}") +#let fa-door-closed = fa-icon.with("\u{f52a}") +#let fa-door-open = fa-icon.with("\u{f52b}") +#let fa-dove = fa-icon.with("\u{f4ba}") +#let fa-down = fa-icon.with("\u{f354}") +#let fa-arrow-alt-down = fa-icon.with("\u{f354}") +#let fa-down-from-bracket = fa-icon.with("\u{e66b}") +#let fa-down-from-dotted-line = fa-icon.with("\u{e407}") +#let fa-down-from-line = fa-icon.with("\u{f349}") +#let fa-arrow-alt-from-top = fa-icon.with("\u{f349}") +#let fa-down-left = fa-icon.with("\u{e16a}") +#let fa-down-left-and-up-right-to-center = fa-icon.with("\u{f422}") +#let fa-compress-alt = fa-icon.with("\u{f422}") +#let fa-download = fa-icon.with("\u{f019}") +#let fa-down-long = fa-icon.with("\u{f309}") +#let fa-long-arrow-alt-down = fa-icon.with("\u{f309}") +#let fa-down-right = fa-icon.with("\u{e16b}") +#let fa-down-to-bracket = fa-icon.with("\u{e4e7}") +#let fa-down-to-dotted-line = fa-icon.with("\u{e408}") +#let fa-down-to-line = fa-icon.with("\u{f34a}") +#let fa-arrow-alt-to-bottom = fa-icon.with("\u{f34a}") +#let fa-draft2digital = fa-icon.with("\u{f396}") +#let fa-dragon = fa-icon.with("\u{f6d5}") +#let fa-draw-circle = fa-icon.with("\u{f5ed}") +#let fa-draw-polygon = fa-icon.with("\u{f5ee}") +#let fa-draw-square = fa-icon.with("\u{f5ef}") +#let fa-dreidel = fa-icon.with("\u{f792}") +#let fa-dribbble = fa-icon.with("\u{f17d}") +#let fa-drone = fa-icon.with("\u{f85f}") +#let fa-drone-front = fa-icon.with("\u{f860}") +#let fa-drone-alt = fa-icon.with("\u{f860}") +#let fa-dropbox = fa-icon.with("\u{f16b}") +#let fa-droplet = fa-icon.with("\u{f043}") +#let fa-tint = fa-icon.with("\u{f043}") +#let fa-droplet-degree = fa-icon.with("\u{f748}") +#let fa-dewpoint = fa-icon.with("\u{f748}") +#let fa-droplet-percent = fa-icon.with("\u{f750}") +#let fa-humidity = fa-icon.with("\u{f750}") +#let fa-droplet-slash = fa-icon.with("\u{f5c7}") +#let fa-tint-slash = fa-icon.with("\u{f5c7}") +#let fa-drum = fa-icon.with("\u{f569}") +#let fa-drum-steelpan = fa-icon.with("\u{f56a}") +#let fa-drumstick = fa-icon.with("\u{f6d6}") +#let fa-drumstick-bite = fa-icon.with("\u{f6d7}") +#let fa-drupal = fa-icon.with("\u{f1a9}") +#let fa-dryer = fa-icon.with("\u{f861}") +#let fa-dryer-heat = fa-icon.with("\u{f862}") +#let fa-dryer-alt = fa-icon.with("\u{f862}") +#let fa-duck = fa-icon.with("\u{f6d8}") +#let fa-dumbbell = fa-icon.with("\u{f44b}") +#let fa-dumpster = fa-icon.with("\u{f793}") +#let fa-dumpster-fire = fa-icon.with("\u{f794}") +#let fa-dungeon = fa-icon.with("\u{f6d9}") +#let fa-dyalog = fa-icon.with("\u{f399}") +#let fa-e = fa-icon.with("\u{45}") +#let fa-ear = fa-icon.with("\u{f5f0}") +#let fa-ear-deaf = fa-icon.with("\u{f2a4}") +#let fa-deaf = fa-icon.with("\u{f2a4}") +#let fa-deafness = fa-icon.with("\u{f2a4}") +#let fa-hard-of-hearing = fa-icon.with("\u{f2a4}") +#let fa-ear-listen = fa-icon.with("\u{f2a2}") +#let fa-assistive-listening-systems = fa-icon.with("\u{f2a2}") +#let fa-earlybirds = fa-icon.with("\u{f39a}") +#let fa-ear-muffs = fa-icon.with("\u{f795}") +#let fa-earth-africa = fa-icon.with("\u{f57c}") +#let fa-globe-africa = fa-icon.with("\u{f57c}") +#let fa-earth-americas = fa-icon.with("\u{f57d}") +#let fa-earth = fa-icon.with("\u{f57d}") +#let fa-earth-america = fa-icon.with("\u{f57d}") +#let fa-globe-americas = fa-icon.with("\u{f57d}") +#let fa-earth-asia = fa-icon.with("\u{f57e}") +#let fa-globe-asia = fa-icon.with("\u{f57e}") +#let fa-earth-europe = fa-icon.with("\u{f7a2}") +#let fa-globe-europe = fa-icon.with("\u{f7a2}") +#let fa-earth-oceania = fa-icon.with("\u{e47b}") +#let fa-globe-oceania = fa-icon.with("\u{e47b}") +#let fa-ebay = fa-icon.with("\u{f4f4}") +#let fa-eclipse = fa-icon.with("\u{f749}") +#let fa-edge = fa-icon.with("\u{f282}") +#let fa-edge-legacy = fa-icon.with("\u{e078}") +#let fa-egg = fa-icon.with("\u{f7fb}") +#let fa-egg-fried = fa-icon.with("\u{f7fc}") +#let fa-eggplant = fa-icon.with("\u{e16c}") +#let fa-eject = fa-icon.with("\u{f052}") +#let fa-elementor = fa-icon.with("\u{f430}") +#let fa-elephant = fa-icon.with("\u{f6da}") +#let fa-elevator = fa-icon.with("\u{e16d}") +#let fa-ellipsis = fa-icon.with("\u{f141}") +#let fa-ellipsis-h = fa-icon.with("\u{f141}") +#let fa-ellipsis-stroke = fa-icon.with("\u{f39b}") +#let fa-ellipsis-h-alt = fa-icon.with("\u{f39b}") +#let fa-ellipsis-stroke-vertical = fa-icon.with("\u{f39c}") +#let fa-ellipsis-v-alt = fa-icon.with("\u{f39c}") +#let fa-ellipsis-vertical = fa-icon.with("\u{f142}") +#let fa-ellipsis-v = fa-icon.with("\u{f142}") +#let fa-ello = fa-icon.with("\u{f5f1}") +#let fa-ember = fa-icon.with("\u{f423}") +#let fa-empire = fa-icon.with("\u{f1d1}") +#let fa-empty-set = fa-icon.with("\u{f656}") +#let fa-engine = fa-icon.with("\u{e16e}") +#let fa-engine-warning = fa-icon.with("\u{f5f2}") +#let fa-engine-exclamation = fa-icon.with("\u{f5f2}") +#let fa-envelope = fa-icon.with("\u{f0e0}") +#let fa-envelope-circle-check = fa-icon.with("\u{e4e8}") +#let fa-envelope-dot = fa-icon.with("\u{e16f}") +#let fa-envelope-badge = fa-icon.with("\u{e16f}") +#let fa-envelope-open = fa-icon.with("\u{f2b6}") +#let fa-envelope-open-dollar = fa-icon.with("\u{f657}") +#let fa-envelope-open-text = fa-icon.with("\u{f658}") +#let fa-envelopes = fa-icon.with("\u{e170}") +#let fa-envelopes-bulk = fa-icon.with("\u{f674}") +#let fa-mail-bulk = fa-icon.with("\u{f674}") +#let fa-envira = fa-icon.with("\u{f299}") +#let fa-equals = fa-icon.with("\u{3d}") +#let fa-eraser = fa-icon.with("\u{f12d}") +#let fa-erlang = fa-icon.with("\u{f39d}") +#let fa-escalator = fa-icon.with("\u{e171}") +#let fa-ethereum = fa-icon.with("\u{f42e}") +#let fa-ethernet = fa-icon.with("\u{f796}") +#let fa-etsy = fa-icon.with("\u{f2d7}") +#let fa-euro-sign = fa-icon.with("\u{f153}") +#let fa-eur = fa-icon.with("\u{f153}") +#let fa-euro = fa-icon.with("\u{f153}") +#let fa-evernote = fa-icon.with("\u{f839}") +#let fa-excavator = fa-icon.with("\u{e656}") +#let fa-exclamation = fa-icon.with("\u{21}") +#let fa-expand = fa-icon.with("\u{f065}") +#let fa-expand-wide = fa-icon.with("\u{f320}") +#let fa-expeditedssl = fa-icon.with("\u{f23e}") +#let fa-explosion = fa-icon.with("\u{e4e9}") +#let fa-eye = fa-icon.with("\u{f06e}") +#let fa-eye-dropper = fa-icon.with("\u{f1fb}") +#let fa-eye-dropper-empty = fa-icon.with("\u{f1fb}") +#let fa-eyedropper = fa-icon.with("\u{f1fb}") +#let fa-eye-dropper-full = fa-icon.with("\u{e172}") +#let fa-eye-dropper-half = fa-icon.with("\u{e173}") +#let fa-eye-evil = fa-icon.with("\u{f6db}") +#let fa-eye-low-vision = fa-icon.with("\u{f2a8}") +#let fa-low-vision = fa-icon.with("\u{f2a8}") +#let fa-eyes = fa-icon.with("\u{e367}") +#let fa-eye-slash = fa-icon.with("\u{f070}") +#let fa-f = fa-icon.with("\u{46}") +#let fa-face-angry = fa-icon.with("\u{f556}") +#let fa-angry = fa-icon.with("\u{f556}") +#let fa-face-angry-horns = fa-icon.with("\u{e368}") +#let fa-face-anguished = fa-icon.with("\u{e369}") +#let fa-face-anxious-sweat = fa-icon.with("\u{e36a}") +#let fa-face-astonished = fa-icon.with("\u{e36b}") +#let fa-face-awesome = fa-icon.with("\u{e409}") +#let fa-gave-dandy = fa-icon.with("\u{e409}") +#let fa-face-beam-hand-over-mouth = fa-icon.with("\u{e47c}") +#let fa-facebook = fa-icon.with("\u{f09a}") +#let fa-facebook-f = fa-icon.with("\u{f39e}") +#let fa-facebook-messenger = fa-icon.with("\u{f39f}") +#let fa-face-clouds = fa-icon.with("\u{e47d}") +#let fa-face-confounded = fa-icon.with("\u{e36c}") +#let fa-face-confused = fa-icon.with("\u{e36d}") +#let fa-face-cowboy-hat = fa-icon.with("\u{e36e}") +#let fa-face-diagonal-mouth = fa-icon.with("\u{e47e}") +#let fa-face-disappointed = fa-icon.with("\u{e36f}") +#let fa-face-disguise = fa-icon.with("\u{e370}") +#let fa-face-dizzy = fa-icon.with("\u{f567}") +#let fa-dizzy = fa-icon.with("\u{f567}") +#let fa-face-dotted = fa-icon.with("\u{e47f}") +#let fa-face-downcast-sweat = fa-icon.with("\u{e371}") +#let fa-face-drooling = fa-icon.with("\u{e372}") +#let fa-face-exhaling = fa-icon.with("\u{e480}") +#let fa-face-explode = fa-icon.with("\u{e2fe}") +#let fa-exploding-head = fa-icon.with("\u{e2fe}") +#let fa-face-expressionless = fa-icon.with("\u{e373}") +#let fa-face-eyes-xmarks = fa-icon.with("\u{e374}") +#let fa-face-fearful = fa-icon.with("\u{e375}") +#let fa-face-flushed = fa-icon.with("\u{f579}") +#let fa-flushed = fa-icon.with("\u{f579}") +#let fa-face-frown = fa-icon.with("\u{f119}") +#let fa-frown = fa-icon.with("\u{f119}") +#let fa-face-frown-open = fa-icon.with("\u{f57a}") +#let fa-frown-open = fa-icon.with("\u{f57a}") +#let fa-face-frown-slight = fa-icon.with("\u{e376}") +#let fa-face-glasses = fa-icon.with("\u{e377}") +#let fa-face-grimace = fa-icon.with("\u{f57f}") +#let fa-grimace = fa-icon.with("\u{f57f}") +#let fa-face-grin = fa-icon.with("\u{f580}") +#let fa-grin = fa-icon.with("\u{f580}") +#let fa-face-grin-beam = fa-icon.with("\u{f582}") +#let fa-grin-beam = fa-icon.with("\u{f582}") +#let fa-face-grin-beam-sweat = fa-icon.with("\u{f583}") +#let fa-grin-beam-sweat = fa-icon.with("\u{f583}") +#let fa-face-grin-hearts = fa-icon.with("\u{f584}") +#let fa-grin-hearts = fa-icon.with("\u{f584}") +#let fa-face-grin-squint = fa-icon.with("\u{f585}") +#let fa-grin-squint = fa-icon.with("\u{f585}") +#let fa-face-grin-squint-tears = fa-icon.with("\u{f586}") +#let fa-grin-squint-tears = fa-icon.with("\u{f586}") +#let fa-face-grin-stars = fa-icon.with("\u{f587}") +#let fa-grin-stars = fa-icon.with("\u{f587}") +#let fa-face-grin-tears = fa-icon.with("\u{f588}") +#let fa-grin-tears = fa-icon.with("\u{f588}") +#let fa-face-grin-tongue = fa-icon.with("\u{f589}") +#let fa-grin-tongue = fa-icon.with("\u{f589}") +#let fa-face-grin-tongue-squint = fa-icon.with("\u{f58a}") +#let fa-grin-tongue-squint = fa-icon.with("\u{f58a}") +#let fa-face-grin-tongue-wink = fa-icon.with("\u{f58b}") +#let fa-grin-tongue-wink = fa-icon.with("\u{f58b}") +#let fa-face-grin-wide = fa-icon.with("\u{f581}") +#let fa-grin-alt = fa-icon.with("\u{f581}") +#let fa-face-grin-wink = fa-icon.with("\u{f58c}") +#let fa-grin-wink = fa-icon.with("\u{f58c}") +#let fa-face-hand-over-mouth = fa-icon.with("\u{e378}") +#let fa-face-hand-peeking = fa-icon.with("\u{e481}") +#let fa-face-hand-yawn = fa-icon.with("\u{e379}") +#let fa-face-head-bandage = fa-icon.with("\u{e37a}") +#let fa-face-holding-back-tears = fa-icon.with("\u{e482}") +#let fa-face-hushed = fa-icon.with("\u{e37b}") +#let fa-face-icicles = fa-icon.with("\u{e37c}") +#let fa-face-kiss = fa-icon.with("\u{f596}") +#let fa-kiss = fa-icon.with("\u{f596}") +#let fa-face-kiss-beam = fa-icon.with("\u{f597}") +#let fa-kiss-beam = fa-icon.with("\u{f597}") +#let fa-face-kiss-closed-eyes = fa-icon.with("\u{e37d}") +#let fa-face-kiss-wink-heart = fa-icon.with("\u{f598}") +#let fa-kiss-wink-heart = fa-icon.with("\u{f598}") +#let fa-face-laugh = fa-icon.with("\u{f599}") +#let fa-laugh = fa-icon.with("\u{f599}") +#let fa-face-laugh-beam = fa-icon.with("\u{f59a}") +#let fa-laugh-beam = fa-icon.with("\u{f59a}") +#let fa-face-laugh-squint = fa-icon.with("\u{f59b}") +#let fa-laugh-squint = fa-icon.with("\u{f59b}") +#let fa-face-laugh-wink = fa-icon.with("\u{f59c}") +#let fa-laugh-wink = fa-icon.with("\u{f59c}") +#let fa-face-lying = fa-icon.with("\u{e37e}") +#let fa-face-mask = fa-icon.with("\u{e37f}") +#let fa-face-meh = fa-icon.with("\u{f11a}") +#let fa-meh = fa-icon.with("\u{f11a}") +#let fa-face-meh-blank = fa-icon.with("\u{f5a4}") +#let fa-meh-blank = fa-icon.with("\u{f5a4}") +#let fa-face-melting = fa-icon.with("\u{e483}") +#let fa-face-monocle = fa-icon.with("\u{e380}") +#let fa-face-nauseated = fa-icon.with("\u{e381}") +#let fa-face-nose-steam = fa-icon.with("\u{e382}") +#let fa-face-party = fa-icon.with("\u{e383}") +#let fa-face-pensive = fa-icon.with("\u{e384}") +#let fa-face-persevering = fa-icon.with("\u{e385}") +#let fa-face-pleading = fa-icon.with("\u{e386}") +#let fa-face-pouting = fa-icon.with("\u{e387}") +#let fa-face-raised-eyebrow = fa-icon.with("\u{e388}") +#let fa-face-relieved = fa-icon.with("\u{e389}") +#let fa-face-rolling-eyes = fa-icon.with("\u{f5a5}") +#let fa-meh-rolling-eyes = fa-icon.with("\u{f5a5}") +#let fa-face-sad-cry = fa-icon.with("\u{f5b3}") +#let fa-sad-cry = fa-icon.with("\u{f5b3}") +#let fa-face-sad-sweat = fa-icon.with("\u{e38a}") +#let fa-face-sad-tear = fa-icon.with("\u{f5b4}") +#let fa-sad-tear = fa-icon.with("\u{f5b4}") +#let fa-face-saluting = fa-icon.with("\u{e484}") +#let fa-face-scream = fa-icon.with("\u{e38b}") +#let fa-face-shush = fa-icon.with("\u{e38c}") +#let fa-face-sleeping = fa-icon.with("\u{e38d}") +#let fa-face-sleepy = fa-icon.with("\u{e38e}") +#let fa-face-smile = fa-icon.with("\u{f118}") +#let fa-smile = fa-icon.with("\u{f118}") +#let fa-face-smile-beam = fa-icon.with("\u{f5b8}") +#let fa-smile-beam = fa-icon.with("\u{f5b8}") +#let fa-face-smile-halo = fa-icon.with("\u{e38f}") +#let fa-face-smile-hearts = fa-icon.with("\u{e390}") +#let fa-face-smile-horns = fa-icon.with("\u{e391}") +#let fa-face-smile-plus = fa-icon.with("\u{f5b9}") +#let fa-smile-plus = fa-icon.with("\u{f5b9}") +#let fa-face-smile-relaxed = fa-icon.with("\u{e392}") +#let fa-face-smile-tear = fa-icon.with("\u{e393}") +#let fa-face-smile-tongue = fa-icon.with("\u{e394}") +#let fa-face-smile-upside-down = fa-icon.with("\u{e395}") +#let fa-face-smile-wink = fa-icon.with("\u{f4da}") +#let fa-smile-wink = fa-icon.with("\u{f4da}") +#let fa-face-smiling-hands = fa-icon.with("\u{e396}") +#let fa-face-smirking = fa-icon.with("\u{e397}") +#let fa-face-spiral-eyes = fa-icon.with("\u{e485}") +#let fa-face-sunglasses = fa-icon.with("\u{e398}") +#let fa-face-surprise = fa-icon.with("\u{f5c2}") +#let fa-surprise = fa-icon.with("\u{f5c2}") +#let fa-face-swear = fa-icon.with("\u{e399}") +#let fa-face-thermometer = fa-icon.with("\u{e39a}") +#let fa-face-thinking = fa-icon.with("\u{e39b}") +#let fa-face-tired = fa-icon.with("\u{f5c8}") +#let fa-tired = fa-icon.with("\u{f5c8}") +#let fa-face-tissue = fa-icon.with("\u{e39c}") +#let fa-face-tongue-money = fa-icon.with("\u{e39d}") +#let fa-face-tongue-sweat = fa-icon.with("\u{e39e}") +#let fa-face-unamused = fa-icon.with("\u{e39f}") +#let fa-face-viewfinder = fa-icon.with("\u{e2ff}") +#let fa-face-vomit = fa-icon.with("\u{e3a0}") +#let fa-face-weary = fa-icon.with("\u{e3a1}") +#let fa-face-woozy = fa-icon.with("\u{e3a2}") +#let fa-face-worried = fa-icon.with("\u{e3a3}") +#let fa-face-zany = fa-icon.with("\u{e3a4}") +#let fa-face-zipper = fa-icon.with("\u{e3a5}") +#let fa-falafel = fa-icon.with("\u{e40a}") +#let fa-family = fa-icon.with("\u{e300}") +#let fa-family-dress = fa-icon.with("\u{e301}") +#let fa-family-pants = fa-icon.with("\u{e302}") +#let fa-fan = fa-icon.with("\u{f863}") +#let fa-fan-table = fa-icon.with("\u{e004}") +#let fa-fantasy-flight-games = fa-icon.with("\u{f6dc}") +#let fa-farm = fa-icon.with("\u{f864}") +#let fa-barn-silo = fa-icon.with("\u{f864}") +#let fa-faucet = fa-icon.with("\u{e005}") +#let fa-faucet-drip = fa-icon.with("\u{e006}") +#let fa-fax = fa-icon.with("\u{f1ac}") +#let fa-feather = fa-icon.with("\u{f52d}") +#let fa-feather-pointed = fa-icon.with("\u{f56b}") +#let fa-feather-alt = fa-icon.with("\u{f56b}") +#let fa-fedex = fa-icon.with("\u{f797}") +#let fa-fedora = fa-icon.with("\u{f798}") +#let fa-fence = fa-icon.with("\u{e303}") +#let fa-ferris-wheel = fa-icon.with("\u{e174}") +#let fa-ferry = fa-icon.with("\u{e4ea}") +#let fa-field-hockey-stick-ball = fa-icon.with("\u{f44c}") +#let fa-field-hockey = fa-icon.with("\u{f44c}") +#let fa-figma = fa-icon.with("\u{f799}") +#let fa-file = fa-icon.with("\u{f15b}") +#let fa-file-arrow-down = fa-icon.with("\u{f56d}") +#let fa-file-download = fa-icon.with("\u{f56d}") +#let fa-file-arrow-up = fa-icon.with("\u{f574}") +#let fa-file-upload = fa-icon.with("\u{f574}") +#let fa-file-audio = fa-icon.with("\u{f1c7}") +#let fa-file-binary = fa-icon.with("\u{e175}") +#let fa-file-cad = fa-icon.with("\u{e672}") +#let fa-file-certificate = fa-icon.with("\u{f5f3}") +#let fa-file-award = fa-icon.with("\u{f5f3}") +#let fa-file-chart-column = fa-icon.with("\u{f659}") +#let fa-file-chart-line = fa-icon.with("\u{f659}") +#let fa-file-chart-pie = fa-icon.with("\u{f65a}") +#let fa-file-check = fa-icon.with("\u{f316}") +#let fa-file-circle-check = fa-icon.with("\u{e5a0}") +#let fa-file-circle-exclamation = fa-icon.with("\u{e4eb}") +#let fa-file-circle-info = fa-icon.with("\u{e493}") +#let fa-file-circle-minus = fa-icon.with("\u{e4ed}") +#let fa-file-circle-plus = fa-icon.with("\u{e494}") +#let fa-file-circle-question = fa-icon.with("\u{e4ef}") +#let fa-file-circle-xmark = fa-icon.with("\u{e5a1}") +#let fa-file-code = fa-icon.with("\u{f1c9}") +#let fa-file-contract = fa-icon.with("\u{f56c}") +#let fa-file-csv = fa-icon.with("\u{f6dd}") +#let fa-file-dashed-line = fa-icon.with("\u{f877}") +#let fa-page-break = fa-icon.with("\u{f877}") +#let fa-file-doc = fa-icon.with("\u{e5ed}") +#let fa-file-eps = fa-icon.with("\u{e644}") +#let fa-file-excel = fa-icon.with("\u{f1c3}") +#let fa-file-exclamation = fa-icon.with("\u{f31a}") +#let fa-file-export = fa-icon.with("\u{f56e}") +#let fa-arrow-right-from-file = fa-icon.with("\u{f56e}") +#let fa-file-gif = fa-icon.with("\u{e645}") +#let fa-file-heart = fa-icon.with("\u{e176}") +#let fa-file-image = fa-icon.with("\u{f1c5}") +#let fa-file-import = fa-icon.with("\u{f56f}") +#let fa-arrow-right-to-file = fa-icon.with("\u{f56f}") +#let fa-file-invoice = fa-icon.with("\u{f570}") +#let fa-file-invoice-dollar = fa-icon.with("\u{f571}") +#let fa-file-jpg = fa-icon.with("\u{e646}") +#let fa-file-lines = fa-icon.with("\u{f15c}") +#let fa-file-alt = fa-icon.with("\u{f15c}") +#let fa-file-text = fa-icon.with("\u{f15c}") +#let fa-file-lock = fa-icon.with("\u{e3a6}") +#let fa-file-magnifying-glass = fa-icon.with("\u{f865}") +#let fa-file-search = fa-icon.with("\u{f865}") +#let fa-file-medical = fa-icon.with("\u{f477}") +#let fa-file-minus = fa-icon.with("\u{f318}") +#let fa-file-mov = fa-icon.with("\u{e647}") +#let fa-file-mp3 = fa-icon.with("\u{e648}") +#let fa-file-mp4 = fa-icon.with("\u{e649}") +#let fa-file-music = fa-icon.with("\u{f8b6}") +#let fa-file-pdf = fa-icon.with("\u{f1c1}") +#let fa-file-pen = fa-icon.with("\u{f31c}") +#let fa-file-edit = fa-icon.with("\u{f31c}") +#let fa-file-plus = fa-icon.with("\u{f319}") +#let fa-file-plus-minus = fa-icon.with("\u{e177}") +#let fa-file-png = fa-icon.with("\u{e666}") +#let fa-file-powerpoint = fa-icon.with("\u{f1c4}") +#let fa-file-ppt = fa-icon.with("\u{e64a}") +#let fa-file-prescription = fa-icon.with("\u{f572}") +#let fa-files = fa-icon.with("\u{e178}") +#let fa-file-shield = fa-icon.with("\u{e4f0}") +#let fa-file-signature = fa-icon.with("\u{f573}") +#let fa-file-slash = fa-icon.with("\u{e3a7}") +#let fa-files-medical = fa-icon.with("\u{f7fd}") +#let fa-file-spreadsheet = fa-icon.with("\u{f65b}") +#let fa-file-svg = fa-icon.with("\u{e64b}") +#let fa-file-user = fa-icon.with("\u{f65c}") +#let fa-file-vector = fa-icon.with("\u{e64c}") +#let fa-file-video = fa-icon.with("\u{f1c8}") +#let fa-file-waveform = fa-icon.with("\u{f478}") +#let fa-file-medical-alt = fa-icon.with("\u{f478}") +#let fa-file-word = fa-icon.with("\u{f1c2}") +#let fa-file-xls = fa-icon.with("\u{e64d}") +#let fa-file-xmark = fa-icon.with("\u{f317}") +#let fa-file-times = fa-icon.with("\u{f317}") +#let fa-file-xml = fa-icon.with("\u{e654}") +#let fa-file-zip = fa-icon.with("\u{e5ee}") +#let fa-file-zipper = fa-icon.with("\u{f1c6}") +#let fa-file-archive = fa-icon.with("\u{f1c6}") +#let fa-fill = fa-icon.with("\u{f575}") +#let fa-fill-drip = fa-icon.with("\u{f576}") +#let fa-film = fa-icon.with("\u{f008}") +#let fa-film-canister = fa-icon.with("\u{f8b7}") +#let fa-film-cannister = fa-icon.with("\u{f8b7}") +#let fa-films = fa-icon.with("\u{e17a}") +#let fa-film-simple = fa-icon.with("\u{f3a0}") +#let fa-film-alt = fa-icon.with("\u{f3a0}") +#let fa-film-slash = fa-icon.with("\u{e179}") +#let fa-filter = fa-icon.with("\u{f0b0}") +#let fa-filter-circle-dollar = fa-icon.with("\u{f662}") +#let fa-funnel-dollar = fa-icon.with("\u{f662}") +#let fa-filter-circle-xmark = fa-icon.with("\u{e17b}") +#let fa-filter-list = fa-icon.with("\u{e17c}") +#let fa-filters = fa-icon.with("\u{e17e}") +#let fa-filter-slash = fa-icon.with("\u{e17d}") +#let fa-fingerprint = fa-icon.with("\u{f577}") +#let fa-fire = fa-icon.with("\u{f06d}") +#let fa-fire-burner = fa-icon.with("\u{e4f1}") +#let fa-fire-extinguisher = fa-icon.with("\u{f134}") +#let fa-fire-flame = fa-icon.with("\u{f6df}") +#let fa-flame = fa-icon.with("\u{f6df}") +#let fa-fire-flame-curved = fa-icon.with("\u{f7e4}") +#let fa-fire-alt = fa-icon.with("\u{f7e4}") +#let fa-fire-flame-simple = fa-icon.with("\u{f46a}") +#let fa-burn = fa-icon.with("\u{f46a}") +#let fa-firefox = fa-icon.with("\u{f269}") +#let fa-firefox-browser = fa-icon.with("\u{e007}") +#let fa-fire-hydrant = fa-icon.with("\u{e17f}") +#let fa-fireplace = fa-icon.with("\u{f79a}") +#let fa-fire-smoke = fa-icon.with("\u{f74b}") +#let fa-firstdraft = fa-icon.with("\u{f3a1}") +#let fa-first-order = fa-icon.with("\u{f2b0}") +#let fa-first-order-alt = fa-icon.with("\u{f50a}") +#let fa-fish = fa-icon.with("\u{f578}") +#let fa-fish-bones = fa-icon.with("\u{e304}") +#let fa-fish-cooked = fa-icon.with("\u{f7fe}") +#let fa-fish-fins = fa-icon.with("\u{e4f2}") +#let fa-fishing-rod = fa-icon.with("\u{e3a8}") +#let fa-flag = fa-icon.with("\u{f024}") +#let fa-flag-checkered = fa-icon.with("\u{f11e}") +#let fa-flag-pennant = fa-icon.with("\u{f456}") +#let fa-pennant = fa-icon.with("\u{f456}") +#let fa-flag-swallowtail = fa-icon.with("\u{f74c}") +#let fa-flag-alt = fa-icon.with("\u{f74c}") +#let fa-flag-usa = fa-icon.with("\u{f74d}") +#let fa-flashlight = fa-icon.with("\u{f8b8}") +#let fa-flask = fa-icon.with("\u{f0c3}") +#let fa-flask-gear = fa-icon.with("\u{e5f1}") +#let fa-flask-round-poison = fa-icon.with("\u{f6e0}") +#let fa-flask-poison = fa-icon.with("\u{f6e0}") +#let fa-flask-round-potion = fa-icon.with("\u{f6e1}") +#let fa-flask-potion = fa-icon.with("\u{f6e1}") +#let fa-flask-vial = fa-icon.with("\u{e4f3}") +#let fa-flatbread = fa-icon.with("\u{e40b}") +#let fa-flatbread-stuffed = fa-icon.with("\u{e40c}") +#let fa-flickr = fa-icon.with("\u{f16e}") +#let fa-flipboard = fa-icon.with("\u{f44d}") +#let fa-floppy-disk = fa-icon.with("\u{f0c7}") +#let fa-save = fa-icon.with("\u{f0c7}") +#let fa-floppy-disk-circle-arrow-right = fa-icon.with("\u{e180}") +#let fa-save-circle-arrow-right = fa-icon.with("\u{e180}") +#let fa-floppy-disk-circle-xmark = fa-icon.with("\u{e181}") +#let fa-floppy-disk-times = fa-icon.with("\u{e181}") +#let fa-save-circle-xmark = fa-icon.with("\u{e181}") +#let fa-save-times = fa-icon.with("\u{e181}") +#let fa-floppy-disk-pen = fa-icon.with("\u{e182}") +#let fa-floppy-disks = fa-icon.with("\u{e183}") +#let fa-florin-sign = fa-icon.with("\u{e184}") +#let fa-flower = fa-icon.with("\u{f7ff}") +#let fa-flower-daffodil = fa-icon.with("\u{f800}") +#let fa-flower-tulip = fa-icon.with("\u{f801}") +#let fa-flute = fa-icon.with("\u{f8b9}") +#let fa-flutter = fa-icon.with("\u{e694}") +#let fa-flux-capacitor = fa-icon.with("\u{f8ba}") +#let fa-fly = fa-icon.with("\u{f417}") +#let fa-flying-disc = fa-icon.with("\u{e3a9}") +#let fa-folder = fa-icon.with("\u{f07b}") +#let fa-folder-blank = fa-icon.with("\u{f07b}") +#let fa-folder-arrow-down = fa-icon.with("\u{e053}") +#let fa-folder-download = fa-icon.with("\u{e053}") +#let fa-folder-arrow-up = fa-icon.with("\u{e054}") +#let fa-folder-upload = fa-icon.with("\u{e054}") +#let fa-folder-bookmark = fa-icon.with("\u{e186}") +#let fa-folder-check = fa-icon.with("\u{e64e}") +#let fa-folder-closed = fa-icon.with("\u{e185}") +#let fa-folder-gear = fa-icon.with("\u{e187}") +#let fa-folder-cog = fa-icon.with("\u{e187}") +#let fa-folder-grid = fa-icon.with("\u{e188}") +#let fa-folder-heart = fa-icon.with("\u{e189}") +#let fa-folder-image = fa-icon.with("\u{e18a}") +#let fa-folder-magnifying-glass = fa-icon.with("\u{e18b}") +#let fa-folder-search = fa-icon.with("\u{e18b}") +#let fa-folder-medical = fa-icon.with("\u{e18c}") +#let fa-folder-minus = fa-icon.with("\u{f65d}") +#let fa-folder-music = fa-icon.with("\u{e18d}") +#let fa-folder-open = fa-icon.with("\u{f07c}") +#let fa-folder-plus = fa-icon.with("\u{f65e}") +#let fa-folders = fa-icon.with("\u{f660}") +#let fa-folder-tree = fa-icon.with("\u{f802}") +#let fa-folder-user = fa-icon.with("\u{e18e}") +#let fa-folder-xmark = fa-icon.with("\u{f65f}") +#let fa-folder-times = fa-icon.with("\u{f65f}") +#let fa-fondue-pot = fa-icon.with("\u{e40d}") +#let fa-font = fa-icon.with("\u{f031}") +#let fa-font-awesome = fa-icon.with("\u{f2b4}") +#let fa-font-awesome-flag = fa-icon.with("\u{f2b4}") +#let fa-font-awesome-logo-full = fa-icon.with("\u{f2b4}") +#let fa-font-case = fa-icon.with("\u{f866}") +#let fa-fonticons = fa-icon.with("\u{f280}") +#let fa-fonticons-fi = fa-icon.with("\u{f3a2}") +#let fa-football = fa-icon.with("\u{f44e}") +#let fa-football-ball = fa-icon.with("\u{f44e}") +#let fa-football-helmet = fa-icon.with("\u{f44f}") +#let fa-fork = fa-icon.with("\u{f2e3}") +#let fa-utensil-fork = fa-icon.with("\u{f2e3}") +#let fa-fork-knife = fa-icon.with("\u{f2e6}") +#let fa-utensils-alt = fa-icon.with("\u{f2e6}") +#let fa-forklift = fa-icon.with("\u{f47a}") +#let fa-fort = fa-icon.with("\u{e486}") +#let fa-fort-awesome = fa-icon.with("\u{f286}") +#let fa-fort-awesome-alt = fa-icon.with("\u{f3a3}") +#let fa-forumbee = fa-icon.with("\u{f211}") +#let fa-forward = fa-icon.with("\u{f04e}") +#let fa-forward-fast = fa-icon.with("\u{f050}") +#let fa-fast-forward = fa-icon.with("\u{f050}") +#let fa-forward-step = fa-icon.with("\u{f051}") +#let fa-step-forward = fa-icon.with("\u{f051}") +#let fa-foursquare = fa-icon.with("\u{f180}") +#let fa-frame = fa-icon.with("\u{e495}") +#let fa-franc-sign = fa-icon.with("\u{e18f}") +#let fa-freebsd = fa-icon.with("\u{f3a4}") +#let fa-free-code-camp = fa-icon.with("\u{f2c5}") +#let fa-french-fries = fa-icon.with("\u{f803}") +#let fa-frog = fa-icon.with("\u{f52e}") +#let fa-fulcrum = fa-icon.with("\u{f50b}") +#let fa-function = fa-icon.with("\u{f661}") +#let fa-futbol = fa-icon.with("\u{f1e3}") +#let fa-futbol-ball = fa-icon.with("\u{f1e3}") +#let fa-soccer-ball = fa-icon.with("\u{f1e3}") +#let fa-g = fa-icon.with("\u{47}") +#let fa-galactic-republic = fa-icon.with("\u{f50c}") +#let fa-galactic-senate = fa-icon.with("\u{f50d}") +#let fa-galaxy = fa-icon.with("\u{e008}") +#let fa-gallery-thumbnails = fa-icon.with("\u{e3aa}") +#let fa-game-board = fa-icon.with("\u{f867}") +#let fa-game-board-simple = fa-icon.with("\u{f868}") +#let fa-game-board-alt = fa-icon.with("\u{f868}") +#let fa-game-console-handheld = fa-icon.with("\u{f8bb}") +#let fa-game-console-handheld-crank = fa-icon.with("\u{e5b9}") +#let fa-gamepad = fa-icon.with("\u{f11b}") +#let fa-gamepad-modern = fa-icon.with("\u{e5a2}") +#let fa-gamepad-alt = fa-icon.with("\u{e5a2}") +#let fa-garage = fa-icon.with("\u{e009}") +#let fa-garage-car = fa-icon.with("\u{e00a}") +#let fa-garage-open = fa-icon.with("\u{e00b}") +#let fa-garlic = fa-icon.with("\u{e40e}") +#let fa-gas-pump = fa-icon.with("\u{f52f}") +#let fa-gas-pump-slash = fa-icon.with("\u{f5f4}") +#let fa-gauge = fa-icon.with("\u{f624}") +#let fa-dashboard = fa-icon.with("\u{f624}") +#let fa-gauge-med = fa-icon.with("\u{f624}") +#let fa-tachometer-alt-average = fa-icon.with("\u{f624}") +#let fa-gauge-circle-bolt = fa-icon.with("\u{e496}") +#let fa-gauge-circle-minus = fa-icon.with("\u{e497}") +#let fa-gauge-circle-plus = fa-icon.with("\u{e498}") +#let fa-gauge-high = fa-icon.with("\u{f625}") +#let fa-tachometer-alt = fa-icon.with("\u{f625}") +#let fa-tachometer-alt-fast = fa-icon.with("\u{f625}") +#let fa-gauge-low = fa-icon.with("\u{f627}") +#let fa-tachometer-alt-slow = fa-icon.with("\u{f627}") +#let fa-gauge-max = fa-icon.with("\u{f626}") +#let fa-tachometer-alt-fastest = fa-icon.with("\u{f626}") +#let fa-gauge-min = fa-icon.with("\u{f628}") +#let fa-tachometer-alt-slowest = fa-icon.with("\u{f628}") +#let fa-gauge-simple = fa-icon.with("\u{f629}") +#let fa-gauge-simple-med = fa-icon.with("\u{f629}") +#let fa-tachometer-average = fa-icon.with("\u{f629}") +#let fa-gauge-simple-high = fa-icon.with("\u{f62a}") +#let fa-tachometer = fa-icon.with("\u{f62a}") +#let fa-tachometer-fast = fa-icon.with("\u{f62a}") +#let fa-gauge-simple-low = fa-icon.with("\u{f62c}") +#let fa-tachometer-slow = fa-icon.with("\u{f62c}") +#let fa-gauge-simple-max = fa-icon.with("\u{f62b}") +#let fa-tachometer-fastest = fa-icon.with("\u{f62b}") +#let fa-gauge-simple-min = fa-icon.with("\u{f62d}") +#let fa-tachometer-slowest = fa-icon.with("\u{f62d}") +#let fa-gavel = fa-icon.with("\u{f0e3}") +#let fa-legal = fa-icon.with("\u{f0e3}") +#let fa-gear = fa-icon.with("\u{f013}") +#let fa-cog = fa-icon.with("\u{f013}") +#let fa-gear-code = fa-icon.with("\u{e5e8}") +#let fa-gear-complex = fa-icon.with("\u{e5e9}") +#let fa-gear-complex-code = fa-icon.with("\u{e5eb}") +#let fa-gears = fa-icon.with("\u{f085}") +#let fa-cogs = fa-icon.with("\u{f085}") +#let fa-gem = fa-icon.with("\u{f3a5}") +#let fa-genderless = fa-icon.with("\u{f22d}") +#let fa-get-pocket = fa-icon.with("\u{f265}") +#let fa-gg = fa-icon.with("\u{f260}") +#let fa-gg-circle = fa-icon.with("\u{f261}") +#let fa-ghost = fa-icon.with("\u{f6e2}") +#let fa-gif = fa-icon.with("\u{e190}") +#let fa-gift = fa-icon.with("\u{f06b}") +#let fa-gift-card = fa-icon.with("\u{f663}") +#let fa-gifts = fa-icon.with("\u{f79c}") +#let fa-gingerbread-man = fa-icon.with("\u{f79d}") +#let fa-git = fa-icon.with("\u{f1d3}") +#let fa-git-alt = fa-icon.with("\u{f841}") +#let fa-github = fa-icon.with("\u{f09b}") +#let fa-github-alt = fa-icon.with("\u{f113}") +#let fa-gitkraken = fa-icon.with("\u{f3a6}") +#let fa-gitlab = fa-icon.with("\u{f296}") +#let fa-gitter = fa-icon.with("\u{f426}") +#let fa-glass = fa-icon.with("\u{f804}") +#let fa-glass-citrus = fa-icon.with("\u{f869}") +#let fa-glass-empty = fa-icon.with("\u{e191}") +#let fa-glasses = fa-icon.with("\u{f530}") +#let fa-glasses-round = fa-icon.with("\u{f5f5}") +#let fa-glasses-alt = fa-icon.with("\u{f5f5}") +#let fa-glass-half = fa-icon.with("\u{e192}") +#let fa-glass-half-empty = fa-icon.with("\u{e192}") +#let fa-glass-half-full = fa-icon.with("\u{e192}") +#let fa-glass-water = fa-icon.with("\u{e4f4}") +#let fa-glass-water-droplet = fa-icon.with("\u{e4f5}") +#let fa-glide = fa-icon.with("\u{f2a5}") +#let fa-glide-g = fa-icon.with("\u{f2a6}") +#let fa-globe = fa-icon.with("\u{f0ac}") +#let fa-globe-pointer = fa-icon.with("\u{e60e}") +#let fa-globe-snow = fa-icon.with("\u{f7a3}") +#let fa-globe-stand = fa-icon.with("\u{f5f6}") +#let fa-globe-wifi = fa-icon.with("\u{e685}") +#let fa-goal-net = fa-icon.with("\u{e3ab}") +#let fa-gofore = fa-icon.with("\u{f3a7}") +#let fa-golang = fa-icon.with("\u{e40f}") +#let fa-golf-ball-tee = fa-icon.with("\u{f450}") +#let fa-golf-ball = fa-icon.with("\u{f450}") +#let fa-golf-club = fa-icon.with("\u{f451}") +#let fa-golf-flag-hole = fa-icon.with("\u{e3ac}") +#let fa-goodreads = fa-icon.with("\u{f3a8}") +#let fa-goodreads-g = fa-icon.with("\u{f3a9}") +#let fa-google = fa-icon.with("\u{f1a0}") +#let fa-google-drive = fa-icon.with("\u{f3aa}") +#let fa-google-pay = fa-icon.with("\u{e079}") +#let fa-google-play = fa-icon.with("\u{f3ab}") +#let fa-google-plus = fa-icon.with("\u{f2b3}") +#let fa-google-plus-g = fa-icon.with("\u{f0d5}") +#let fa-google-scholar = fa-icon.with("\u{e63b}") +#let fa-google-wallet = fa-icon.with("\u{f1ee}") +#let fa-gopuram = fa-icon.with("\u{f664}") +#let fa-graduation-cap = fa-icon.with("\u{f19d}") +#let fa-mortar-board = fa-icon.with("\u{f19d}") +#let fa-gramophone = fa-icon.with("\u{f8bd}") +#let fa-grapes = fa-icon.with("\u{e306}") +#let fa-grate = fa-icon.with("\u{e193}") +#let fa-grate-droplet = fa-icon.with("\u{e194}") +#let fa-gratipay = fa-icon.with("\u{f184}") +#let fa-grav = fa-icon.with("\u{f2d6}") +#let fa-greater-than = fa-icon.with("\u{3e}") +#let fa-greater-than-equal = fa-icon.with("\u{f532}") +#let fa-grid = fa-icon.with("\u{e195}") +#let fa-grid-3 = fa-icon.with("\u{e195}") +#let fa-grid-2 = fa-icon.with("\u{e196}") +#let fa-grid-2-plus = fa-icon.with("\u{e197}") +#let fa-grid-4 = fa-icon.with("\u{e198}") +#let fa-grid-5 = fa-icon.with("\u{e199}") +#let fa-grid-dividers = fa-icon.with("\u{e3ad}") +#let fa-grid-horizontal = fa-icon.with("\u{e307}") +#let fa-grid-round = fa-icon.with("\u{e5da}") +#let fa-grid-round-2 = fa-icon.with("\u{e5db}") +#let fa-grid-round-2-plus = fa-icon.with("\u{e5dc}") +#let fa-grid-round-4 = fa-icon.with("\u{e5dd}") +#let fa-grid-round-5 = fa-icon.with("\u{e5de}") +#let fa-grill = fa-icon.with("\u{e5a3}") +#let fa-grill-fire = fa-icon.with("\u{e5a4}") +#let fa-grill-hot = fa-icon.with("\u{e5a5}") +#let fa-grip = fa-icon.with("\u{f58d}") +#let fa-grip-horizontal = fa-icon.with("\u{f58d}") +#let fa-grip-dots = fa-icon.with("\u{e410}") +#let fa-grip-dots-vertical = fa-icon.with("\u{e411}") +#let fa-gripfire = fa-icon.with("\u{f3ac}") +#let fa-grip-lines = fa-icon.with("\u{f7a4}") +#let fa-grip-lines-vertical = fa-icon.with("\u{f7a5}") +#let fa-grip-vertical = fa-icon.with("\u{f58e}") +#let fa-group-arrows-rotate = fa-icon.with("\u{e4f6}") +#let fa-grunt = fa-icon.with("\u{f3ad}") +#let fa-guarani-sign = fa-icon.with("\u{e19a}") +#let fa-guilded = fa-icon.with("\u{e07e}") +#let fa-guitar = fa-icon.with("\u{f7a6}") +#let fa-guitar-electric = fa-icon.with("\u{f8be}") +#let fa-guitars = fa-icon.with("\u{f8bf}") +#let fa-gulp = fa-icon.with("\u{f3ae}") +#let fa-gun = fa-icon.with("\u{e19b}") +#let fa-gun-slash = fa-icon.with("\u{e19c}") +#let fa-gun-squirt = fa-icon.with("\u{e19d}") +#let fa-h = fa-icon.with("\u{48}") +#let fa-h1 = fa-icon.with("\u{f313}") +#let fa-h2 = fa-icon.with("\u{f314}") +#let fa-h3 = fa-icon.with("\u{f315}") +#let fa-h4 = fa-icon.with("\u{f86a}") +#let fa-h5 = fa-icon.with("\u{e412}") +#let fa-h6 = fa-icon.with("\u{e413}") +#let fa-hacker-news = fa-icon.with("\u{f1d4}") +#let fa-hackerrank = fa-icon.with("\u{f5f7}") +#let fa-hammer = fa-icon.with("\u{f6e3}") +#let fa-hammer-brush = fa-icon.with("\u{e620}") +#let fa-hammer-crash = fa-icon.with("\u{e414}") +#let fa-hammer-war = fa-icon.with("\u{f6e4}") +#let fa-hamsa = fa-icon.with("\u{f665}") +#let fa-hand = fa-icon.with("\u{f256}") +#let fa-hand-paper = fa-icon.with("\u{f256}") +#let fa-hand-back-fist = fa-icon.with("\u{f255}") +#let fa-hand-rock = fa-icon.with("\u{f255}") +#let fa-hand-back-point-down = fa-icon.with("\u{e19e}") +#let fa-hand-back-point-left = fa-icon.with("\u{e19f}") +#let fa-hand-back-point-ribbon = fa-icon.with("\u{e1a0}") +#let fa-hand-back-point-right = fa-icon.with("\u{e1a1}") +#let fa-hand-back-point-up = fa-icon.with("\u{e1a2}") +#let fa-handcuffs = fa-icon.with("\u{e4f8}") +#let fa-hand-dots = fa-icon.with("\u{f461}") +#let fa-allergies = fa-icon.with("\u{f461}") +#let fa-hand-fingers-crossed = fa-icon.with("\u{e1a3}") +#let fa-hand-fist = fa-icon.with("\u{f6de}") +#let fa-fist-raised = fa-icon.with("\u{f6de}") +#let fa-hand-heart = fa-icon.with("\u{f4bc}") +#let fa-hand-holding = fa-icon.with("\u{f4bd}") +#let fa-hand-holding-box = fa-icon.with("\u{f47b}") +#let fa-hand-holding-circle-dollar = fa-icon.with("\u{e621}") +#let fa-hand-holding-dollar = fa-icon.with("\u{f4c0}") +#let fa-hand-holding-usd = fa-icon.with("\u{f4c0}") +#let fa-hand-holding-droplet = fa-icon.with("\u{f4c1}") +#let fa-hand-holding-water = fa-icon.with("\u{f4c1}") +#let fa-hand-holding-hand = fa-icon.with("\u{e4f7}") +#let fa-hand-holding-heart = fa-icon.with("\u{f4be}") +#let fa-hand-holding-magic = fa-icon.with("\u{f6e5}") +#let fa-hand-holding-medical = fa-icon.with("\u{e05c}") +#let fa-hand-holding-seedling = fa-icon.with("\u{f4bf}") +#let fa-hand-holding-skull = fa-icon.with("\u{e1a4}") +#let fa-hand-horns = fa-icon.with("\u{e1a9}") +#let fa-hand-lizard = fa-icon.with("\u{f258}") +#let fa-hand-love = fa-icon.with("\u{e1a5}") +#let fa-hand-middle-finger = fa-icon.with("\u{f806}") +#let fa-hand-peace = fa-icon.with("\u{f25b}") +#let fa-hand-point-down = fa-icon.with("\u{f0a7}") +#let fa-hand-pointer = fa-icon.with("\u{f25a}") +#let fa-hand-point-left = fa-icon.with("\u{f0a5}") +#let fa-hand-point-ribbon = fa-icon.with("\u{e1a6}") +#let fa-hand-point-right = fa-icon.with("\u{f0a4}") +#let fa-hand-point-up = fa-icon.with("\u{f0a6}") +#let fa-hands = fa-icon.with("\u{f2a7}") +#let fa-sign-language = fa-icon.with("\u{f2a7}") +#let fa-signing = fa-icon.with("\u{f2a7}") +#let fa-hands-asl-interpreting = fa-icon.with("\u{f2a3}") +#let fa-american-sign-language-interpreting = fa-icon.with("\u{f2a3}") +#let fa-asl-interpreting = fa-icon.with("\u{f2a3}") +#let fa-hands-american-sign-language-interpreting = fa-icon.with("\u{f2a3}") +#let fa-hands-bound = fa-icon.with("\u{e4f9}") +#let fa-hands-bubbles = fa-icon.with("\u{e05e}") +#let fa-hands-wash = fa-icon.with("\u{e05e}") +#let fa-hand-scissors = fa-icon.with("\u{f257}") +#let fa-hands-clapping = fa-icon.with("\u{e1a8}") +#let fa-handshake = fa-icon.with("\u{f2b5}") +#let fa-handshake-angle = fa-icon.with("\u{f4c4}") +#let fa-hands-helping = fa-icon.with("\u{f4c4}") +#let fa-handshake-simple = fa-icon.with("\u{f4c6}") +#let fa-handshake-alt = fa-icon.with("\u{f4c6}") +#let fa-handshake-simple-slash = fa-icon.with("\u{e05f}") +#let fa-handshake-alt-slash = fa-icon.with("\u{e05f}") +#let fa-handshake-slash = fa-icon.with("\u{e060}") +#let fa-hands-holding = fa-icon.with("\u{f4c2}") +#let fa-hands-holding-child = fa-icon.with("\u{e4fa}") +#let fa-hands-holding-circle = fa-icon.with("\u{e4fb}") +#let fa-hands-holding-diamond = fa-icon.with("\u{f47c}") +#let fa-hand-receiving = fa-icon.with("\u{f47c}") +#let fa-hands-holding-dollar = fa-icon.with("\u{f4c5}") +#let fa-hands-usd = fa-icon.with("\u{f4c5}") +#let fa-hands-holding-heart = fa-icon.with("\u{f4c3}") +#let fa-hands-heart = fa-icon.with("\u{f4c3}") +#let fa-hand-sparkles = fa-icon.with("\u{e05d}") +#let fa-hand-spock = fa-icon.with("\u{f259}") +#let fa-hands-praying = fa-icon.with("\u{f684}") +#let fa-praying-hands = fa-icon.with("\u{f684}") +#let fa-hand-wave = fa-icon.with("\u{e1a7}") +#let fa-hanukiah = fa-icon.with("\u{f6e6}") +#let fa-hard-drive = fa-icon.with("\u{f0a0}") +#let fa-hdd = fa-icon.with("\u{f0a0}") +#let fa-hashnode = fa-icon.with("\u{e499}") +#let fa-hashtag = fa-icon.with("\u{23}") +#let fa-hashtag-lock = fa-icon.with("\u{e415}") +#let fa-hat-beach = fa-icon.with("\u{e606}") +#let fa-hat-chef = fa-icon.with("\u{f86b}") +#let fa-hat-cowboy = fa-icon.with("\u{f8c0}") +#let fa-hat-cowboy-side = fa-icon.with("\u{f8c1}") +#let fa-hat-santa = fa-icon.with("\u{f7a7}") +#let fa-hat-winter = fa-icon.with("\u{f7a8}") +#let fa-hat-witch = fa-icon.with("\u{f6e7}") +#let fa-hat-wizard = fa-icon.with("\u{f6e8}") +#let fa-heading = fa-icon.with("\u{f1dc}") +#let fa-header = fa-icon.with("\u{f1dc}") +#let fa-headphones = fa-icon.with("\u{f025}") +#let fa-headphones-simple = fa-icon.with("\u{f58f}") +#let fa-headphones-alt = fa-icon.with("\u{f58f}") +#let fa-headset = fa-icon.with("\u{f590}") +#let fa-head-side = fa-icon.with("\u{f6e9}") +#let fa-head-side-brain = fa-icon.with("\u{f808}") +#let fa-head-side-cough = fa-icon.with("\u{e061}") +#let fa-head-side-cough-slash = fa-icon.with("\u{e062}") +#let fa-head-side-gear = fa-icon.with("\u{e611}") +#let fa-head-side-goggles = fa-icon.with("\u{f6ea}") +#let fa-head-vr = fa-icon.with("\u{f6ea}") +#let fa-head-side-headphones = fa-icon.with("\u{f8c2}") +#let fa-head-side-heart = fa-icon.with("\u{e1aa}") +#let fa-head-side-mask = fa-icon.with("\u{e063}") +#let fa-head-side-medical = fa-icon.with("\u{f809}") +#let fa-head-side-virus = fa-icon.with("\u{e064}") +#let fa-heart = fa-icon.with("\u{f004}") +#let fa-heart-circle-bolt = fa-icon.with("\u{e4fc}") +#let fa-heart-circle-check = fa-icon.with("\u{e4fd}") +#let fa-heart-circle-exclamation = fa-icon.with("\u{e4fe}") +#let fa-heart-circle-minus = fa-icon.with("\u{e4ff}") +#let fa-heart-circle-plus = fa-icon.with("\u{e500}") +#let fa-heart-circle-xmark = fa-icon.with("\u{e501}") +#let fa-heart-crack = fa-icon.with("\u{f7a9}") +#let fa-heart-broken = fa-icon.with("\u{f7a9}") +#let fa-heart-half = fa-icon.with("\u{e1ab}") +#let fa-heart-half-stroke = fa-icon.with("\u{e1ac}") +#let fa-heart-half-alt = fa-icon.with("\u{e1ac}") +#let fa-heart-pulse = fa-icon.with("\u{f21e}") +#let fa-heartbeat = fa-icon.with("\u{f21e}") +#let fa-heat = fa-icon.with("\u{e00c}") +#let fa-helicopter = fa-icon.with("\u{f533}") +#let fa-helicopter-symbol = fa-icon.with("\u{e502}") +#let fa-helmet-battle = fa-icon.with("\u{f6eb}") +#let fa-helmet-safety = fa-icon.with("\u{f807}") +#let fa-hard-hat = fa-icon.with("\u{f807}") +#let fa-hat-hard = fa-icon.with("\u{f807}") +#let fa-helmet-un = fa-icon.with("\u{e503}") +#let fa-hexagon = fa-icon.with("\u{f312}") +#let fa-hexagon-check = fa-icon.with("\u{e416}") +#let fa-hexagon-divide = fa-icon.with("\u{e1ad}") +#let fa-hexagon-exclamation = fa-icon.with("\u{e417}") +#let fa-hexagon-image = fa-icon.with("\u{e504}") +#let fa-hexagon-minus = fa-icon.with("\u{f307}") +#let fa-minus-hexagon = fa-icon.with("\u{f307}") +#let fa-hexagon-plus = fa-icon.with("\u{f300}") +#let fa-plus-hexagon = fa-icon.with("\u{f300}") +#let fa-hexagon-vertical-nft = fa-icon.with("\u{e505}") +#let fa-hexagon-vertical-nft-slanted = fa-icon.with("\u{e506}") +#let fa-hexagon-xmark = fa-icon.with("\u{f2ee}") +#let fa-times-hexagon = fa-icon.with("\u{f2ee}") +#let fa-xmark-hexagon = fa-icon.with("\u{f2ee}") +#let fa-high-definition = fa-icon.with("\u{e1ae}") +#let fa-rectangle-hd = fa-icon.with("\u{e1ae}") +#let fa-highlighter = fa-icon.with("\u{f591}") +#let fa-highlighter-line = fa-icon.with("\u{e1af}") +#let fa-hill-avalanche = fa-icon.with("\u{e507}") +#let fa-hill-rockslide = fa-icon.with("\u{e508}") +#let fa-hippo = fa-icon.with("\u{f6ed}") +#let fa-hips = fa-icon.with("\u{f452}") +#let fa-hire-a-helper = fa-icon.with("\u{f3b0}") +#let fa-hive = fa-icon.with("\u{e07f}") +#let fa-hockey-mask = fa-icon.with("\u{f6ee}") +#let fa-hockey-puck = fa-icon.with("\u{f453}") +#let fa-hockey-stick-puck = fa-icon.with("\u{e3ae}") +#let fa-hockey-sticks = fa-icon.with("\u{f454}") +#let fa-holly-berry = fa-icon.with("\u{f7aa}") +#let fa-honey-pot = fa-icon.with("\u{e418}") +#let fa-hood-cloak = fa-icon.with("\u{f6ef}") +#let fa-hooli = fa-icon.with("\u{f427}") +#let fa-horizontal-rule = fa-icon.with("\u{f86c}") +#let fa-hornbill = fa-icon.with("\u{f592}") +#let fa-horse = fa-icon.with("\u{f6f0}") +#let fa-horse-head = fa-icon.with("\u{f7ab}") +#let fa-horse-saddle = fa-icon.with("\u{f8c3}") +#let fa-hose = fa-icon.with("\u{e419}") +#let fa-hose-reel = fa-icon.with("\u{e41a}") +#let fa-hospital = fa-icon.with("\u{f0f8}") +#let fa-hospital-alt = fa-icon.with("\u{f0f8}") +#let fa-hospital-wide = fa-icon.with("\u{f0f8}") +#let fa-hospitals = fa-icon.with("\u{f80e}") +#let fa-hospital-user = fa-icon.with("\u{f80d}") +#let fa-hotdog = fa-icon.with("\u{f80f}") +#let fa-hotel = fa-icon.with("\u{f594}") +#let fa-hotjar = fa-icon.with("\u{f3b1}") +#let fa-hot-tub-person = fa-icon.with("\u{f593}") +#let fa-hot-tub = fa-icon.with("\u{f593}") +#let fa-hourglass = fa-icon.with("\u{f254}") +#let fa-hourglass-empty = fa-icon.with("\u{f254}") +#let fa-hourglass-clock = fa-icon.with("\u{e41b}") +#let fa-hourglass-end = fa-icon.with("\u{f253}") +#let fa-hourglass-3 = fa-icon.with("\u{f253}") +#let fa-hourglass-half = fa-icon.with("\u{f252}") +#let fa-hourglass-2 = fa-icon.with("\u{f252}") +#let fa-hourglass-start = fa-icon.with("\u{f251}") +#let fa-hourglass-1 = fa-icon.with("\u{f251}") +#let fa-house = fa-icon.with("\u{f015}") +#let fa-home = fa-icon.with("\u{f015}") +#let fa-home-alt = fa-icon.with("\u{f015}") +#let fa-home-lg-alt = fa-icon.with("\u{f015}") +#let fa-house-blank = fa-icon.with("\u{e487}") +#let fa-home-blank = fa-icon.with("\u{e487}") +#let fa-house-building = fa-icon.with("\u{e1b1}") +#let fa-house-chimney = fa-icon.with("\u{e3af}") +#let fa-home-lg = fa-icon.with("\u{e3af}") +#let fa-house-chimney-blank = fa-icon.with("\u{e3b0}") +#let fa-house-chimney-crack = fa-icon.with("\u{f6f1}") +#let fa-house-damage = fa-icon.with("\u{f6f1}") +#let fa-house-chimney-heart = fa-icon.with("\u{e1b2}") +#let fa-house-chimney-medical = fa-icon.with("\u{f7f2}") +#let fa-clinic-medical = fa-icon.with("\u{f7f2}") +#let fa-house-chimney-user = fa-icon.with("\u{e065}") +#let fa-house-chimney-window = fa-icon.with("\u{e00d}") +#let fa-house-circle-check = fa-icon.with("\u{e509}") +#let fa-house-circle-exclamation = fa-icon.with("\u{e50a}") +#let fa-house-circle-xmark = fa-icon.with("\u{e50b}") +#let fa-house-crack = fa-icon.with("\u{e3b1}") +#let fa-house-day = fa-icon.with("\u{e00e}") +#let fa-house-fire = fa-icon.with("\u{e50c}") +#let fa-house-flag = fa-icon.with("\u{e50d}") +#let fa-house-flood-water = fa-icon.with("\u{e50e}") +#let fa-house-flood-water-circle-arrow-right = fa-icon.with("\u{e50f}") +#let fa-house-heart = fa-icon.with("\u{f4c9}") +#let fa-home-heart = fa-icon.with("\u{f4c9}") +#let fa-house-laptop = fa-icon.with("\u{e066}") +#let fa-laptop-house = fa-icon.with("\u{e066}") +#let fa-house-lock = fa-icon.with("\u{e510}") +#let fa-house-medical = fa-icon.with("\u{e3b2}") +#let fa-house-medical-circle-check = fa-icon.with("\u{e511}") +#let fa-house-medical-circle-exclamation = fa-icon.with("\u{e512}") +#let fa-house-medical-circle-xmark = fa-icon.with("\u{e513}") +#let fa-house-medical-flag = fa-icon.with("\u{e514}") +#let fa-house-night = fa-icon.with("\u{e010}") +#let fa-house-person-leave = fa-icon.with("\u{e00f}") +#let fa-house-leave = fa-icon.with("\u{e00f}") +#let fa-house-person-depart = fa-icon.with("\u{e00f}") +#let fa-house-person-return = fa-icon.with("\u{e011}") +#let fa-house-person-arrive = fa-icon.with("\u{e011}") +#let fa-house-return = fa-icon.with("\u{e011}") +#let fa-house-signal = fa-icon.with("\u{e012}") +#let fa-house-tree = fa-icon.with("\u{e1b3}") +#let fa-house-tsunami = fa-icon.with("\u{e515}") +#let fa-house-turret = fa-icon.with("\u{e1b4}") +#let fa-house-user = fa-icon.with("\u{e1b0}") +#let fa-home-user = fa-icon.with("\u{e1b0}") +#let fa-house-water = fa-icon.with("\u{f74f}") +#let fa-house-flood = fa-icon.with("\u{f74f}") +#let fa-house-window = fa-icon.with("\u{e3b3}") +#let fa-houzz = fa-icon.with("\u{f27c}") +#let fa-hryvnia-sign = fa-icon.with("\u{f6f2}") +#let fa-hryvnia = fa-icon.with("\u{f6f2}") +#let fa-html5 = fa-icon.with("\u{f13b}") +#let fa-hubspot = fa-icon.with("\u{f3b2}") +#let fa-hundred-points = fa-icon.with("\u{e41c}") +#let fa-100 = fa-icon.with("\u{e41c}") +#let fa-hurricane = fa-icon.with("\u{f751}") +#let fa-hydra = fa-icon.with("\u{e686}") +#let fa-hyphen = fa-icon.with("\u{2d}") +#let fa-i = fa-icon.with("\u{49}") +#let fa-ice-cream = fa-icon.with("\u{f810}") +#let fa-ice-skate = fa-icon.with("\u{f7ac}") +#let fa-icicles = fa-icon.with("\u{f7ad}") +#let fa-icons = fa-icon.with("\u{f86d}") +#let fa-heart-music-camera-bolt = fa-icon.with("\u{f86d}") +#let fa-i-cursor = fa-icon.with("\u{f246}") +#let fa-id-badge = fa-icon.with("\u{f2c1}") +#let fa-id-card = fa-icon.with("\u{f2c2}") +#let fa-drivers-license = fa-icon.with("\u{f2c2}") +#let fa-id-card-clip = fa-icon.with("\u{f47f}") +#let fa-id-card-alt = fa-icon.with("\u{f47f}") +#let fa-ideal = fa-icon.with("\u{e013}") +#let fa-igloo = fa-icon.with("\u{f7ae}") +#let fa-image = fa-icon.with("\u{f03e}") +#let fa-image-landscape = fa-icon.with("\u{e1b5}") +#let fa-landscape = fa-icon.with("\u{e1b5}") +#let fa-image-polaroid = fa-icon.with("\u{f8c4}") +#let fa-image-polaroid-user = fa-icon.with("\u{e1b6}") +#let fa-image-portrait = fa-icon.with("\u{f3e0}") +#let fa-portrait = fa-icon.with("\u{f3e0}") +#let fa-images = fa-icon.with("\u{f302}") +#let fa-image-slash = fa-icon.with("\u{e1b7}") +#let fa-images-user = fa-icon.with("\u{e1b9}") +#let fa-image-user = fa-icon.with("\u{e1b8}") +#let fa-imdb = fa-icon.with("\u{f2d8}") +#let fa-inbox = fa-icon.with("\u{f01c}") +#let fa-inboxes = fa-icon.with("\u{e1bb}") +#let fa-inbox-full = fa-icon.with("\u{e1ba}") +#let fa-inbox-in = fa-icon.with("\u{f310}") +#let fa-inbox-arrow-down = fa-icon.with("\u{f310}") +#let fa-inbox-out = fa-icon.with("\u{f311}") +#let fa-inbox-arrow-up = fa-icon.with("\u{f311}") +#let fa-indent = fa-icon.with("\u{f03c}") +#let fa-indian-rupee-sign = fa-icon.with("\u{e1bc}") +#let fa-indian-rupee = fa-icon.with("\u{e1bc}") +#let fa-inr = fa-icon.with("\u{e1bc}") +#let fa-industry = fa-icon.with("\u{f275}") +#let fa-industry-windows = fa-icon.with("\u{f3b3}") +#let fa-industry-alt = fa-icon.with("\u{f3b3}") +#let fa-infinity = fa-icon.with("\u{f534}") +#let fa-info = fa-icon.with("\u{f129}") +#let fa-inhaler = fa-icon.with("\u{f5f9}") +#let fa-input-numeric = fa-icon.with("\u{e1bd}") +#let fa-input-pipe = fa-icon.with("\u{e1be}") +#let fa-input-text = fa-icon.with("\u{e1bf}") +#let fa-instagram = fa-icon.with("\u{f16d}") +#let fa-instalod = fa-icon.with("\u{e081}") +#let fa-integral = fa-icon.with("\u{f667}") +#let fa-intercom = fa-icon.with("\u{f7af}") +#let fa-internet-explorer = fa-icon.with("\u{f26b}") +#let fa-interrobang = fa-icon.with("\u{e5ba}") +#let fa-intersection = fa-icon.with("\u{f668}") +#let fa-invision = fa-icon.with("\u{f7b0}") +#let fa-ioxhost = fa-icon.with("\u{f208}") +#let fa-island-tropical = fa-icon.with("\u{f811}") +#let fa-island-tree-palm = fa-icon.with("\u{f811}") +#let fa-italic = fa-icon.with("\u{f033}") +#let fa-itch-io = fa-icon.with("\u{f83a}") +#let fa-itunes = fa-icon.with("\u{f3b4}") +#let fa-itunes-note = fa-icon.with("\u{f3b5}") +#let fa-j = fa-icon.with("\u{4a}") +#let fa-jack-o-lantern = fa-icon.with("\u{f30e}") +#let fa-jar = fa-icon.with("\u{e516}") +#let fa-jar-wheat = fa-icon.with("\u{e517}") +#let fa-java = fa-icon.with("\u{f4e4}") +#let fa-jedi = fa-icon.with("\u{f669}") +#let fa-jedi-order = fa-icon.with("\u{f50e}") +#let fa-jenkins = fa-icon.with("\u{f3b6}") +#let fa-jet-fighter = fa-icon.with("\u{f0fb}") +#let fa-fighter-jet = fa-icon.with("\u{f0fb}") +#let fa-jet-fighter-up = fa-icon.with("\u{e518}") +#let fa-jira = fa-icon.with("\u{f7b1}") +#let fa-joget = fa-icon.with("\u{f3b7}") +#let fa-joint = fa-icon.with("\u{f595}") +#let fa-joomla = fa-icon.with("\u{f1aa}") +#let fa-joystick = fa-icon.with("\u{f8c5}") +#let fa-js = fa-icon.with("\u{f3b8}") +#let fa-jsfiddle = fa-icon.with("\u{f1cc}") +#let fa-jug = fa-icon.with("\u{f8c6}") +#let fa-jug-bottle = fa-icon.with("\u{e5fb}") +#let fa-jug-detergent = fa-icon.with("\u{e519}") +#let fa-jxl = fa-icon.with("\u{e67b}") +#let fa-k = fa-icon.with("\u{4b}") +#let fa-kaaba = fa-icon.with("\u{f66b}") +#let fa-kaggle = fa-icon.with("\u{f5fa}") +#let fa-kazoo = fa-icon.with("\u{f8c7}") +#let fa-kerning = fa-icon.with("\u{f86f}") +#let fa-key = fa-icon.with("\u{f084}") +#let fa-keybase = fa-icon.with("\u{f4f5}") +#let fa-keyboard = fa-icon.with("\u{f11c}") +#let fa-keyboard-brightness = fa-icon.with("\u{e1c0}") +#let fa-keyboard-brightness-low = fa-icon.with("\u{e1c1}") +#let fa-keyboard-down = fa-icon.with("\u{e1c2}") +#let fa-keyboard-left = fa-icon.with("\u{e1c3}") +#let fa-keycdn = fa-icon.with("\u{f3ba}") +#let fa-keynote = fa-icon.with("\u{f66c}") +#let fa-key-skeleton = fa-icon.with("\u{f6f3}") +#let fa-key-skeleton-left-right = fa-icon.with("\u{e3b4}") +#let fa-khanda = fa-icon.with("\u{f66d}") +#let fa-kickstarter = fa-icon.with("\u{f3bb}") +#let fa-square-kickstarter = fa-icon.with("\u{f3bb}") +#let fa-kickstarter-k = fa-icon.with("\u{f3bc}") +#let fa-kidneys = fa-icon.with("\u{f5fb}") +#let fa-kip-sign = fa-icon.with("\u{e1c4}") +#let fa-kitchen-set = fa-icon.with("\u{e51a}") +#let fa-kite = fa-icon.with("\u{f6f4}") +#let fa-kit-medical = fa-icon.with("\u{f479}") +#let fa-first-aid = fa-icon.with("\u{f479}") +#let fa-kiwi-bird = fa-icon.with("\u{f535}") +#let fa-kiwi-fruit = fa-icon.with("\u{e30c}") +#let fa-knife = fa-icon.with("\u{f2e4}") +#let fa-utensil-knife = fa-icon.with("\u{f2e4}") +#let fa-knife-kitchen = fa-icon.with("\u{f6f5}") +#let fa-korvue = fa-icon.with("\u{f42f}") +#let fa-l = fa-icon.with("\u{4c}") +#let fa-lacrosse-stick = fa-icon.with("\u{e3b5}") +#let fa-lacrosse-stick-ball = fa-icon.with("\u{e3b6}") +#let fa-lambda = fa-icon.with("\u{f66e}") +#let fa-lamp = fa-icon.with("\u{f4ca}") +#let fa-lamp-desk = fa-icon.with("\u{e014}") +#let fa-lamp-floor = fa-icon.with("\u{e015}") +#let fa-lamp-street = fa-icon.with("\u{e1c5}") +#let fa-landmark = fa-icon.with("\u{f66f}") +#let fa-landmark-dome = fa-icon.with("\u{f752}") +#let fa-landmark-alt = fa-icon.with("\u{f752}") +#let fa-landmark-flag = fa-icon.with("\u{e51c}") +#let fa-landmark-magnifying-glass = fa-icon.with("\u{e622}") +#let fa-land-mine-on = fa-icon.with("\u{e51b}") +#let fa-language = fa-icon.with("\u{f1ab}") +#let fa-laptop = fa-icon.with("\u{f109}") +#let fa-laptop-arrow-down = fa-icon.with("\u{e1c6}") +#let fa-laptop-binary = fa-icon.with("\u{e5e7}") +#let fa-laptop-code = fa-icon.with("\u{f5fc}") +#let fa-laptop-file = fa-icon.with("\u{e51d}") +#let fa-laptop-medical = fa-icon.with("\u{f812}") +#let fa-laptop-mobile = fa-icon.with("\u{f87a}") +#let fa-phone-laptop = fa-icon.with("\u{f87a}") +#let fa-laptop-slash = fa-icon.with("\u{e1c7}") +#let fa-laravel = fa-icon.with("\u{f3bd}") +#let fa-lari-sign = fa-icon.with("\u{e1c8}") +#let fa-lasso = fa-icon.with("\u{f8c8}") +#let fa-lasso-sparkles = fa-icon.with("\u{e1c9}") +#let fa-lastfm = fa-icon.with("\u{f202}") +#let fa-layer-group = fa-icon.with("\u{f5fd}") +#let fa-layer-minus = fa-icon.with("\u{f5fe}") +#let fa-layer-group-minus = fa-icon.with("\u{f5fe}") +#let fa-layer-plus = fa-icon.with("\u{f5ff}") +#let fa-layer-group-plus = fa-icon.with("\u{f5ff}") +#let fa-leaf = fa-icon.with("\u{f06c}") +#let fa-leaf-heart = fa-icon.with("\u{f4cb}") +#let fa-leaf-maple = fa-icon.with("\u{f6f6}") +#let fa-leaf-oak = fa-icon.with("\u{f6f7}") +#let fa-leafy-green = fa-icon.with("\u{e41d}") +#let fa-leanpub = fa-icon.with("\u{f212}") +#let fa-left = fa-icon.with("\u{f355}") +#let fa-arrow-alt-left = fa-icon.with("\u{f355}") +#let fa-left-from-bracket = fa-icon.with("\u{e66c}") +#let fa-left-from-line = fa-icon.with("\u{f348}") +#let fa-arrow-alt-from-right = fa-icon.with("\u{f348}") +#let fa-left-long = fa-icon.with("\u{f30a}") +#let fa-long-arrow-alt-left = fa-icon.with("\u{f30a}") +#let fa-left-long-to-line = fa-icon.with("\u{e41e}") +#let fa-left-right = fa-icon.with("\u{f337}") +#let fa-arrows-alt-h = fa-icon.with("\u{f337}") +#let fa-left-to-bracket = fa-icon.with("\u{e66d}") +#let fa-left-to-line = fa-icon.with("\u{f34b}") +#let fa-arrow-alt-to-left = fa-icon.with("\u{f34b}") +#let fa-lemon = fa-icon.with("\u{f094}") +#let fa-less = fa-icon.with("\u{f41d}") +#let fa-less-than = fa-icon.with("\u{3c}") +#let fa-less-than-equal = fa-icon.with("\u{f537}") +#let fa-letterboxd = fa-icon.with("\u{e62d}") +#let fa-life-ring = fa-icon.with("\u{f1cd}") +#let fa-lightbulb = fa-icon.with("\u{f0eb}") +#let fa-lightbulb-cfl = fa-icon.with("\u{e5a6}") +#let fa-lightbulb-cfl-on = fa-icon.with("\u{e5a7}") +#let fa-lightbulb-dollar = fa-icon.with("\u{f670}") +#let fa-lightbulb-exclamation = fa-icon.with("\u{f671}") +#let fa-lightbulb-exclamation-on = fa-icon.with("\u{e1ca}") +#let fa-lightbulb-gear = fa-icon.with("\u{e5fd}") +#let fa-lightbulb-message = fa-icon.with("\u{e687}") +#let fa-lightbulb-on = fa-icon.with("\u{f672}") +#let fa-lightbulb-slash = fa-icon.with("\u{f673}") +#let fa-light-ceiling = fa-icon.with("\u{e016}") +#let fa-light-emergency = fa-icon.with("\u{e41f}") +#let fa-light-emergency-on = fa-icon.with("\u{e420}") +#let fa-lighthouse = fa-icon.with("\u{e612}") +#let fa-lights-holiday = fa-icon.with("\u{f7b2}") +#let fa-light-switch = fa-icon.with("\u{e017}") +#let fa-light-switch-off = fa-icon.with("\u{e018}") +#let fa-light-switch-on = fa-icon.with("\u{e019}") +#let fa-line = fa-icon.with("\u{f3c0}") +#let fa-line-columns = fa-icon.with("\u{f870}") +#let fa-line-height = fa-icon.with("\u{f871}") +#let fa-lines-leaning = fa-icon.with("\u{e51e}") +#let fa-link = fa-icon.with("\u{f0c1}") +#let fa-chain = fa-icon.with("\u{f0c1}") +#let fa-linkedin = fa-icon.with("\u{f08c}") +#let fa-linkedin-in = fa-icon.with("\u{f0e1}") +#let fa-link-horizontal = fa-icon.with("\u{e1cb}") +#let fa-chain-horizontal = fa-icon.with("\u{e1cb}") +#let fa-link-horizontal-slash = fa-icon.with("\u{e1cc}") +#let fa-chain-horizontal-slash = fa-icon.with("\u{e1cc}") +#let fa-link-simple = fa-icon.with("\u{e1cd}") +#let fa-link-simple-slash = fa-icon.with("\u{e1ce}") +#let fa-link-slash = fa-icon.with("\u{f127}") +#let fa-chain-broken = fa-icon.with("\u{f127}") +#let fa-chain-slash = fa-icon.with("\u{f127}") +#let fa-unlink = fa-icon.with("\u{f127}") +#let fa-linode = fa-icon.with("\u{f2b8}") +#let fa-linux = fa-icon.with("\u{f17c}") +#let fa-lips = fa-icon.with("\u{f600}") +#let fa-lira-sign = fa-icon.with("\u{f195}") +#let fa-list = fa-icon.with("\u{f03a}") +#let fa-list-squares = fa-icon.with("\u{f03a}") +#let fa-list-check = fa-icon.with("\u{f0ae}") +#let fa-tasks = fa-icon.with("\u{f0ae}") +#let fa-list-dropdown = fa-icon.with("\u{e1cf}") +#let fa-list-music = fa-icon.with("\u{f8c9}") +#let fa-list-ol = fa-icon.with("\u{f0cb}") +#let fa-list-1-2 = fa-icon.with("\u{f0cb}") +#let fa-list-numeric = fa-icon.with("\u{f0cb}") +#let fa-list-radio = fa-icon.with("\u{e1d0}") +#let fa-list-timeline = fa-icon.with("\u{e1d1}") +#let fa-list-tree = fa-icon.with("\u{e1d2}") +#let fa-list-ul = fa-icon.with("\u{f0ca}") +#let fa-list-dots = fa-icon.with("\u{f0ca}") +#let fa-litecoin-sign = fa-icon.with("\u{e1d3}") +#let fa-loader = fa-icon.with("\u{e1d4}") +#let fa-lobster = fa-icon.with("\u{e421}") +#let fa-location-arrow = fa-icon.with("\u{f124}") +#let fa-location-arrow-up = fa-icon.with("\u{e63a}") +#let fa-location-check = fa-icon.with("\u{f606}") +#let fa-map-marker-check = fa-icon.with("\u{f606}") +#let fa-location-crosshairs = fa-icon.with("\u{f601}") +#let fa-location = fa-icon.with("\u{f601}") +#let fa-location-crosshairs-slash = fa-icon.with("\u{f603}") +#let fa-location-slash = fa-icon.with("\u{f603}") +#let fa-location-dot = fa-icon.with("\u{f3c5}") +#let fa-map-marker-alt = fa-icon.with("\u{f3c5}") +#let fa-location-dot-slash = fa-icon.with("\u{f605}") +#let fa-map-marker-alt-slash = fa-icon.with("\u{f605}") +#let fa-location-exclamation = fa-icon.with("\u{f608}") +#let fa-map-marker-exclamation = fa-icon.with("\u{f608}") +#let fa-location-minus = fa-icon.with("\u{f609}") +#let fa-map-marker-minus = fa-icon.with("\u{f609}") +#let fa-location-pen = fa-icon.with("\u{f607}") +#let fa-map-marker-edit = fa-icon.with("\u{f607}") +#let fa-location-pin = fa-icon.with("\u{f041}") +#let fa-map-marker = fa-icon.with("\u{f041}") +#let fa-location-pin-lock = fa-icon.with("\u{e51f}") +#let fa-location-pin-slash = fa-icon.with("\u{f60c}") +#let fa-map-marker-slash = fa-icon.with("\u{f60c}") +#let fa-location-plus = fa-icon.with("\u{f60a}") +#let fa-map-marker-plus = fa-icon.with("\u{f60a}") +#let fa-location-question = fa-icon.with("\u{f60b}") +#let fa-map-marker-question = fa-icon.with("\u{f60b}") +#let fa-location-smile = fa-icon.with("\u{f60d}") +#let fa-map-marker-smile = fa-icon.with("\u{f60d}") +#let fa-location-xmark = fa-icon.with("\u{f60e}") +#let fa-map-marker-times = fa-icon.with("\u{f60e}") +#let fa-map-marker-xmark = fa-icon.with("\u{f60e}") +#let fa-lock = fa-icon.with("\u{f023}") +#let fa-lock-a = fa-icon.with("\u{e422}") +#let fa-lock-hashtag = fa-icon.with("\u{e423}") +#let fa-lock-keyhole = fa-icon.with("\u{f30d}") +#let fa-lock-alt = fa-icon.with("\u{f30d}") +#let fa-lock-keyhole-open = fa-icon.with("\u{f3c2}") +#let fa-lock-open-alt = fa-icon.with("\u{f3c2}") +#let fa-lock-open = fa-icon.with("\u{f3c1}") +#let fa-locust = fa-icon.with("\u{e520}") +#let fa-lollipop = fa-icon.with("\u{e424}") +#let fa-lollypop = fa-icon.with("\u{e424}") +#let fa-loveseat = fa-icon.with("\u{f4cc}") +#let fa-couch-small = fa-icon.with("\u{f4cc}") +#let fa-luchador-mask = fa-icon.with("\u{f455}") +#let fa-luchador = fa-icon.with("\u{f455}") +#let fa-mask-luchador = fa-icon.with("\u{f455}") +#let fa-lungs = fa-icon.with("\u{f604}") +#let fa-lungs-virus = fa-icon.with("\u{e067}") +#let fa-lyft = fa-icon.with("\u{f3c3}") +#let fa-m = fa-icon.with("\u{4d}") +#let fa-mace = fa-icon.with("\u{f6f8}") +#let fa-magento = fa-icon.with("\u{f3c4}") +#let fa-magnet = fa-icon.with("\u{f076}") +#let fa-magnifying-glass = fa-icon.with("\u{f002}") +#let fa-search = fa-icon.with("\u{f002}") +#let fa-magnifying-glass-arrow-right = fa-icon.with("\u{e521}") +#let fa-magnifying-glass-arrows-rotate = fa-icon.with("\u{e65e}") +#let fa-magnifying-glass-chart = fa-icon.with("\u{e522}") +#let fa-magnifying-glass-dollar = fa-icon.with("\u{f688}") +#let fa-search-dollar = fa-icon.with("\u{f688}") +#let fa-magnifying-glass-location = fa-icon.with("\u{f689}") +#let fa-search-location = fa-icon.with("\u{f689}") +#let fa-magnifying-glass-minus = fa-icon.with("\u{f010}") +#let fa-search-minus = fa-icon.with("\u{f010}") +#let fa-magnifying-glass-music = fa-icon.with("\u{e65f}") +#let fa-magnifying-glass-play = fa-icon.with("\u{e660}") +#let fa-magnifying-glass-plus = fa-icon.with("\u{f00e}") +#let fa-search-plus = fa-icon.with("\u{f00e}") +#let fa-magnifying-glass-waveform = fa-icon.with("\u{e661}") +#let fa-mailbox = fa-icon.with("\u{f813}") +#let fa-mailbox-flag-up = fa-icon.with("\u{e5bb}") +#let fa-mailchimp = fa-icon.with("\u{f59e}") +#let fa-manat-sign = fa-icon.with("\u{e1d5}") +#let fa-mandalorian = fa-icon.with("\u{f50f}") +#let fa-mandolin = fa-icon.with("\u{f6f9}") +#let fa-mango = fa-icon.with("\u{e30f}") +#let fa-manhole = fa-icon.with("\u{e1d6}") +#let fa-map = fa-icon.with("\u{f279}") +#let fa-map-location = fa-icon.with("\u{f59f}") +#let fa-map-marked = fa-icon.with("\u{f59f}") +#let fa-map-location-dot = fa-icon.with("\u{f5a0}") +#let fa-map-marked-alt = fa-icon.with("\u{f5a0}") +#let fa-map-pin = fa-icon.with("\u{f276}") +#let fa-markdown = fa-icon.with("\u{f60f}") +#let fa-marker = fa-icon.with("\u{f5a1}") +#let fa-mars = fa-icon.with("\u{f222}") +#let fa-mars-and-venus = fa-icon.with("\u{f224}") +#let fa-mars-and-venus-burst = fa-icon.with("\u{e523}") +#let fa-mars-double = fa-icon.with("\u{f227}") +#let fa-mars-stroke = fa-icon.with("\u{f229}") +#let fa-mars-stroke-right = fa-icon.with("\u{f22b}") +#let fa-mars-stroke-h = fa-icon.with("\u{f22b}") +#let fa-mars-stroke-up = fa-icon.with("\u{f22a}") +#let fa-mars-stroke-v = fa-icon.with("\u{f22a}") +#let fa-martini-glass = fa-icon.with("\u{f57b}") +#let fa-glass-martini-alt = fa-icon.with("\u{f57b}") +#let fa-martini-glass-citrus = fa-icon.with("\u{f561}") +#let fa-cocktail = fa-icon.with("\u{f561}") +#let fa-martini-glass-empty = fa-icon.with("\u{f000}") +#let fa-glass-martini = fa-icon.with("\u{f000}") +#let fa-mask = fa-icon.with("\u{f6fa}") +#let fa-mask-face = fa-icon.with("\u{e1d7}") +#let fa-mask-snorkel = fa-icon.with("\u{e3b7}") +#let fa-masks-theater = fa-icon.with("\u{f630}") +#let fa-theater-masks = fa-icon.with("\u{f630}") +#let fa-mask-ventilator = fa-icon.with("\u{e524}") +#let fa-mastodon = fa-icon.with("\u{f4f6}") +#let fa-mattress-pillow = fa-icon.with("\u{e525}") +#let fa-maxcdn = fa-icon.with("\u{f136}") +#let fa-maximize = fa-icon.with("\u{f31e}") +#let fa-expand-arrows-alt = fa-icon.with("\u{f31e}") +#let fa-mdb = fa-icon.with("\u{f8ca}") +#let fa-meat = fa-icon.with("\u{f814}") +#let fa-medal = fa-icon.with("\u{f5a2}") +#let fa-medapps = fa-icon.with("\u{f3c6}") +#let fa-medium = fa-icon.with("\u{f23a}") +#let fa-medium-m = fa-icon.with("\u{f23a}") +#let fa-medrt = fa-icon.with("\u{f3c8}") +#let fa-meetup = fa-icon.with("\u{f2e0}") +#let fa-megaphone = fa-icon.with("\u{f675}") +#let fa-megaport = fa-icon.with("\u{f5a3}") +#let fa-melon = fa-icon.with("\u{e310}") +#let fa-melon-slice = fa-icon.with("\u{e311}") +#let fa-memo = fa-icon.with("\u{e1d8}") +#let fa-memo-circle-check = fa-icon.with("\u{e1d9}") +#let fa-memo-circle-info = fa-icon.with("\u{e49a}") +#let fa-memo-pad = fa-icon.with("\u{e1da}") +#let fa-memory = fa-icon.with("\u{f538}") +#let fa-mendeley = fa-icon.with("\u{f7b3}") +#let fa-menorah = fa-icon.with("\u{f676}") +#let fa-mercury = fa-icon.with("\u{f223}") +#let fa-merge = fa-icon.with("\u{e526}") +#let fa-message = fa-icon.with("\u{f27a}") +#let fa-comment-alt = fa-icon.with("\u{f27a}") +#let fa-message-arrow-down = fa-icon.with("\u{e1db}") +#let fa-comment-alt-arrow-down = fa-icon.with("\u{e1db}") +#let fa-message-arrow-up = fa-icon.with("\u{e1dc}") +#let fa-comment-alt-arrow-up = fa-icon.with("\u{e1dc}") +#let fa-message-arrow-up-right = fa-icon.with("\u{e1dd}") +#let fa-message-bot = fa-icon.with("\u{e3b8}") +#let fa-message-captions = fa-icon.with("\u{e1de}") +#let fa-comment-alt-captions = fa-icon.with("\u{e1de}") +#let fa-message-check = fa-icon.with("\u{f4a2}") +#let fa-comment-alt-check = fa-icon.with("\u{f4a2}") +#let fa-message-code = fa-icon.with("\u{e1df}") +#let fa-message-dollar = fa-icon.with("\u{f650}") +#let fa-comment-alt-dollar = fa-icon.with("\u{f650}") +#let fa-message-dots = fa-icon.with("\u{f4a3}") +#let fa-comment-alt-dots = fa-icon.with("\u{f4a3}") +#let fa-messaging = fa-icon.with("\u{f4a3}") +#let fa-message-exclamation = fa-icon.with("\u{f4a5}") +#let fa-comment-alt-exclamation = fa-icon.with("\u{f4a5}") +#let fa-message-heart = fa-icon.with("\u{e5c9}") +#let fa-message-image = fa-icon.with("\u{e1e0}") +#let fa-comment-alt-image = fa-icon.with("\u{e1e0}") +#let fa-message-lines = fa-icon.with("\u{f4a6}") +#let fa-comment-alt-lines = fa-icon.with("\u{f4a6}") +#let fa-message-medical = fa-icon.with("\u{f7f4}") +#let fa-comment-alt-medical = fa-icon.with("\u{f7f4}") +#let fa-message-middle = fa-icon.with("\u{e1e1}") +#let fa-comment-middle-alt = fa-icon.with("\u{e1e1}") +#let fa-message-middle-top = fa-icon.with("\u{e1e2}") +#let fa-comment-middle-top-alt = fa-icon.with("\u{e1e2}") +#let fa-message-minus = fa-icon.with("\u{f4a7}") +#let fa-comment-alt-minus = fa-icon.with("\u{f4a7}") +#let fa-message-music = fa-icon.with("\u{f8af}") +#let fa-comment-alt-music = fa-icon.with("\u{f8af}") +#let fa-message-pen = fa-icon.with("\u{f4a4}") +#let fa-comment-alt-edit = fa-icon.with("\u{f4a4}") +#let fa-message-edit = fa-icon.with("\u{f4a4}") +#let fa-message-plus = fa-icon.with("\u{f4a8}") +#let fa-comment-alt-plus = fa-icon.with("\u{f4a8}") +#let fa-message-question = fa-icon.with("\u{e1e3}") +#let fa-message-quote = fa-icon.with("\u{e1e4}") +#let fa-comment-alt-quote = fa-icon.with("\u{e1e4}") +#let fa-messages = fa-icon.with("\u{f4b6}") +#let fa-comments-alt = fa-icon.with("\u{f4b6}") +#let fa-messages-dollar = fa-icon.with("\u{f652}") +#let fa-comments-alt-dollar = fa-icon.with("\u{f652}") +#let fa-message-slash = fa-icon.with("\u{f4a9}") +#let fa-comment-alt-slash = fa-icon.with("\u{f4a9}") +#let fa-message-smile = fa-icon.with("\u{f4aa}") +#let fa-comment-alt-smile = fa-icon.with("\u{f4aa}") +#let fa-message-sms = fa-icon.with("\u{e1e5}") +#let fa-messages-question = fa-icon.with("\u{e1e7}") +#let fa-message-text = fa-icon.with("\u{e1e6}") +#let fa-comment-alt-text = fa-icon.with("\u{e1e6}") +#let fa-message-xmark = fa-icon.with("\u{f4ab}") +#let fa-comment-alt-times = fa-icon.with("\u{f4ab}") +#let fa-message-times = fa-icon.with("\u{f4ab}") +#let fa-meta = fa-icon.with("\u{e49b}") +#let fa-meteor = fa-icon.with("\u{f753}") +#let fa-meter = fa-icon.with("\u{e1e8}") +#let fa-meter-bolt = fa-icon.with("\u{e1e9}") +#let fa-meter-droplet = fa-icon.with("\u{e1ea}") +#let fa-meter-fire = fa-icon.with("\u{e1eb}") +#let fa-microblog = fa-icon.with("\u{e01a}") +#let fa-microchip = fa-icon.with("\u{f2db}") +#let fa-microchip-ai = fa-icon.with("\u{e1ec}") +#let fa-microphone = fa-icon.with("\u{f130}") +#let fa-microphone-lines = fa-icon.with("\u{f3c9}") +#let fa-microphone-alt = fa-icon.with("\u{f3c9}") +#let fa-microphone-lines-slash = fa-icon.with("\u{f539}") +#let fa-microphone-alt-slash = fa-icon.with("\u{f539}") +#let fa-microphone-slash = fa-icon.with("\u{f131}") +#let fa-microphone-stand = fa-icon.with("\u{f8cb}") +#let fa-microscope = fa-icon.with("\u{f610}") +#let fa-microsoft = fa-icon.with("\u{f3ca}") +#let fa-microwave = fa-icon.with("\u{e01b}") +#let fa-mill-sign = fa-icon.with("\u{e1ed}") +#let fa-minimize = fa-icon.with("\u{f78c}") +#let fa-compress-arrows-alt = fa-icon.with("\u{f78c}") +#let fa-mintbit = fa-icon.with("\u{e62f}") +#let fa-minus = fa-icon.with("\u{f068}") +#let fa-subtract = fa-icon.with("\u{f068}") +#let fa-mistletoe = fa-icon.with("\u{f7b4}") +#let fa-mitten = fa-icon.with("\u{f7b5}") +#let fa-mix = fa-icon.with("\u{f3cb}") +#let fa-mixcloud = fa-icon.with("\u{f289}") +#let fa-mixer = fa-icon.with("\u{e056}") +#let fa-mizuni = fa-icon.with("\u{f3cc}") +#let fa-mobile = fa-icon.with("\u{f3ce}") +#let fa-mobile-android = fa-icon.with("\u{f3ce}") +#let fa-mobile-phone = fa-icon.with("\u{f3ce}") +#let fa-mobile-button = fa-icon.with("\u{f10b}") +#let fa-mobile-notch = fa-icon.with("\u{e1ee}") +#let fa-mobile-iphone = fa-icon.with("\u{e1ee}") +#let fa-mobile-retro = fa-icon.with("\u{e527}") +#let fa-mobile-screen = fa-icon.with("\u{f3cf}") +#let fa-mobile-android-alt = fa-icon.with("\u{f3cf}") +#let fa-mobile-screen-button = fa-icon.with("\u{f3cd}") +#let fa-mobile-alt = fa-icon.with("\u{f3cd}") +#let fa-mobile-signal = fa-icon.with("\u{e1ef}") +#let fa-mobile-signal-out = fa-icon.with("\u{e1f0}") +#let fa-modx = fa-icon.with("\u{f285}") +#let fa-monero = fa-icon.with("\u{f3d0}") +#let fa-money-bill = fa-icon.with("\u{f0d6}") +#let fa-money-bill-1 = fa-icon.with("\u{f3d1}") +#let fa-money-bill-alt = fa-icon.with("\u{f3d1}") +#let fa-money-bill-1-wave = fa-icon.with("\u{f53b}") +#let fa-money-bill-wave-alt = fa-icon.with("\u{f53b}") +#let fa-money-bills = fa-icon.with("\u{e1f3}") +#let fa-money-bill-simple = fa-icon.with("\u{e1f1}") +#let fa-money-bill-simple-wave = fa-icon.with("\u{e1f2}") +#let fa-money-bills-simple = fa-icon.with("\u{e1f4}") +#let fa-money-bills-alt = fa-icon.with("\u{e1f4}") +#let fa-money-bill-transfer = fa-icon.with("\u{e528}") +#let fa-money-bill-trend-up = fa-icon.with("\u{e529}") +#let fa-money-bill-wave = fa-icon.with("\u{f53a}") +#let fa-money-bill-wheat = fa-icon.with("\u{e52a}") +#let fa-money-check = fa-icon.with("\u{f53c}") +#let fa-money-check-dollar = fa-icon.with("\u{f53d}") +#let fa-money-check-alt = fa-icon.with("\u{f53d}") +#let fa-money-check-dollar-pen = fa-icon.with("\u{f873}") +#let fa-money-check-edit-alt = fa-icon.with("\u{f873}") +#let fa-money-check-pen = fa-icon.with("\u{f872}") +#let fa-money-check-edit = fa-icon.with("\u{f872}") +#let fa-money-from-bracket = fa-icon.with("\u{e312}") +#let fa-money-simple-from-bracket = fa-icon.with("\u{e313}") +#let fa-monitor-waveform = fa-icon.with("\u{f611}") +#let fa-monitor-heart-rate = fa-icon.with("\u{f611}") +#let fa-monkey = fa-icon.with("\u{f6fb}") +#let fa-monument = fa-icon.with("\u{f5a6}") +#let fa-moon = fa-icon.with("\u{f186}") +#let fa-moon-cloud = fa-icon.with("\u{f754}") +#let fa-moon-over-sun = fa-icon.with("\u{f74a}") +#let fa-eclipse-alt = fa-icon.with("\u{f74a}") +#let fa-moon-stars = fa-icon.with("\u{f755}") +#let fa-moped = fa-icon.with("\u{e3b9}") +#let fa-mortar-pestle = fa-icon.with("\u{f5a7}") +#let fa-mosque = fa-icon.with("\u{f678}") +#let fa-mosquito = fa-icon.with("\u{e52b}") +#let fa-mosquito-net = fa-icon.with("\u{e52c}") +#let fa-motorcycle = fa-icon.with("\u{f21c}") +#let fa-mound = fa-icon.with("\u{e52d}") +#let fa-mountain = fa-icon.with("\u{f6fc}") +#let fa-mountain-city = fa-icon.with("\u{e52e}") +#let fa-mountains = fa-icon.with("\u{f6fd}") +#let fa-mountain-sun = fa-icon.with("\u{e52f}") +#let fa-mouse-field = fa-icon.with("\u{e5a8}") +#let fa-mp3-player = fa-icon.with("\u{f8ce}") +#let fa-mug = fa-icon.with("\u{f874}") +#let fa-mug-hot = fa-icon.with("\u{f7b6}") +#let fa-mug-marshmallows = fa-icon.with("\u{f7b7}") +#let fa-mug-saucer = fa-icon.with("\u{f0f4}") +#let fa-coffee = fa-icon.with("\u{f0f4}") +#let fa-mug-tea = fa-icon.with("\u{f875}") +#let fa-mug-tea-saucer = fa-icon.with("\u{e1f5}") +#let fa-mushroom = fa-icon.with("\u{e425}") +#let fa-music = fa-icon.with("\u{f001}") +#let fa-music-magnifying-glass = fa-icon.with("\u{e662}") +#let fa-music-note = fa-icon.with("\u{f8cf}") +#let fa-music-alt = fa-icon.with("\u{f8cf}") +#let fa-music-note-slash = fa-icon.with("\u{f8d0}") +#let fa-music-alt-slash = fa-icon.with("\u{f8d0}") +#let fa-music-slash = fa-icon.with("\u{f8d1}") +#let fa-mustache = fa-icon.with("\u{e5bc}") +#let fa-n = fa-icon.with("\u{4e}") +#let fa-naira-sign = fa-icon.with("\u{e1f6}") +#let fa-napster = fa-icon.with("\u{f3d2}") +#let fa-narwhal = fa-icon.with("\u{f6fe}") +#let fa-neos = fa-icon.with("\u{f612}") +#let fa-nesting-dolls = fa-icon.with("\u{e3ba}") +#let fa-network-wired = fa-icon.with("\u{f6ff}") +#let fa-neuter = fa-icon.with("\u{f22c}") +#let fa-newspaper = fa-icon.with("\u{f1ea}") +#let fa-nfc = fa-icon.with("\u{e1f7}") +#let fa-nfc-directional = fa-icon.with("\u{e530}") +#let fa-nfc-lock = fa-icon.with("\u{e1f8}") +#let fa-nfc-magnifying-glass = fa-icon.with("\u{e1f9}") +#let fa-nfc-pen = fa-icon.with("\u{e1fa}") +#let fa-nfc-signal = fa-icon.with("\u{e1fb}") +#let fa-nfc-slash = fa-icon.with("\u{e1fc}") +#let fa-nfc-symbol = fa-icon.with("\u{e531}") +#let fa-nfc-trash = fa-icon.with("\u{e1fd}") +#let fa-nimblr = fa-icon.with("\u{f5a8}") +#let fa-node = fa-icon.with("\u{f419}") +#let fa-node-js = fa-icon.with("\u{f3d3}") +#let fa-nose = fa-icon.with("\u{e5bd}") +#let fa-notdef = fa-icon.with("\u{e1fe}") +#let fa-note = fa-icon.with("\u{e1ff}") +#let fa-notebook = fa-icon.with("\u{e201}") +#let fa-note-medical = fa-icon.with("\u{e200}") +#let fa-not-equal = fa-icon.with("\u{f53e}") +#let fa-notes = fa-icon.with("\u{e202}") +#let fa-notes-medical = fa-icon.with("\u{f481}") +#let fa-note-sticky = fa-icon.with("\u{f249}") +#let fa-sticky-note = fa-icon.with("\u{f249}") +#let fa-npm = fa-icon.with("\u{f3d4}") +#let fa-ns8 = fa-icon.with("\u{f3d5}") +#let fa-nutritionix = fa-icon.with("\u{f3d6}") +#let fa-o = fa-icon.with("\u{4f}") +#let fa-object-exclude = fa-icon.with("\u{e49c}") +#let fa-object-group = fa-icon.with("\u{f247}") +#let fa-object-intersect = fa-icon.with("\u{e49d}") +#let fa-objects-align-bottom = fa-icon.with("\u{e3bb}") +#let fa-objects-align-center-horizontal = fa-icon.with("\u{e3bc}") +#let fa-objects-align-center-vertical = fa-icon.with("\u{e3bd}") +#let fa-objects-align-left = fa-icon.with("\u{e3be}") +#let fa-objects-align-right = fa-icon.with("\u{e3bf}") +#let fa-objects-align-top = fa-icon.with("\u{e3c0}") +#let fa-objects-column = fa-icon.with("\u{e3c1}") +#let fa-object-subtract = fa-icon.with("\u{e49e}") +#let fa-object-ungroup = fa-icon.with("\u{f248}") +#let fa-object-union = fa-icon.with("\u{e49f}") +#let fa-octagon = fa-icon.with("\u{f306}") +#let fa-octagon-check = fa-icon.with("\u{e426}") +#let fa-octagon-divide = fa-icon.with("\u{e203}") +#let fa-octagon-exclamation = fa-icon.with("\u{e204}") +#let fa-octagon-minus = fa-icon.with("\u{f308}") +#let fa-minus-octagon = fa-icon.with("\u{f308}") +#let fa-octagon-plus = fa-icon.with("\u{f301}") +#let fa-plus-octagon = fa-icon.with("\u{f301}") +#let fa-octagon-xmark = fa-icon.with("\u{f2f0}") +#let fa-times-octagon = fa-icon.with("\u{f2f0}") +#let fa-xmark-octagon = fa-icon.with("\u{f2f0}") +#let fa-octopus = fa-icon.with("\u{e688}") +#let fa-octopus-deploy = fa-icon.with("\u{e082}") +#let fa-odnoklassniki = fa-icon.with("\u{f263}") +#let fa-odysee = fa-icon.with("\u{e5c6}") +#let fa-oil-can = fa-icon.with("\u{f613}") +#let fa-oil-can-drip = fa-icon.with("\u{e205}") +#let fa-oil-temperature = fa-icon.with("\u{f614}") +#let fa-oil-temp = fa-icon.with("\u{f614}") +#let fa-oil-well = fa-icon.with("\u{e532}") +#let fa-old-republic = fa-icon.with("\u{f510}") +#let fa-olive = fa-icon.with("\u{e316}") +#let fa-olive-branch = fa-icon.with("\u{e317}") +#let fa-om = fa-icon.with("\u{f679}") +#let fa-omega = fa-icon.with("\u{f67a}") +#let fa-onion = fa-icon.with("\u{e427}") +#let fa-opencart = fa-icon.with("\u{f23d}") +#let fa-openid = fa-icon.with("\u{f19b}") +#let fa-opensuse = fa-icon.with("\u{e62b}") +#let fa-opera = fa-icon.with("\u{f26a}") +#let fa-optin-monster = fa-icon.with("\u{f23c}") +#let fa-option = fa-icon.with("\u{e318}") +#let fa-orcid = fa-icon.with("\u{f8d2}") +#let fa-ornament = fa-icon.with("\u{f7b8}") +#let fa-osi = fa-icon.with("\u{f41a}") +#let fa-otter = fa-icon.with("\u{f700}") +#let fa-outdent = fa-icon.with("\u{f03b}") +#let fa-dedent = fa-icon.with("\u{f03b}") +#let fa-outlet = fa-icon.with("\u{e01c}") +#let fa-oven = fa-icon.with("\u{e01d}") +#let fa-overline = fa-icon.with("\u{f876}") +#let fa-p = fa-icon.with("\u{50}") +#let fa-padlet = fa-icon.with("\u{e4a0}") +#let fa-page = fa-icon.with("\u{e428}") +#let fa-page4 = fa-icon.with("\u{f3d7}") +#let fa-page-caret-down = fa-icon.with("\u{e429}") +#let fa-file-caret-down = fa-icon.with("\u{e429}") +#let fa-page-caret-up = fa-icon.with("\u{e42a}") +#let fa-file-caret-up = fa-icon.with("\u{e42a}") +#let fa-pagelines = fa-icon.with("\u{f18c}") +#let fa-pager = fa-icon.with("\u{f815}") +#let fa-paintbrush = fa-icon.with("\u{f1fc}") +#let fa-paint-brush = fa-icon.with("\u{f1fc}") +#let fa-paintbrush-fine = fa-icon.with("\u{f5a9}") +#let fa-paint-brush-alt = fa-icon.with("\u{f5a9}") +#let fa-paint-brush-fine = fa-icon.with("\u{f5a9}") +#let fa-paintbrush-alt = fa-icon.with("\u{f5a9}") +#let fa-paintbrush-pencil = fa-icon.with("\u{e206}") +#let fa-paint-roller = fa-icon.with("\u{f5aa}") +#let fa-palette = fa-icon.with("\u{f53f}") +#let fa-palfed = fa-icon.with("\u{f3d8}") +#let fa-pallet = fa-icon.with("\u{f482}") +#let fa-pallet-box = fa-icon.with("\u{e208}") +#let fa-pallet-boxes = fa-icon.with("\u{f483}") +#let fa-palette-boxes = fa-icon.with("\u{f483}") +#let fa-pallet-alt = fa-icon.with("\u{f483}") +#let fa-pancakes = fa-icon.with("\u{e42d}") +#let fa-panel-ews = fa-icon.with("\u{e42e}") +#let fa-panel-fire = fa-icon.with("\u{e42f}") +#let fa-pan-food = fa-icon.with("\u{e42b}") +#let fa-pan-frying = fa-icon.with("\u{e42c}") +#let fa-panorama = fa-icon.with("\u{e209}") +#let fa-paperclip = fa-icon.with("\u{f0c6}") +#let fa-paperclip-vertical = fa-icon.with("\u{e3c2}") +#let fa-paper-plane = fa-icon.with("\u{f1d8}") +#let fa-paper-plane-top = fa-icon.with("\u{e20a}") +#let fa-paper-plane-alt = fa-icon.with("\u{e20a}") +#let fa-send = fa-icon.with("\u{e20a}") +#let fa-parachute-box = fa-icon.with("\u{f4cd}") +#let fa-paragraph = fa-icon.with("\u{f1dd}") +#let fa-paragraph-left = fa-icon.with("\u{f878}") +#let fa-paragraph-rtl = fa-icon.with("\u{f878}") +#let fa-party-bell = fa-icon.with("\u{e31a}") +#let fa-party-horn = fa-icon.with("\u{e31b}") +#let fa-passport = fa-icon.with("\u{f5ab}") +#let fa-paste = fa-icon.with("\u{f0ea}") +#let fa-file-clipboard = fa-icon.with("\u{f0ea}") +#let fa-patreon = fa-icon.with("\u{f3d9}") +#let fa-pause = fa-icon.with("\u{f04c}") +#let fa-paw = fa-icon.with("\u{f1b0}") +#let fa-paw-claws = fa-icon.with("\u{f702}") +#let fa-paw-simple = fa-icon.with("\u{f701}") +#let fa-paw-alt = fa-icon.with("\u{f701}") +#let fa-paypal = fa-icon.with("\u{f1ed}") +#let fa-peace = fa-icon.with("\u{f67c}") +#let fa-peach = fa-icon.with("\u{e20b}") +#let fa-peanut = fa-icon.with("\u{e430}") +#let fa-peanuts = fa-icon.with("\u{e431}") +#let fa-peapod = fa-icon.with("\u{e31c}") +#let fa-pear = fa-icon.with("\u{e20c}") +#let fa-pedestal = fa-icon.with("\u{e20d}") +#let fa-pegasus = fa-icon.with("\u{f703}") +#let fa-pen = fa-icon.with("\u{f304}") +#let fa-pencil = fa-icon.with("\u{f303}") +#let fa-pencil-alt = fa-icon.with("\u{f303}") +#let fa-pencil-mechanical = fa-icon.with("\u{e5ca}") +#let fa-pencil-slash = fa-icon.with("\u{e215}") +#let fa-pen-circle = fa-icon.with("\u{e20e}") +#let fa-pen-clip = fa-icon.with("\u{f305}") +#let fa-pen-alt = fa-icon.with("\u{f305}") +#let fa-pen-clip-slash = fa-icon.with("\u{e20f}") +#let fa-pen-alt-slash = fa-icon.with("\u{e20f}") +#let fa-pen-fancy = fa-icon.with("\u{f5ac}") +#let fa-pen-fancy-slash = fa-icon.with("\u{e210}") +#let fa-pen-field = fa-icon.with("\u{e211}") +#let fa-pen-line = fa-icon.with("\u{e212}") +#let fa-pen-nib = fa-icon.with("\u{f5ad}") +#let fa-pen-nib-slash = fa-icon.with("\u{e4a1}") +#let fa-pen-paintbrush = fa-icon.with("\u{f618}") +#let fa-pencil-paintbrush = fa-icon.with("\u{f618}") +#let fa-pen-ruler = fa-icon.with("\u{f5ae}") +#let fa-pencil-ruler = fa-icon.with("\u{f5ae}") +#let fa-pen-slash = fa-icon.with("\u{e213}") +#let fa-pen-swirl = fa-icon.with("\u{e214}") +#let fa-pen-to-square = fa-icon.with("\u{f044}") +#let fa-edit = fa-icon.with("\u{f044}") +#let fa-people = fa-icon.with("\u{e216}") +#let fa-people-arrows = fa-icon.with("\u{e068}") +#let fa-people-arrows-left-right = fa-icon.with("\u{e068}") +#let fa-people-carry-box = fa-icon.with("\u{f4ce}") +#let fa-people-carry = fa-icon.with("\u{f4ce}") +#let fa-people-dress = fa-icon.with("\u{e217}") +#let fa-people-dress-simple = fa-icon.with("\u{e218}") +#let fa-people-group = fa-icon.with("\u{e533}") +#let fa-people-line = fa-icon.with("\u{e534}") +#let fa-people-pants = fa-icon.with("\u{e219}") +#let fa-people-pants-simple = fa-icon.with("\u{e21a}") +#let fa-people-pulling = fa-icon.with("\u{e535}") +#let fa-people-robbery = fa-icon.with("\u{e536}") +#let fa-people-roof = fa-icon.with("\u{e537}") +#let fa-people-simple = fa-icon.with("\u{e21b}") +#let fa-pepper = fa-icon.with("\u{e432}") +#let fa-pepper-hot = fa-icon.with("\u{f816}") +#let fa-perbyte = fa-icon.with("\u{e083}") +#let fa-percent = fa-icon.with("\u{25}") +#let fa-percentage = fa-icon.with("\u{25}") +#let fa-period = fa-icon.with("\u{2e}") +#let fa-periscope = fa-icon.with("\u{f3da}") +#let fa-person = fa-icon.with("\u{f183}") +#let fa-male = fa-icon.with("\u{f183}") +#let fa-person-arrow-down-to-line = fa-icon.with("\u{e538}") +#let fa-person-arrow-up-from-line = fa-icon.with("\u{e539}") +#let fa-person-biking = fa-icon.with("\u{f84a}") +#let fa-biking = fa-icon.with("\u{f84a}") +#let fa-person-biking-mountain = fa-icon.with("\u{f84b}") +#let fa-biking-mountain = fa-icon.with("\u{f84b}") +#let fa-person-booth = fa-icon.with("\u{f756}") +#let fa-person-breastfeeding = fa-icon.with("\u{e53a}") +#let fa-person-burst = fa-icon.with("\u{e53b}") +#let fa-person-cane = fa-icon.with("\u{e53c}") +#let fa-person-carry-box = fa-icon.with("\u{f4cf}") +#let fa-person-carry = fa-icon.with("\u{f4cf}") +#let fa-person-chalkboard = fa-icon.with("\u{e53d}") +#let fa-person-circle-check = fa-icon.with("\u{e53e}") +#let fa-person-circle-exclamation = fa-icon.with("\u{e53f}") +#let fa-person-circle-minus = fa-icon.with("\u{e540}") +#let fa-person-circle-plus = fa-icon.with("\u{e541}") +#let fa-person-circle-question = fa-icon.with("\u{e542}") +#let fa-person-circle-xmark = fa-icon.with("\u{e543}") +#let fa-person-digging = fa-icon.with("\u{f85e}") +#let fa-digging = fa-icon.with("\u{f85e}") +#let fa-person-dolly = fa-icon.with("\u{f4d0}") +#let fa-person-dolly-empty = fa-icon.with("\u{f4d1}") +#let fa-person-dots-from-line = fa-icon.with("\u{f470}") +#let fa-diagnoses = fa-icon.with("\u{f470}") +#let fa-person-dress = fa-icon.with("\u{f182}") +#let fa-female = fa-icon.with("\u{f182}") +#let fa-person-dress-burst = fa-icon.with("\u{e544}") +#let fa-person-dress-fairy = fa-icon.with("\u{e607}") +#let fa-person-dress-simple = fa-icon.with("\u{e21c}") +#let fa-person-drowning = fa-icon.with("\u{e545}") +#let fa-person-fairy = fa-icon.with("\u{e608}") +#let fa-person-falling = fa-icon.with("\u{e546}") +#let fa-person-falling-burst = fa-icon.with("\u{e547}") +#let fa-person-from-portal = fa-icon.with("\u{e023}") +#let fa-portal-exit = fa-icon.with("\u{e023}") +#let fa-person-half-dress = fa-icon.with("\u{e548}") +#let fa-person-harassing = fa-icon.with("\u{e549}") +#let fa-person-hiking = fa-icon.with("\u{f6ec}") +#let fa-hiking = fa-icon.with("\u{f6ec}") +#let fa-person-military-pointing = fa-icon.with("\u{e54a}") +#let fa-person-military-rifle = fa-icon.with("\u{e54b}") +#let fa-person-military-to-person = fa-icon.with("\u{e54c}") +#let fa-person-pinball = fa-icon.with("\u{e21d}") +#let fa-person-praying = fa-icon.with("\u{f683}") +#let fa-pray = fa-icon.with("\u{f683}") +#let fa-person-pregnant = fa-icon.with("\u{e31e}") +#let fa-person-rays = fa-icon.with("\u{e54d}") +#let fa-person-rifle = fa-icon.with("\u{e54e}") +#let fa-person-running = fa-icon.with("\u{f70c}") +#let fa-running = fa-icon.with("\u{f70c}") +#let fa-person-running-fast = fa-icon.with("\u{e5ff}") +#let fa-person-seat = fa-icon.with("\u{e21e}") +#let fa-person-seat-reclined = fa-icon.with("\u{e21f}") +#let fa-person-shelter = fa-icon.with("\u{e54f}") +#let fa-person-sign = fa-icon.with("\u{f757}") +#let fa-person-simple = fa-icon.with("\u{e220}") +#let fa-person-skating = fa-icon.with("\u{f7c5}") +#let fa-skating = fa-icon.with("\u{f7c5}") +#let fa-person-skiing = fa-icon.with("\u{f7c9}") +#let fa-skiing = fa-icon.with("\u{f7c9}") +#let fa-person-skiing-nordic = fa-icon.with("\u{f7ca}") +#let fa-skiing-nordic = fa-icon.with("\u{f7ca}") +#let fa-person-ski-jumping = fa-icon.with("\u{f7c7}") +#let fa-ski-jump = fa-icon.with("\u{f7c7}") +#let fa-person-ski-lift = fa-icon.with("\u{f7c8}") +#let fa-ski-lift = fa-icon.with("\u{f7c8}") +#let fa-person-sledding = fa-icon.with("\u{f7cb}") +#let fa-sledding = fa-icon.with("\u{f7cb}") +#let fa-person-snowboarding = fa-icon.with("\u{f7ce}") +#let fa-snowboarding = fa-icon.with("\u{f7ce}") +#let fa-person-snowmobiling = fa-icon.with("\u{f7d1}") +#let fa-snowmobile = fa-icon.with("\u{f7d1}") +#let fa-person-swimming = fa-icon.with("\u{f5c4}") +#let fa-swimmer = fa-icon.with("\u{f5c4}") +#let fa-person-through-window = fa-icon.with("\u{e5a9}") +#let fa-person-to-door = fa-icon.with("\u{e433}") +#let fa-person-to-portal = fa-icon.with("\u{e022}") +#let fa-portal-enter = fa-icon.with("\u{e022}") +#let fa-person-walking = fa-icon.with("\u{f554}") +#let fa-walking = fa-icon.with("\u{f554}") +#let fa-person-walking-arrow-loop-left = fa-icon.with("\u{e551}") +#let fa-person-walking-arrow-right = fa-icon.with("\u{e552}") +#let fa-person-walking-dashed-line-arrow-right = fa-icon.with("\u{e553}") +#let fa-person-walking-luggage = fa-icon.with("\u{e554}") +#let fa-person-walking-with-cane = fa-icon.with("\u{f29d}") +#let fa-blind = fa-icon.with("\u{f29d}") +#let fa-peseta-sign = fa-icon.with("\u{e221}") +#let fa-peso-sign = fa-icon.with("\u{e222}") +#let fa-phabricator = fa-icon.with("\u{f3db}") +#let fa-phoenix-framework = fa-icon.with("\u{f3dc}") +#let fa-phoenix-squadron = fa-icon.with("\u{f511}") +#let fa-phone = fa-icon.with("\u{f095}") +#let fa-phone-arrow-down-left = fa-icon.with("\u{e223}") +#let fa-phone-arrow-down = fa-icon.with("\u{e223}") +#let fa-phone-incoming = fa-icon.with("\u{e223}") +#let fa-phone-arrow-right = fa-icon.with("\u{e5be}") +#let fa-phone-arrow-up-right = fa-icon.with("\u{e224}") +#let fa-phone-arrow-up = fa-icon.with("\u{e224}") +#let fa-phone-outgoing = fa-icon.with("\u{e224}") +#let fa-phone-flip = fa-icon.with("\u{f879}") +#let fa-phone-alt = fa-icon.with("\u{f879}") +#let fa-phone-hangup = fa-icon.with("\u{e225}") +#let fa-phone-intercom = fa-icon.with("\u{e434}") +#let fa-phone-missed = fa-icon.with("\u{e226}") +#let fa-phone-office = fa-icon.with("\u{f67d}") +#let fa-phone-plus = fa-icon.with("\u{f4d2}") +#let fa-phone-rotary = fa-icon.with("\u{f8d3}") +#let fa-phone-slash = fa-icon.with("\u{f3dd}") +#let fa-phone-volume = fa-icon.with("\u{f2a0}") +#let fa-volume-control-phone = fa-icon.with("\u{f2a0}") +#let fa-phone-xmark = fa-icon.with("\u{e227}") +#let fa-photo-film = fa-icon.with("\u{f87c}") +#let fa-photo-video = fa-icon.with("\u{f87c}") +#let fa-photo-film-music = fa-icon.with("\u{e228}") +#let fa-php = fa-icon.with("\u{f457}") +#let fa-pi = fa-icon.with("\u{f67e}") +#let fa-piano = fa-icon.with("\u{f8d4}") +#let fa-piano-keyboard = fa-icon.with("\u{f8d5}") +#let fa-pickaxe = fa-icon.with("\u{e5bf}") +#let fa-pickleball = fa-icon.with("\u{e435}") +#let fa-pie = fa-icon.with("\u{f705}") +#let fa-pied-piper = fa-icon.with("\u{f2ae}") +#let fa-pied-piper-alt = fa-icon.with("\u{f1a8}") +#let fa-pied-piper-hat = fa-icon.with("\u{f4e5}") +#let fa-pied-piper-pp = fa-icon.with("\u{f1a7}") +#let fa-pig = fa-icon.with("\u{f706}") +#let fa-piggy-bank = fa-icon.with("\u{f4d3}") +#let fa-pills = fa-icon.with("\u{f484}") +#let fa-pinata = fa-icon.with("\u{e3c3}") +#let fa-pinball = fa-icon.with("\u{e229}") +#let fa-pineapple = fa-icon.with("\u{e31f}") +#let fa-pinterest = fa-icon.with("\u{f0d2}") +#let fa-pinterest-p = fa-icon.with("\u{f231}") +#let fa-pipe = fa-icon.with("\u{7c}") +#let fa-pipe-circle-check = fa-icon.with("\u{e436}") +#let fa-pipe-collar = fa-icon.with("\u{e437}") +#let fa-pipe-section = fa-icon.with("\u{e438}") +#let fa-pipe-smoking = fa-icon.with("\u{e3c4}") +#let fa-pipe-valve = fa-icon.with("\u{e439}") +#let fa-pix = fa-icon.with("\u{e43a}") +#let fa-pixiv = fa-icon.with("\u{e640}") +#let fa-pizza = fa-icon.with("\u{f817}") +#let fa-pizza-slice = fa-icon.with("\u{f818}") +#let fa-place-of-worship = fa-icon.with("\u{f67f}") +#let fa-plane = fa-icon.with("\u{f072}") +#let fa-plane-arrival = fa-icon.with("\u{f5af}") +#let fa-plane-circle-check = fa-icon.with("\u{e555}") +#let fa-plane-circle-exclamation = fa-icon.with("\u{e556}") +#let fa-plane-circle-xmark = fa-icon.with("\u{e557}") +#let fa-plane-departure = fa-icon.with("\u{f5b0}") +#let fa-plane-engines = fa-icon.with("\u{f3de}") +#let fa-plane-alt = fa-icon.with("\u{f3de}") +#let fa-plane-lock = fa-icon.with("\u{e558}") +#let fa-plane-prop = fa-icon.with("\u{e22b}") +#let fa-plane-slash = fa-icon.with("\u{e069}") +#let fa-plane-tail = fa-icon.with("\u{e22c}") +#let fa-planet-moon = fa-icon.with("\u{e01f}") +#let fa-planet-ringed = fa-icon.with("\u{e020}") +#let fa-plane-up = fa-icon.with("\u{e22d}") +#let fa-plane-up-slash = fa-icon.with("\u{e22e}") +#let fa-plant-wilt = fa-icon.with("\u{e5aa}") +#let fa-plate-utensils = fa-icon.with("\u{e43b}") +#let fa-plate-wheat = fa-icon.with("\u{e55a}") +#let fa-play = fa-icon.with("\u{f04b}") +#let fa-play-pause = fa-icon.with("\u{e22f}") +#let fa-playstation = fa-icon.with("\u{f3df}") +#let fa-plug = fa-icon.with("\u{f1e6}") +#let fa-plug-circle-bolt = fa-icon.with("\u{e55b}") +#let fa-plug-circle-check = fa-icon.with("\u{e55c}") +#let fa-plug-circle-exclamation = fa-icon.with("\u{e55d}") +#let fa-plug-circle-minus = fa-icon.with("\u{e55e}") +#let fa-plug-circle-plus = fa-icon.with("\u{e55f}") +#let fa-plug-circle-xmark = fa-icon.with("\u{e560}") +#let fa-plus = fa-icon.with("\u{2b}") +#let fa-add = fa-icon.with("\u{2b}") +#let fa-plus-large = fa-icon.with("\u{e59e}") +#let fa-plus-minus = fa-icon.with("\u{e43c}") +#let fa-podcast = fa-icon.with("\u{f2ce}") +#let fa-podium = fa-icon.with("\u{f680}") +#let fa-podium-star = fa-icon.with("\u{f758}") +#let fa-police-box = fa-icon.with("\u{e021}") +#let fa-poll-people = fa-icon.with("\u{f759}") +#let fa-pompebled = fa-icon.with("\u{e43d}") +#let fa-poo = fa-icon.with("\u{f2fe}") +#let fa-pool-8-ball = fa-icon.with("\u{e3c5}") +#let fa-poop = fa-icon.with("\u{f619}") +#let fa-poo-storm = fa-icon.with("\u{f75a}") +#let fa-poo-bolt = fa-icon.with("\u{f75a}") +#let fa-popcorn = fa-icon.with("\u{f819}") +#let fa-popsicle = fa-icon.with("\u{e43e}") +#let fa-potato = fa-icon.with("\u{e440}") +#let fa-pot-food = fa-icon.with("\u{e43f}") +#let fa-power-off = fa-icon.with("\u{f011}") +#let fa-prescription = fa-icon.with("\u{f5b1}") +#let fa-prescription-bottle = fa-icon.with("\u{f485}") +#let fa-prescription-bottle-medical = fa-icon.with("\u{f486}") +#let fa-prescription-bottle-alt = fa-icon.with("\u{f486}") +#let fa-prescription-bottle-pill = fa-icon.with("\u{e5c0}") +#let fa-presentation-screen = fa-icon.with("\u{f685}") +#let fa-presentation = fa-icon.with("\u{f685}") +#let fa-pretzel = fa-icon.with("\u{e441}") +#let fa-print = fa-icon.with("\u{f02f}") +#let fa-print-magnifying-glass = fa-icon.with("\u{f81a}") +#let fa-print-search = fa-icon.with("\u{f81a}") +#let fa-print-slash = fa-icon.with("\u{f686}") +#let fa-product-hunt = fa-icon.with("\u{f288}") +#let fa-projector = fa-icon.with("\u{f8d6}") +#let fa-pump = fa-icon.with("\u{e442}") +#let fa-pumpkin = fa-icon.with("\u{f707}") +#let fa-pump-medical = fa-icon.with("\u{e06a}") +#let fa-pump-soap = fa-icon.with("\u{e06b}") +#let fa-pushed = fa-icon.with("\u{f3e1}") +#let fa-puzzle = fa-icon.with("\u{e443}") +#let fa-puzzle-piece = fa-icon.with("\u{f12e}") +#let fa-puzzle-piece-simple = fa-icon.with("\u{e231}") +#let fa-puzzle-piece-alt = fa-icon.with("\u{e231}") +#let fa-python = fa-icon.with("\u{f3e2}") +#let fa-q = fa-icon.with("\u{51}") +#let fa-qq = fa-icon.with("\u{f1d6}") +#let fa-qrcode = fa-icon.with("\u{f029}") +#let fa-question = fa-icon.with("\u{3f}") +#let fa-quinscape = fa-icon.with("\u{f459}") +#let fa-quora = fa-icon.with("\u{f2c4}") +#let fa-quote-left = fa-icon.with("\u{f10d}") +#let fa-quote-left-alt = fa-icon.with("\u{f10d}") +#let fa-quote-right = fa-icon.with("\u{f10e}") +#let fa-quote-right-alt = fa-icon.with("\u{f10e}") +#let fa-quotes = fa-icon.with("\u{e234}") +#let fa-r = fa-icon.with("\u{52}") +#let fa-rabbit = fa-icon.with("\u{f708}") +#let fa-rabbit-running = fa-icon.with("\u{f709}") +#let fa-rabbit-fast = fa-icon.with("\u{f709}") +#let fa-raccoon = fa-icon.with("\u{e613}") +#let fa-racquet = fa-icon.with("\u{f45a}") +#let fa-radar = fa-icon.with("\u{e024}") +#let fa-radiation = fa-icon.with("\u{f7b9}") +#let fa-radio = fa-icon.with("\u{f8d7}") +#let fa-radio-tuner = fa-icon.with("\u{f8d8}") +#let fa-radio-alt = fa-icon.with("\u{f8d8}") +#let fa-rainbow = fa-icon.with("\u{f75b}") +#let fa-raindrops = fa-icon.with("\u{f75c}") +#let fa-ram = fa-icon.with("\u{f70a}") +#let fa-ramp-loading = fa-icon.with("\u{f4d4}") +#let fa-ranking-star = fa-icon.with("\u{e561}") +#let fa-raspberry-pi = fa-icon.with("\u{f7bb}") +#let fa-ravelry = fa-icon.with("\u{f2d9}") +#let fa-raygun = fa-icon.with("\u{e025}") +#let fa-react = fa-icon.with("\u{f41b}") +#let fa-reacteurope = fa-icon.with("\u{f75d}") +#let fa-readme = fa-icon.with("\u{f4d5}") +#let fa-rebel = fa-icon.with("\u{f1d0}") +#let fa-receipt = fa-icon.with("\u{f543}") +#let fa-record-vinyl = fa-icon.with("\u{f8d9}") +#let fa-rectangle = fa-icon.with("\u{f2fa}") +#let fa-rectangle-landscape = fa-icon.with("\u{f2fa}") +#let fa-rectangle-ad = fa-icon.with("\u{f641}") +#let fa-ad = fa-icon.with("\u{f641}") +#let fa-rectangle-barcode = fa-icon.with("\u{f463}") +#let fa-barcode-alt = fa-icon.with("\u{f463}") +#let fa-rectangle-code = fa-icon.with("\u{e322}") +#let fa-rectangle-history = fa-icon.with("\u{e4a2}") +#let fa-rectangle-history-circle-plus = fa-icon.with("\u{e4a3}") +#let fa-rectangle-history-circle-user = fa-icon.with("\u{e4a4}") +#let fa-rectangle-list = fa-icon.with("\u{f022}") +#let fa-list-alt = fa-icon.with("\u{f022}") +#let fa-rectangle-pro = fa-icon.with("\u{e235}") +#let fa-pro = fa-icon.with("\u{e235}") +#let fa-rectangles-mixed = fa-icon.with("\u{e323}") +#let fa-rectangle-terminal = fa-icon.with("\u{e236}") +#let fa-rectangle-vertical = fa-icon.with("\u{f2fb}") +#let fa-rectangle-portrait = fa-icon.with("\u{f2fb}") +#let fa-rectangle-vertical-history = fa-icon.with("\u{e237}") +#let fa-rectangle-wide = fa-icon.with("\u{f2fc}") +#let fa-rectangle-xmark = fa-icon.with("\u{f410}") +#let fa-rectangle-times = fa-icon.with("\u{f410}") +#let fa-times-rectangle = fa-icon.with("\u{f410}") +#let fa-window-close = fa-icon.with("\u{f410}") +#let fa-recycle = fa-icon.with("\u{f1b8}") +#let fa-reddit = fa-icon.with("\u{f1a1}") +#let fa-reddit-alien = fa-icon.with("\u{f281}") +#let fa-redhat = fa-icon.with("\u{f7bc}") +#let fa-red-river = fa-icon.with("\u{f3e3}") +#let fa-reel = fa-icon.with("\u{e238}") +#let fa-reflect-both = fa-icon.with("\u{e66f}") +#let fa-reflect-horizontal = fa-icon.with("\u{e664}") +#let fa-reflect-vertical = fa-icon.with("\u{e665}") +#let fa-refrigerator = fa-icon.with("\u{e026}") +#let fa-registered = fa-icon.with("\u{f25d}") +#let fa-renren = fa-icon.with("\u{f18b}") +#let fa-repeat = fa-icon.with("\u{f363}") +#let fa-repeat-1 = fa-icon.with("\u{f365}") +#let fa-reply = fa-icon.with("\u{f3e5}") +#let fa-mail-reply = fa-icon.with("\u{f3e5}") +#let fa-reply-all = fa-icon.with("\u{f122}") +#let fa-mail-reply-all = fa-icon.with("\u{f122}") +#let fa-reply-clock = fa-icon.with("\u{e239}") +#let fa-reply-time = fa-icon.with("\u{e239}") +#let fa-replyd = fa-icon.with("\u{f3e6}") +#let fa-republican = fa-icon.with("\u{f75e}") +#let fa-researchgate = fa-icon.with("\u{f4f8}") +#let fa-resolving = fa-icon.with("\u{f3e7}") +#let fa-restroom = fa-icon.with("\u{f7bd}") +#let fa-restroom-simple = fa-icon.with("\u{e23a}") +#let fa-retweet = fa-icon.with("\u{f079}") +#let fa-rev = fa-icon.with("\u{f5b2}") +#let fa-rhombus = fa-icon.with("\u{e23b}") +#let fa-ribbon = fa-icon.with("\u{f4d6}") +#let fa-right = fa-icon.with("\u{f356}") +#let fa-arrow-alt-right = fa-icon.with("\u{f356}") +#let fa-right-from-bracket = fa-icon.with("\u{f2f5}") +#let fa-sign-out-alt = fa-icon.with("\u{f2f5}") +#let fa-right-from-line = fa-icon.with("\u{f347}") +#let fa-arrow-alt-from-left = fa-icon.with("\u{f347}") +#let fa-right-left = fa-icon.with("\u{f362}") +#let fa-exchange-alt = fa-icon.with("\u{f362}") +#let fa-right-left-large = fa-icon.with("\u{e5e1}") +#let fa-right-long = fa-icon.with("\u{f30b}") +#let fa-long-arrow-alt-right = fa-icon.with("\u{f30b}") +#let fa-right-long-to-line = fa-icon.with("\u{e444}") +#let fa-right-to-bracket = fa-icon.with("\u{f2f6}") +#let fa-sign-in-alt = fa-icon.with("\u{f2f6}") +#let fa-right-to-line = fa-icon.with("\u{f34c}") +#let fa-arrow-alt-to-right = fa-icon.with("\u{f34c}") +#let fa-ring = fa-icon.with("\u{f70b}") +#let fa-ring-diamond = fa-icon.with("\u{e5ab}") +#let fa-rings-wedding = fa-icon.with("\u{f81b}") +#let fa-road = fa-icon.with("\u{f018}") +#let fa-road-barrier = fa-icon.with("\u{e562}") +#let fa-road-bridge = fa-icon.with("\u{e563}") +#let fa-road-circle-check = fa-icon.with("\u{e564}") +#let fa-road-circle-exclamation = fa-icon.with("\u{e565}") +#let fa-road-circle-xmark = fa-icon.with("\u{e566}") +#let fa-road-lock = fa-icon.with("\u{e567}") +#let fa-road-spikes = fa-icon.with("\u{e568}") +#let fa-robot = fa-icon.with("\u{f544}") +#let fa-robot-astromech = fa-icon.with("\u{e2d2}") +#let fa-rocket = fa-icon.with("\u{f135}") +#let fa-rocketchat = fa-icon.with("\u{f3e8}") +#let fa-rocket-launch = fa-icon.with("\u{e027}") +#let fa-rockrms = fa-icon.with("\u{f3e9}") +#let fa-roller-coaster = fa-icon.with("\u{e324}") +#let fa-rotate = fa-icon.with("\u{f2f1}") +#let fa-sync-alt = fa-icon.with("\u{f2f1}") +#let fa-rotate-exclamation = fa-icon.with("\u{e23c}") +#let fa-rotate-left = fa-icon.with("\u{f2ea}") +#let fa-rotate-back = fa-icon.with("\u{f2ea}") +#let fa-rotate-backward = fa-icon.with("\u{f2ea}") +#let fa-undo-alt = fa-icon.with("\u{f2ea}") +#let fa-rotate-reverse = fa-icon.with("\u{e631}") +#let fa-rotate-right = fa-icon.with("\u{f2f9}") +#let fa-redo-alt = fa-icon.with("\u{f2f9}") +#let fa-rotate-forward = fa-icon.with("\u{f2f9}") +#let fa-route = fa-icon.with("\u{f4d7}") +#let fa-route-highway = fa-icon.with("\u{f61a}") +#let fa-route-interstate = fa-icon.with("\u{f61b}") +#let fa-router = fa-icon.with("\u{f8da}") +#let fa-r-project = fa-icon.with("\u{f4f7}") +#let fa-rss = fa-icon.with("\u{f09e}") +#let fa-feed = fa-icon.with("\u{f09e}") +#let fa-ruble-sign = fa-icon.with("\u{f158}") +#let fa-rouble = fa-icon.with("\u{f158}") +#let fa-rub = fa-icon.with("\u{f158}") +#let fa-ruble = fa-icon.with("\u{f158}") +#let fa-rug = fa-icon.with("\u{e569}") +#let fa-rugby-ball = fa-icon.with("\u{e3c6}") +#let fa-ruler = fa-icon.with("\u{f545}") +#let fa-ruler-combined = fa-icon.with("\u{f546}") +#let fa-ruler-horizontal = fa-icon.with("\u{f547}") +#let fa-ruler-triangle = fa-icon.with("\u{f61c}") +#let fa-ruler-vertical = fa-icon.with("\u{f548}") +#let fa-rupee-sign = fa-icon.with("\u{f156}") +#let fa-rupee = fa-icon.with("\u{f156}") +#let fa-rupiah-sign = fa-icon.with("\u{e23d}") +#let fa-rust = fa-icon.with("\u{e07a}") +#let fa-rv = fa-icon.with("\u{f7be}") +#let fa-s = fa-icon.with("\u{53}") +#let fa-sack = fa-icon.with("\u{f81c}") +#let fa-sack-dollar = fa-icon.with("\u{f81d}") +#let fa-sack-xmark = fa-icon.with("\u{e56a}") +#let fa-safari = fa-icon.with("\u{f267}") +#let fa-sailboat = fa-icon.with("\u{e445}") +#let fa-salad = fa-icon.with("\u{f81e}") +#let fa-bowl-salad = fa-icon.with("\u{f81e}") +#let fa-salesforce = fa-icon.with("\u{f83b}") +#let fa-salt-shaker = fa-icon.with("\u{e446}") +#let fa-sandwich = fa-icon.with("\u{f81f}") +#let fa-sass = fa-icon.with("\u{f41e}") +#let fa-satellite = fa-icon.with("\u{f7bf}") +#let fa-satellite-dish = fa-icon.with("\u{f7c0}") +#let fa-sausage = fa-icon.with("\u{f820}") +#let fa-saxophone = fa-icon.with("\u{f8dc}") +#let fa-saxophone-fire = fa-icon.with("\u{f8db}") +#let fa-sax-hot = fa-icon.with("\u{f8db}") +#let fa-scale-balanced = fa-icon.with("\u{f24e}") +#let fa-balance-scale = fa-icon.with("\u{f24e}") +#let fa-scale-unbalanced = fa-icon.with("\u{f515}") +#let fa-balance-scale-left = fa-icon.with("\u{f515}") +#let fa-scale-unbalanced-flip = fa-icon.with("\u{f516}") +#let fa-balance-scale-right = fa-icon.with("\u{f516}") +#let fa-scalpel = fa-icon.with("\u{f61d}") +#let fa-scalpel-line-dashed = fa-icon.with("\u{f61e}") +#let fa-scalpel-path = fa-icon.with("\u{f61e}") +#let fa-scanner-gun = fa-icon.with("\u{f488}") +#let fa-scanner = fa-icon.with("\u{f488}") +#let fa-scanner-image = fa-icon.with("\u{f8f3}") +#let fa-scanner-keyboard = fa-icon.with("\u{f489}") +#let fa-scanner-touchscreen = fa-icon.with("\u{f48a}") +#let fa-scarecrow = fa-icon.with("\u{f70d}") +#let fa-scarf = fa-icon.with("\u{f7c1}") +#let fa-schlix = fa-icon.with("\u{f3ea}") +#let fa-school = fa-icon.with("\u{f549}") +#let fa-school-circle-check = fa-icon.with("\u{e56b}") +#let fa-school-circle-exclamation = fa-icon.with("\u{e56c}") +#let fa-school-circle-xmark = fa-icon.with("\u{e56d}") +#let fa-school-flag = fa-icon.with("\u{e56e}") +#let fa-school-lock = fa-icon.with("\u{e56f}") +#let fa-scissors = fa-icon.with("\u{f0c4}") +#let fa-cut = fa-icon.with("\u{f0c4}") +#let fa-screencast = fa-icon.with("\u{e23e}") +#let fa-screenpal = fa-icon.with("\u{e570}") +#let fa-screen-users = fa-icon.with("\u{f63d}") +#let fa-users-class = fa-icon.with("\u{f63d}") +#let fa-screwdriver = fa-icon.with("\u{f54a}") +#let fa-screwdriver-wrench = fa-icon.with("\u{f7d9}") +#let fa-tools = fa-icon.with("\u{f7d9}") +#let fa-scribble = fa-icon.with("\u{e23f}") +#let fa-scribd = fa-icon.with("\u{f28a}") +#let fa-scroll = fa-icon.with("\u{f70e}") +#let fa-scroll-old = fa-icon.with("\u{f70f}") +#let fa-scroll-torah = fa-icon.with("\u{f6a0}") +#let fa-torah = fa-icon.with("\u{f6a0}") +#let fa-scrubber = fa-icon.with("\u{f2f8}") +#let fa-scythe = fa-icon.with("\u{f710}") +#let fa-sd-card = fa-icon.with("\u{f7c2}") +#let fa-sd-cards = fa-icon.with("\u{e240}") +#let fa-seal = fa-icon.with("\u{e241}") +#let fa-seal-exclamation = fa-icon.with("\u{e242}") +#let fa-seal-question = fa-icon.with("\u{e243}") +#let fa-searchengin = fa-icon.with("\u{f3eb}") +#let fa-seat-airline = fa-icon.with("\u{e244}") +#let fa-section = fa-icon.with("\u{e447}") +#let fa-seedling = fa-icon.with("\u{f4d8}") +#let fa-sprout = fa-icon.with("\u{f4d8}") +#let fa-sellcast = fa-icon.with("\u{f2da}") +#let fa-sellsy = fa-icon.with("\u{f213}") +#let fa-semicolon = fa-icon.with("\u{3b}") +#let fa-send-back = fa-icon.with("\u{f87e}") +#let fa-send-backward = fa-icon.with("\u{f87f}") +#let fa-sensor = fa-icon.with("\u{e028}") +#let fa-sensor-cloud = fa-icon.with("\u{e02c}") +#let fa-sensor-smoke = fa-icon.with("\u{e02c}") +#let fa-sensor-fire = fa-icon.with("\u{e02a}") +#let fa-sensor-on = fa-icon.with("\u{e02b}") +#let fa-sensor-triangle-exclamation = fa-icon.with("\u{e029}") +#let fa-sensor-alert = fa-icon.with("\u{e029}") +#let fa-server = fa-icon.with("\u{f233}") +#let fa-servicestack = fa-icon.with("\u{f3ec}") +#let fa-shapes = fa-icon.with("\u{f61f}") +#let fa-triangle-circle-square = fa-icon.with("\u{f61f}") +#let fa-share = fa-icon.with("\u{f064}") +#let fa-mail-forward = fa-icon.with("\u{f064}") +#let fa-share-all = fa-icon.with("\u{f367}") +#let fa-share-from-square = fa-icon.with("\u{f14d}") +#let fa-share-square = fa-icon.with("\u{f14d}") +#let fa-share-nodes = fa-icon.with("\u{f1e0}") +#let fa-share-alt = fa-icon.with("\u{f1e0}") +#let fa-sheep = fa-icon.with("\u{f711}") +#let fa-sheet-plastic = fa-icon.with("\u{e571}") +#let fa-shekel-sign = fa-icon.with("\u{f20b}") +#let fa-ils = fa-icon.with("\u{f20b}") +#let fa-shekel = fa-icon.with("\u{f20b}") +#let fa-sheqel = fa-icon.with("\u{f20b}") +#let fa-sheqel-sign = fa-icon.with("\u{f20b}") +#let fa-shelves = fa-icon.with("\u{f480}") +#let fa-inventory = fa-icon.with("\u{f480}") +#let fa-shelves-empty = fa-icon.with("\u{e246}") +#let fa-shield = fa-icon.with("\u{f132}") +#let fa-shield-blank = fa-icon.with("\u{f132}") +#let fa-shield-cat = fa-icon.with("\u{e572}") +#let fa-shield-check = fa-icon.with("\u{f2f7}") +#let fa-shield-cross = fa-icon.with("\u{f712}") +#let fa-shield-dog = fa-icon.with("\u{e573}") +#let fa-shield-exclamation = fa-icon.with("\u{e247}") +#let fa-shield-halved = fa-icon.with("\u{f3ed}") +#let fa-shield-alt = fa-icon.with("\u{f3ed}") +#let fa-shield-heart = fa-icon.with("\u{e574}") +#let fa-shield-keyhole = fa-icon.with("\u{e248}") +#let fa-shield-minus = fa-icon.with("\u{e249}") +#let fa-shield-plus = fa-icon.with("\u{e24a}") +#let fa-shield-quartered = fa-icon.with("\u{e575}") +#let fa-shield-slash = fa-icon.with("\u{e24b}") +#let fa-shield-virus = fa-icon.with("\u{e06c}") +#let fa-shield-xmark = fa-icon.with("\u{e24c}") +#let fa-shield-times = fa-icon.with("\u{e24c}") +#let fa-ship = fa-icon.with("\u{f21a}") +#let fa-shirt = fa-icon.with("\u{f553}") +#let fa-t-shirt = fa-icon.with("\u{f553}") +#let fa-tshirt = fa-icon.with("\u{f553}") +#let fa-shirt-long-sleeve = fa-icon.with("\u{e3c7}") +#let fa-shirt-running = fa-icon.with("\u{e3c8}") +#let fa-shirtsinbulk = fa-icon.with("\u{f214}") +#let fa-shirt-tank-top = fa-icon.with("\u{e3c9}") +#let fa-shish-kebab = fa-icon.with("\u{f821}") +#let fa-shoelace = fa-icon.with("\u{e60c}") +#let fa-shoe-prints = fa-icon.with("\u{f54b}") +#let fa-shop = fa-icon.with("\u{f54f}") +#let fa-store-alt = fa-icon.with("\u{f54f}") +#let fa-shopify = fa-icon.with("\u{e057}") +#let fa-shop-lock = fa-icon.with("\u{e4a5}") +#let fa-shop-slash = fa-icon.with("\u{e070}") +#let fa-store-alt-slash = fa-icon.with("\u{e070}") +#let fa-shopware = fa-icon.with("\u{f5b5}") +#let fa-shovel = fa-icon.with("\u{f713}") +#let fa-shovel-snow = fa-icon.with("\u{f7c3}") +#let fa-shower = fa-icon.with("\u{f2cc}") +#let fa-shower-down = fa-icon.with("\u{e24d}") +#let fa-shower-alt = fa-icon.with("\u{e24d}") +#let fa-shredder = fa-icon.with("\u{f68a}") +#let fa-shrimp = fa-icon.with("\u{e448}") +#let fa-shuffle = fa-icon.with("\u{f074}") +#let fa-random = fa-icon.with("\u{f074}") +#let fa-shutters = fa-icon.with("\u{e449}") +#let fa-shuttlecock = fa-icon.with("\u{f45b}") +#let fa-shuttle-space = fa-icon.with("\u{f197}") +#let fa-space-shuttle = fa-icon.with("\u{f197}") +#let fa-sickle = fa-icon.with("\u{f822}") +#let fa-sidebar = fa-icon.with("\u{e24e}") +#let fa-sidebar-flip = fa-icon.with("\u{e24f}") +#let fa-sigma = fa-icon.with("\u{f68b}") +#let fa-signal = fa-icon.with("\u{f012}") +#let fa-signal-5 = fa-icon.with("\u{f012}") +#let fa-signal-perfect = fa-icon.with("\u{f012}") +#let fa-signal-bars = fa-icon.with("\u{f690}") +#let fa-signal-alt = fa-icon.with("\u{f690}") +#let fa-signal-alt-4 = fa-icon.with("\u{f690}") +#let fa-signal-bars-strong = fa-icon.with("\u{f690}") +#let fa-signal-bars-fair = fa-icon.with("\u{f692}") +#let fa-signal-alt-2 = fa-icon.with("\u{f692}") +#let fa-signal-bars-good = fa-icon.with("\u{f693}") +#let fa-signal-alt-3 = fa-icon.with("\u{f693}") +#let fa-signal-bars-slash = fa-icon.with("\u{f694}") +#let fa-signal-alt-slash = fa-icon.with("\u{f694}") +#let fa-signal-bars-weak = fa-icon.with("\u{f691}") +#let fa-signal-alt-1 = fa-icon.with("\u{f691}") +#let fa-signal-fair = fa-icon.with("\u{f68d}") +#let fa-signal-2 = fa-icon.with("\u{f68d}") +#let fa-signal-good = fa-icon.with("\u{f68e}") +#let fa-signal-3 = fa-icon.with("\u{f68e}") +#let fa-signal-messenger = fa-icon.with("\u{e663}") +#let fa-signal-slash = fa-icon.with("\u{f695}") +#let fa-signal-stream = fa-icon.with("\u{f8dd}") +#let fa-signal-stream-slash = fa-icon.with("\u{e250}") +#let fa-signal-strong = fa-icon.with("\u{f68f}") +#let fa-signal-4 = fa-icon.with("\u{f68f}") +#let fa-signal-weak = fa-icon.with("\u{f68c}") +#let fa-signal-1 = fa-icon.with("\u{f68c}") +#let fa-signature = fa-icon.with("\u{f5b7}") +#let fa-signature-lock = fa-icon.with("\u{e3ca}") +#let fa-signature-slash = fa-icon.with("\u{e3cb}") +#let fa-sign-hanging = fa-icon.with("\u{f4d9}") +#let fa-sign = fa-icon.with("\u{f4d9}") +#let fa-sign-post = fa-icon.with("\u{e624}") +#let fa-sign-posts = fa-icon.with("\u{e625}") +#let fa-sign-posts-wrench = fa-icon.with("\u{e626}") +#let fa-signs-post = fa-icon.with("\u{f277}") +#let fa-map-signs = fa-icon.with("\u{f277}") +#let fa-sim-card = fa-icon.with("\u{f7c4}") +#let fa-sim-cards = fa-icon.with("\u{e251}") +#let fa-simplybuilt = fa-icon.with("\u{f215}") +#let fa-sink = fa-icon.with("\u{e06d}") +#let fa-siren = fa-icon.with("\u{e02d}") +#let fa-siren-on = fa-icon.with("\u{e02e}") +#let fa-sistrix = fa-icon.with("\u{f3ee}") +#let fa-sitemap = fa-icon.with("\u{f0e8}") +#let fa-sith = fa-icon.with("\u{f512}") +#let fa-sitrox = fa-icon.with("\u{e44a}") +#let fa-skeleton = fa-icon.with("\u{f620}") +#let fa-skeleton-ribs = fa-icon.with("\u{e5cb}") +#let fa-sketch = fa-icon.with("\u{f7c6}") +#let fa-ski-boot = fa-icon.with("\u{e3cc}") +#let fa-ski-boot-ski = fa-icon.with("\u{e3cd}") +#let fa-skull = fa-icon.with("\u{f54c}") +#let fa-skull-cow = fa-icon.with("\u{f8de}") +#let fa-skull-crossbones = fa-icon.with("\u{f714}") +#let fa-skyatlas = fa-icon.with("\u{f216}") +#let fa-skype = fa-icon.with("\u{f17e}") +#let fa-slack = fa-icon.with("\u{f198}") +#let fa-slack-hash = fa-icon.with("\u{f198}") +#let fa-slash = fa-icon.with("\u{f715}") +#let fa-slash-back = fa-icon.with("\u{5c}") +#let fa-slash-forward = fa-icon.with("\u{2f}") +#let fa-sleigh = fa-icon.with("\u{f7cc}") +#let fa-slider = fa-icon.with("\u{e252}") +#let fa-sliders = fa-icon.with("\u{f1de}") +#let fa-sliders-h = fa-icon.with("\u{f1de}") +#let fa-sliders-simple = fa-icon.with("\u{e253}") +#let fa-sliders-up = fa-icon.with("\u{f3f1}") +#let fa-sliders-v = fa-icon.with("\u{f3f1}") +#let fa-slideshare = fa-icon.with("\u{f1e7}") +#let fa-slot-machine = fa-icon.with("\u{e3ce}") +#let fa-smog = fa-icon.with("\u{f75f}") +#let fa-smoke = fa-icon.with("\u{f760}") +#let fa-smoking = fa-icon.with("\u{f48d}") +#let fa-snake = fa-icon.with("\u{f716}") +#let fa-snapchat = fa-icon.with("\u{f2ab}") +#let fa-snapchat-ghost = fa-icon.with("\u{f2ab}") +#let fa-snooze = fa-icon.with("\u{f880}") +#let fa-zzz = fa-icon.with("\u{f880}") +#let fa-snow-blowing = fa-icon.with("\u{f761}") +#let fa-snowflake = fa-icon.with("\u{f2dc}") +#let fa-snowflake-droplets = fa-icon.with("\u{e5c1}") +#let fa-snowflakes = fa-icon.with("\u{f7cf}") +#let fa-snowman = fa-icon.with("\u{f7d0}") +#let fa-snowman-head = fa-icon.with("\u{f79b}") +#let fa-frosty-head = fa-icon.with("\u{f79b}") +#let fa-snowplow = fa-icon.with("\u{f7d2}") +#let fa-soap = fa-icon.with("\u{e06e}") +#let fa-socks = fa-icon.with("\u{f696}") +#let fa-soft-serve = fa-icon.with("\u{e400}") +#let fa-creemee = fa-icon.with("\u{e400}") +#let fa-solar-panel = fa-icon.with("\u{f5ba}") +#let fa-solar-system = fa-icon.with("\u{e02f}") +#let fa-sort = fa-icon.with("\u{f0dc}") +#let fa-unsorted = fa-icon.with("\u{f0dc}") +#let fa-sort-down = fa-icon.with("\u{f0dd}") +#let fa-sort-desc = fa-icon.with("\u{f0dd}") +#let fa-sort-up = fa-icon.with("\u{f0de}") +#let fa-sort-asc = fa-icon.with("\u{f0de}") +#let fa-soundcloud = fa-icon.with("\u{f1be}") +#let fa-sourcetree = fa-icon.with("\u{f7d3}") +#let fa-spa = fa-icon.with("\u{f5bb}") +#let fa-space-awesome = fa-icon.with("\u{e5ac}") +#let fa-space-station-moon = fa-icon.with("\u{e033}") +#let fa-space-station-moon-construction = fa-icon.with("\u{e034}") +#let fa-space-station-moon-alt = fa-icon.with("\u{e034}") +#let fa-spade = fa-icon.with("\u{f2f4}") +#let fa-spaghetti-monster-flying = fa-icon.with("\u{f67b}") +#let fa-pastafarianism = fa-icon.with("\u{f67b}") +#let fa-sparkle = fa-icon.with("\u{e5d6}") +#let fa-sparkles = fa-icon.with("\u{f890}") +#let fa-speakap = fa-icon.with("\u{f3f3}") +#let fa-speaker = fa-icon.with("\u{f8df}") +#let fa-speaker-deck = fa-icon.with("\u{f83c}") +#let fa-speakers = fa-icon.with("\u{f8e0}") +#let fa-spell-check = fa-icon.with("\u{f891}") +#let fa-spider = fa-icon.with("\u{f717}") +#let fa-spider-black-widow = fa-icon.with("\u{f718}") +#let fa-spider-web = fa-icon.with("\u{f719}") +#let fa-spinner = fa-icon.with("\u{f110}") +#let fa-spinner-scale = fa-icon.with("\u{e62a}") +#let fa-spinner-third = fa-icon.with("\u{f3f4}") +#let fa-split = fa-icon.with("\u{e254}") +#let fa-splotch = fa-icon.with("\u{f5bc}") +#let fa-spoon = fa-icon.with("\u{f2e5}") +#let fa-utensil-spoon = fa-icon.with("\u{f2e5}") +#let fa-sportsball = fa-icon.with("\u{e44b}") +#let fa-spotify = fa-icon.with("\u{f1bc}") +#let fa-spray-can = fa-icon.with("\u{f5bd}") +#let fa-spray-can-sparkles = fa-icon.with("\u{f5d0}") +#let fa-air-freshener = fa-icon.with("\u{f5d0}") +#let fa-sprinkler = fa-icon.with("\u{e035}") +#let fa-sprinkler-ceiling = fa-icon.with("\u{e44c}") +#let fa-square = fa-icon.with("\u{f0c8}") +#let fa-square-0 = fa-icon.with("\u{e255}") +#let fa-square-1 = fa-icon.with("\u{e256}") +#let fa-square-2 = fa-icon.with("\u{e257}") +#let fa-square-3 = fa-icon.with("\u{e258}") +#let fa-square-4 = fa-icon.with("\u{e259}") +#let fa-square-5 = fa-icon.with("\u{e25a}") +#let fa-square-6 = fa-icon.with("\u{e25b}") +#let fa-square-7 = fa-icon.with("\u{e25c}") +#let fa-square-8 = fa-icon.with("\u{e25d}") +#let fa-square-9 = fa-icon.with("\u{e25e}") +#let fa-square-a = fa-icon.with("\u{e25f}") +#let fa-square-a-lock = fa-icon.with("\u{e44d}") +#let fa-square-ampersand = fa-icon.with("\u{e260}") +#let fa-square-arrow-down = fa-icon.with("\u{f339}") +#let fa-arrow-square-down = fa-icon.with("\u{f339}") +#let fa-square-arrow-down-left = fa-icon.with("\u{e261}") +#let fa-square-arrow-down-right = fa-icon.with("\u{e262}") +#let fa-square-arrow-left = fa-icon.with("\u{f33a}") +#let fa-arrow-square-left = fa-icon.with("\u{f33a}") +#let fa-square-arrow-right = fa-icon.with("\u{f33b}") +#let fa-arrow-square-right = fa-icon.with("\u{f33b}") +#let fa-square-arrow-up = fa-icon.with("\u{f33c}") +#let fa-arrow-square-up = fa-icon.with("\u{f33c}") +#let fa-square-arrow-up-left = fa-icon.with("\u{e263}") +#let fa-square-arrow-up-right = fa-icon.with("\u{f14c}") +#let fa-external-link-square = fa-icon.with("\u{f14c}") +#let fa-square-b = fa-icon.with("\u{e264}") +#let fa-square-behance = fa-icon.with("\u{f1b5}") +#let fa-behance-square = fa-icon.with("\u{f1b5}") +#let fa-square-bolt = fa-icon.with("\u{e265}") +#let fa-square-c = fa-icon.with("\u{e266}") +#let fa-square-caret-down = fa-icon.with("\u{f150}") +#let fa-caret-square-down = fa-icon.with("\u{f150}") +#let fa-square-caret-left = fa-icon.with("\u{f191}") +#let fa-caret-square-left = fa-icon.with("\u{f191}") +#let fa-square-caret-right = fa-icon.with("\u{f152}") +#let fa-caret-square-right = fa-icon.with("\u{f152}") +#let fa-square-caret-up = fa-icon.with("\u{f151}") +#let fa-caret-square-up = fa-icon.with("\u{f151}") +#let fa-square-check = fa-icon.with("\u{f14a}") +#let fa-check-square = fa-icon.with("\u{f14a}") +#let fa-square-chevron-down = fa-icon.with("\u{f329}") +#let fa-chevron-square-down = fa-icon.with("\u{f329}") +#let fa-square-chevron-left = fa-icon.with("\u{f32a}") +#let fa-chevron-square-left = fa-icon.with("\u{f32a}") +#let fa-square-chevron-right = fa-icon.with("\u{f32b}") +#let fa-chevron-square-right = fa-icon.with("\u{f32b}") +#let fa-square-chevron-up = fa-icon.with("\u{f32c}") +#let fa-chevron-square-up = fa-icon.with("\u{f32c}") +#let fa-square-code = fa-icon.with("\u{e267}") +#let fa-square-d = fa-icon.with("\u{e268}") +#let fa-square-dashed = fa-icon.with("\u{e269}") +#let fa-square-dashed-circle-plus = fa-icon.with("\u{e5c2}") +#let fa-square-divide = fa-icon.with("\u{e26a}") +#let fa-square-dollar = fa-icon.with("\u{f2e9}") +#let fa-dollar-square = fa-icon.with("\u{f2e9}") +#let fa-usd-square = fa-icon.with("\u{f2e9}") +#let fa-square-down = fa-icon.with("\u{f350}") +#let fa-arrow-alt-square-down = fa-icon.with("\u{f350}") +#let fa-square-down-left = fa-icon.with("\u{e26b}") +#let fa-square-down-right = fa-icon.with("\u{e26c}") +#let fa-square-dribbble = fa-icon.with("\u{f397}") +#let fa-dribbble-square = fa-icon.with("\u{f397}") +#let fa-square-e = fa-icon.with("\u{e26d}") +#let fa-square-ellipsis = fa-icon.with("\u{e26e}") +#let fa-square-ellipsis-vertical = fa-icon.with("\u{e26f}") +#let fa-square-envelope = fa-icon.with("\u{f199}") +#let fa-envelope-square = fa-icon.with("\u{f199}") +#let fa-square-exclamation = fa-icon.with("\u{f321}") +#let fa-exclamation-square = fa-icon.with("\u{f321}") +#let fa-square-f = fa-icon.with("\u{e270}") +#let fa-square-facebook = fa-icon.with("\u{f082}") +#let fa-facebook-square = fa-icon.with("\u{f082}") +#let fa-square-font-awesome = fa-icon.with("\u{e5ad}") +#let fa-square-font-awesome-stroke = fa-icon.with("\u{f35c}") +#let fa-font-awesome-alt = fa-icon.with("\u{f35c}") +#let fa-square-fragile = fa-icon.with("\u{f49b}") +#let fa-box-fragile = fa-icon.with("\u{f49b}") +#let fa-square-wine-glass-crack = fa-icon.with("\u{f49b}") +#let fa-square-full = fa-icon.with("\u{f45c}") +#let fa-square-g = fa-icon.with("\u{e271}") +#let fa-square-git = fa-icon.with("\u{f1d2}") +#let fa-git-square = fa-icon.with("\u{f1d2}") +#let fa-square-github = fa-icon.with("\u{f092}") +#let fa-github-square = fa-icon.with("\u{f092}") +#let fa-square-gitlab = fa-icon.with("\u{e5ae}") +#let fa-gitlab-square = fa-icon.with("\u{e5ae}") +#let fa-square-google-plus = fa-icon.with("\u{f0d4}") +#let fa-google-plus-square = fa-icon.with("\u{f0d4}") +#let fa-square-h = fa-icon.with("\u{f0fd}") +#let fa-h-square = fa-icon.with("\u{f0fd}") +#let fa-square-hacker-news = fa-icon.with("\u{f3af}") +#let fa-hacker-news-square = fa-icon.with("\u{f3af}") +#let fa-square-heart = fa-icon.with("\u{f4c8}") +#let fa-heart-square = fa-icon.with("\u{f4c8}") +#let fa-square-i = fa-icon.with("\u{e272}") +#let fa-square-info = fa-icon.with("\u{f30f}") +#let fa-info-square = fa-icon.with("\u{f30f}") +#let fa-square-instagram = fa-icon.with("\u{e055}") +#let fa-instagram-square = fa-icon.with("\u{e055}") +#let fa-square-j = fa-icon.with("\u{e273}") +#let fa-square-js = fa-icon.with("\u{f3b9}") +#let fa-js-square = fa-icon.with("\u{f3b9}") +#let fa-square-k = fa-icon.with("\u{e274}") +#let fa-square-kanban = fa-icon.with("\u{e488}") +#let fa-square-l = fa-icon.with("\u{e275}") +#let fa-square-lastfm = fa-icon.with("\u{f203}") +#let fa-lastfm-square = fa-icon.with("\u{f203}") +#let fa-square-left = fa-icon.with("\u{f351}") +#let fa-arrow-alt-square-left = fa-icon.with("\u{f351}") +#let fa-square-letterboxd = fa-icon.with("\u{e62e}") +#let fa-square-list = fa-icon.with("\u{e489}") +#let fa-square-m = fa-icon.with("\u{e276}") +#let fa-square-minus = fa-icon.with("\u{f146}") +#let fa-minus-square = fa-icon.with("\u{f146}") +#let fa-square-n = fa-icon.with("\u{e277}") +#let fa-square-nfi = fa-icon.with("\u{e576}") +#let fa-square-o = fa-icon.with("\u{e278}") +#let fa-square-odnoklassniki = fa-icon.with("\u{f264}") +#let fa-odnoklassniki-square = fa-icon.with("\u{f264}") +#let fa-square-p = fa-icon.with("\u{e279}") +#let fa-square-parking = fa-icon.with("\u{f540}") +#let fa-parking = fa-icon.with("\u{f540}") +#let fa-square-parking-slash = fa-icon.with("\u{f617}") +#let fa-parking-slash = fa-icon.with("\u{f617}") +#let fa-square-pen = fa-icon.with("\u{f14b}") +#let fa-pen-square = fa-icon.with("\u{f14b}") +#let fa-pencil-square = fa-icon.with("\u{f14b}") +#let fa-square-person-confined = fa-icon.with("\u{e577}") +#let fa-square-phone = fa-icon.with("\u{f098}") +#let fa-phone-square = fa-icon.with("\u{f098}") +#let fa-square-phone-flip = fa-icon.with("\u{f87b}") +#let fa-phone-square-alt = fa-icon.with("\u{f87b}") +#let fa-square-phone-hangup = fa-icon.with("\u{e27a}") +#let fa-phone-square-down = fa-icon.with("\u{e27a}") +#let fa-square-pied-piper = fa-icon.with("\u{e01e}") +#let fa-pied-piper-square = fa-icon.with("\u{e01e}") +#let fa-square-pinterest = fa-icon.with("\u{f0d3}") +#let fa-pinterest-square = fa-icon.with("\u{f0d3}") +#let fa-square-plus = fa-icon.with("\u{f0fe}") +#let fa-plus-square = fa-icon.with("\u{f0fe}") +#let fa-square-poll-horizontal = fa-icon.with("\u{f682}") +#let fa-poll-h = fa-icon.with("\u{f682}") +#let fa-square-poll-vertical = fa-icon.with("\u{f681}") +#let fa-poll = fa-icon.with("\u{f681}") +#let fa-square-q = fa-icon.with("\u{e27b}") +#let fa-square-quarters = fa-icon.with("\u{e44e}") +#let fa-square-question = fa-icon.with("\u{f2fd}") +#let fa-question-square = fa-icon.with("\u{f2fd}") +#let fa-square-quote = fa-icon.with("\u{e329}") +#let fa-square-r = fa-icon.with("\u{e27c}") +#let fa-square-reddit = fa-icon.with("\u{f1a2}") +#let fa-reddit-square = fa-icon.with("\u{f1a2}") +#let fa-square-right = fa-icon.with("\u{f352}") +#let fa-arrow-alt-square-right = fa-icon.with("\u{f352}") +#let fa-square-ring = fa-icon.with("\u{e44f}") +#let fa-square-root = fa-icon.with("\u{f697}") +#let fa-square-root-variable = fa-icon.with("\u{f698}") +#let fa-square-root-alt = fa-icon.with("\u{f698}") +#let fa-square-rss = fa-icon.with("\u{f143}") +#let fa-rss-square = fa-icon.with("\u{f143}") +#let fa-square-s = fa-icon.with("\u{e27d}") +#let fa-square-share-nodes = fa-icon.with("\u{f1e1}") +#let fa-share-alt-square = fa-icon.with("\u{f1e1}") +#let fa-square-sliders = fa-icon.with("\u{f3f0}") +#let fa-sliders-h-square = fa-icon.with("\u{f3f0}") +#let fa-square-sliders-vertical = fa-icon.with("\u{f3f2}") +#let fa-sliders-v-square = fa-icon.with("\u{f3f2}") +#let fa-square-small = fa-icon.with("\u{e27e}") +#let fa-square-snapchat = fa-icon.with("\u{f2ad}") +#let fa-snapchat-square = fa-icon.with("\u{f2ad}") +#let fa-squarespace = fa-icon.with("\u{f5be}") +#let fa-square-star = fa-icon.with("\u{e27f}") +#let fa-square-steam = fa-icon.with("\u{f1b7}") +#let fa-steam-square = fa-icon.with("\u{f1b7}") +#let fa-square-t = fa-icon.with("\u{e280}") +#let fa-square-terminal = fa-icon.with("\u{e32a}") +#let fa-square-this-way-up = fa-icon.with("\u{f49f}") +#let fa-box-up = fa-icon.with("\u{f49f}") +#let fa-square-threads = fa-icon.with("\u{e619}") +#let fa-square-tumblr = fa-icon.with("\u{f174}") +#let fa-tumblr-square = fa-icon.with("\u{f174}") +#let fa-square-twitter = fa-icon.with("\u{f081}") +#let fa-twitter-square = fa-icon.with("\u{f081}") +#let fa-square-u = fa-icon.with("\u{e281}") +#let fa-square-up = fa-icon.with("\u{f353}") +#let fa-arrow-alt-square-up = fa-icon.with("\u{f353}") +#let fa-square-up-left = fa-icon.with("\u{e282}") +#let fa-square-up-right = fa-icon.with("\u{f360}") +#let fa-external-link-square-alt = fa-icon.with("\u{f360}") +#let fa-square-upwork = fa-icon.with("\u{e67c}") +#let fa-square-user = fa-icon.with("\u{e283}") +#let fa-square-v = fa-icon.with("\u{e284}") +#let fa-square-viadeo = fa-icon.with("\u{f2aa}") +#let fa-viadeo-square = fa-icon.with("\u{f2aa}") +#let fa-square-vimeo = fa-icon.with("\u{f194}") +#let fa-vimeo-square = fa-icon.with("\u{f194}") +#let fa-square-virus = fa-icon.with("\u{e578}") +#let fa-square-w = fa-icon.with("\u{e285}") +#let fa-square-web-awesome = fa-icon.with("\u{e683}") +#let fa-square-web-awesome-stroke = fa-icon.with("\u{e684}") +#let fa-square-whatsapp = fa-icon.with("\u{f40c}") +#let fa-whatsapp-square = fa-icon.with("\u{f40c}") +#let fa-square-x = fa-icon.with("\u{e286}") +#let fa-square-xing = fa-icon.with("\u{f169}") +#let fa-xing-square = fa-icon.with("\u{f169}") +#let fa-square-xmark = fa-icon.with("\u{f2d3}") +#let fa-times-square = fa-icon.with("\u{f2d3}") +#let fa-xmark-square = fa-icon.with("\u{f2d3}") +#let fa-square-x-twitter = fa-icon.with("\u{e61a}") +#let fa-square-y = fa-icon.with("\u{e287}") +#let fa-square-youtube = fa-icon.with("\u{f431}") +#let fa-youtube-square = fa-icon.with("\u{f431}") +#let fa-square-z = fa-icon.with("\u{e288}") +#let fa-squid = fa-icon.with("\u{e450}") +#let fa-squirrel = fa-icon.with("\u{f71a}") +#let fa-stack-exchange = fa-icon.with("\u{f18d}") +#let fa-stack-overflow = fa-icon.with("\u{f16c}") +#let fa-stackpath = fa-icon.with("\u{f842}") +#let fa-staff = fa-icon.with("\u{f71b}") +#let fa-staff-snake = fa-icon.with("\u{e579}") +#let fa-rod-asclepius = fa-icon.with("\u{e579}") +#let fa-rod-snake = fa-icon.with("\u{e579}") +#let fa-staff-aesculapius = fa-icon.with("\u{e579}") +#let fa-stairs = fa-icon.with("\u{e289}") +#let fa-stamp = fa-icon.with("\u{f5bf}") +#let fa-standard-definition = fa-icon.with("\u{e28a}") +#let fa-rectangle-sd = fa-icon.with("\u{e28a}") +#let fa-stapler = fa-icon.with("\u{e5af}") +#let fa-star = fa-icon.with("\u{f005}") +#let fa-star-and-crescent = fa-icon.with("\u{f699}") +#let fa-star-christmas = fa-icon.with("\u{f7d4}") +#let fa-star-exclamation = fa-icon.with("\u{f2f3}") +#let fa-starfighter = fa-icon.with("\u{e037}") +#let fa-starfighter-twin-ion-engine = fa-icon.with("\u{e038}") +#let fa-starfighter-alt = fa-icon.with("\u{e038}") +#let fa-starfighter-twin-ion-engine-advanced = fa-icon.with("\u{e28e}") +#let fa-starfighter-alt-advanced = fa-icon.with("\u{e28e}") +#let fa-star-half = fa-icon.with("\u{f089}") +#let fa-star-half-stroke = fa-icon.with("\u{f5c0}") +#let fa-star-half-alt = fa-icon.with("\u{f5c0}") +#let fa-star-of-david = fa-icon.with("\u{f69a}") +#let fa-star-of-life = fa-icon.with("\u{f621}") +#let fa-stars = fa-icon.with("\u{f762}") +#let fa-star-sharp = fa-icon.with("\u{e28b}") +#let fa-star-sharp-half = fa-icon.with("\u{e28c}") +#let fa-star-sharp-half-stroke = fa-icon.with("\u{e28d}") +#let fa-star-sharp-half-alt = fa-icon.with("\u{e28d}") +#let fa-starship = fa-icon.with("\u{e039}") +#let fa-starship-freighter = fa-icon.with("\u{e03a}") +#let fa-star-shooting = fa-icon.with("\u{e036}") +#let fa-staylinked = fa-icon.with("\u{f3f5}") +#let fa-steak = fa-icon.with("\u{f824}") +#let fa-steam = fa-icon.with("\u{f1b6}") +#let fa-steam-symbol = fa-icon.with("\u{f3f6}") +#let fa-steering-wheel = fa-icon.with("\u{f622}") +#let fa-sterling-sign = fa-icon.with("\u{f154}") +#let fa-gbp = fa-icon.with("\u{f154}") +#let fa-pound-sign = fa-icon.with("\u{f154}") +#let fa-stethoscope = fa-icon.with("\u{f0f1}") +#let fa-sticker-mule = fa-icon.with("\u{f3f7}") +#let fa-stocking = fa-icon.with("\u{f7d5}") +#let fa-stomach = fa-icon.with("\u{f623}") +#let fa-stop = fa-icon.with("\u{f04d}") +#let fa-stopwatch = fa-icon.with("\u{f2f2}") +#let fa-stopwatch-20 = fa-icon.with("\u{e06f}") +#let fa-store = fa-icon.with("\u{f54e}") +#let fa-store-lock = fa-icon.with("\u{e4a6}") +#let fa-store-slash = fa-icon.with("\u{e071}") +#let fa-strava = fa-icon.with("\u{f428}") +#let fa-strawberry = fa-icon.with("\u{e32b}") +#let fa-street-view = fa-icon.with("\u{f21d}") +#let fa-stretcher = fa-icon.with("\u{f825}") +#let fa-strikethrough = fa-icon.with("\u{f0cc}") +#let fa-stripe = fa-icon.with("\u{f429}") +#let fa-stripe-s = fa-icon.with("\u{f42a}") +#let fa-stroopwafel = fa-icon.with("\u{f551}") +#let fa-stubber = fa-icon.with("\u{e5c7}") +#let fa-studiovinari = fa-icon.with("\u{f3f8}") +#let fa-stumbleupon = fa-icon.with("\u{f1a4}") +#let fa-stumbleupon-circle = fa-icon.with("\u{f1a3}") +#let fa-subscript = fa-icon.with("\u{f12c}") +#let fa-subtitles = fa-icon.with("\u{e60f}") +#let fa-subtitles-slash = fa-icon.with("\u{e610}") +#let fa-suitcase = fa-icon.with("\u{f0f2}") +#let fa-suitcase-medical = fa-icon.with("\u{f0fa}") +#let fa-medkit = fa-icon.with("\u{f0fa}") +#let fa-suitcase-rolling = fa-icon.with("\u{f5c1}") +#let fa-sun = fa-icon.with("\u{f185}") +#let fa-sun-bright = fa-icon.with("\u{e28f}") +#let fa-sun-alt = fa-icon.with("\u{e28f}") +#let fa-sun-cloud = fa-icon.with("\u{f763}") +#let fa-sun-dust = fa-icon.with("\u{f764}") +#let fa-sunglasses = fa-icon.with("\u{f892}") +#let fa-sun-haze = fa-icon.with("\u{f765}") +#let fa-sun-plant-wilt = fa-icon.with("\u{e57a}") +#let fa-sunrise = fa-icon.with("\u{f766}") +#let fa-sunset = fa-icon.with("\u{f767}") +#let fa-superpowers = fa-icon.with("\u{f2dd}") +#let fa-superscript = fa-icon.with("\u{f12b}") +#let fa-supple = fa-icon.with("\u{f3f9}") +#let fa-suse = fa-icon.with("\u{f7d6}") +#let fa-sushi = fa-icon.with("\u{e48a}") +#let fa-nigiri = fa-icon.with("\u{e48a}") +#let fa-sushi-roll = fa-icon.with("\u{e48b}") +#let fa-maki-roll = fa-icon.with("\u{e48b}") +#let fa-makizushi = fa-icon.with("\u{e48b}") +#let fa-swap = fa-icon.with("\u{e609}") +#let fa-swap-arrows = fa-icon.with("\u{e60a}") +#let fa-swatchbook = fa-icon.with("\u{f5c3}") +#let fa-swift = fa-icon.with("\u{f8e1}") +#let fa-sword = fa-icon.with("\u{f71c}") +#let fa-sword-laser = fa-icon.with("\u{e03b}") +#let fa-sword-laser-alt = fa-icon.with("\u{e03c}") +#let fa-swords = fa-icon.with("\u{f71d}") +#let fa-swords-laser = fa-icon.with("\u{e03d}") +#let fa-symbols = fa-icon.with("\u{f86e}") +#let fa-icons-alt = fa-icon.with("\u{f86e}") +#let fa-symfony = fa-icon.with("\u{f83d}") +#let fa-synagogue = fa-icon.with("\u{f69b}") +#let fa-syringe = fa-icon.with("\u{f48e}") +#let fa-t = fa-icon.with("\u{54}") +#let fa-table = fa-icon.with("\u{f0ce}") +#let fa-table-cells = fa-icon.with("\u{f00a}") +#let fa-th = fa-icon.with("\u{f00a}") +#let fa-table-cells-column-lock = fa-icon.with("\u{e678}") +#let fa-table-cells-column-unlock = fa-icon.with("\u{e690}") +#let fa-table-cells-large = fa-icon.with("\u{f009}") +#let fa-th-large = fa-icon.with("\u{f009}") +#let fa-table-cells-lock = fa-icon.with("\u{e679}") +#let fa-table-cells-row-lock = fa-icon.with("\u{e67a}") +#let fa-table-cells-row-unlock = fa-icon.with("\u{e691}") +#let fa-table-cells-unlock = fa-icon.with("\u{e692}") +#let fa-table-columns = fa-icon.with("\u{f0db}") +#let fa-columns = fa-icon.with("\u{f0db}") +#let fa-table-layout = fa-icon.with("\u{e290}") +#let fa-table-list = fa-icon.with("\u{f00b}") +#let fa-th-list = fa-icon.with("\u{f00b}") +#let fa-table-picnic = fa-icon.with("\u{e32d}") +#let fa-table-pivot = fa-icon.with("\u{e291}") +#let fa-table-rows = fa-icon.with("\u{e292}") +#let fa-rows = fa-icon.with("\u{e292}") +#let fa-tablet = fa-icon.with("\u{f3fb}") +#let fa-tablet-android = fa-icon.with("\u{f3fb}") +#let fa-tablet-button = fa-icon.with("\u{f10a}") +#let fa-table-tennis-paddle-ball = fa-icon.with("\u{f45d}") +#let fa-ping-pong-paddle-ball = fa-icon.with("\u{f45d}") +#let fa-table-tennis = fa-icon.with("\u{f45d}") +#let fa-table-tree = fa-icon.with("\u{e293}") +#let fa-tablet-rugged = fa-icon.with("\u{f48f}") +#let fa-tablets = fa-icon.with("\u{f490}") +#let fa-tablet-screen = fa-icon.with("\u{f3fc}") +#let fa-tablet-android-alt = fa-icon.with("\u{f3fc}") +#let fa-tablet-screen-button = fa-icon.with("\u{f3fa}") +#let fa-tablet-alt = fa-icon.with("\u{f3fa}") +#let fa-tachograph-digital = fa-icon.with("\u{f566}") +#let fa-digital-tachograph = fa-icon.with("\u{f566}") +#let fa-taco = fa-icon.with("\u{f826}") +#let fa-tag = fa-icon.with("\u{f02b}") +#let fa-tags = fa-icon.with("\u{f02c}") +#let fa-tally = fa-icon.with("\u{f69c}") +#let fa-tally-5 = fa-icon.with("\u{f69c}") +#let fa-tally-1 = fa-icon.with("\u{e294}") +#let fa-tally-2 = fa-icon.with("\u{e295}") +#let fa-tally-3 = fa-icon.with("\u{e296}") +#let fa-tally-4 = fa-icon.with("\u{e297}") +#let fa-tamale = fa-icon.with("\u{e451}") +#let fa-tank-water = fa-icon.with("\u{e452}") +#let fa-tape = fa-icon.with("\u{f4db}") +#let fa-tarp = fa-icon.with("\u{e57b}") +#let fa-tarp-droplet = fa-icon.with("\u{e57c}") +#let fa-taxi = fa-icon.with("\u{f1ba}") +#let fa-cab = fa-icon.with("\u{f1ba}") +#let fa-taxi-bus = fa-icon.with("\u{e298}") +#let fa-teamspeak = fa-icon.with("\u{f4f9}") +#let fa-teddy-bear = fa-icon.with("\u{e3cf}") +#let fa-teeth = fa-icon.with("\u{f62e}") +#let fa-teeth-open = fa-icon.with("\u{f62f}") +#let fa-telegram = fa-icon.with("\u{f2c6}") +#let fa-telegram-plane = fa-icon.with("\u{f2c6}") +#let fa-telescope = fa-icon.with("\u{e03e}") +#let fa-temperature-arrow-down = fa-icon.with("\u{e03f}") +#let fa-temperature-down = fa-icon.with("\u{e03f}") +#let fa-temperature-arrow-up = fa-icon.with("\u{e040}") +#let fa-temperature-up = fa-icon.with("\u{e040}") +#let fa-temperature-empty = fa-icon.with("\u{f2cb}") +#let fa-temperature-0 = fa-icon.with("\u{f2cb}") +#let fa-thermometer-0 = fa-icon.with("\u{f2cb}") +#let fa-thermometer-empty = fa-icon.with("\u{f2cb}") +#let fa-temperature-full = fa-icon.with("\u{f2c7}") +#let fa-temperature-4 = fa-icon.with("\u{f2c7}") +#let fa-thermometer-4 = fa-icon.with("\u{f2c7}") +#let fa-thermometer-full = fa-icon.with("\u{f2c7}") +#let fa-temperature-half = fa-icon.with("\u{f2c9}") +#let fa-temperature-2 = fa-icon.with("\u{f2c9}") +#let fa-thermometer-2 = fa-icon.with("\u{f2c9}") +#let fa-thermometer-half = fa-icon.with("\u{f2c9}") +#let fa-temperature-high = fa-icon.with("\u{f769}") +#let fa-temperature-list = fa-icon.with("\u{e299}") +#let fa-temperature-low = fa-icon.with("\u{f76b}") +#let fa-temperature-quarter = fa-icon.with("\u{f2ca}") +#let fa-temperature-1 = fa-icon.with("\u{f2ca}") +#let fa-thermometer-1 = fa-icon.with("\u{f2ca}") +#let fa-thermometer-quarter = fa-icon.with("\u{f2ca}") +#let fa-temperature-snow = fa-icon.with("\u{f768}") +#let fa-temperature-frigid = fa-icon.with("\u{f768}") +#let fa-temperature-sun = fa-icon.with("\u{f76a}") +#let fa-temperature-hot = fa-icon.with("\u{f76a}") +#let fa-temperature-three-quarters = fa-icon.with("\u{f2c8}") +#let fa-temperature-3 = fa-icon.with("\u{f2c8}") +#let fa-thermometer-3 = fa-icon.with("\u{f2c8}") +#let fa-thermometer-three-quarters = fa-icon.with("\u{f2c8}") +#let fa-tencent-weibo = fa-icon.with("\u{f1d5}") +#let fa-tenge-sign = fa-icon.with("\u{f7d7}") +#let fa-tenge = fa-icon.with("\u{f7d7}") +#let fa-tennis-ball = fa-icon.with("\u{f45e}") +#let fa-tent = fa-icon.with("\u{e57d}") +#let fa-tent-arrow-down-to-line = fa-icon.with("\u{e57e}") +#let fa-tent-arrow-left-right = fa-icon.with("\u{e57f}") +#let fa-tent-arrows-down = fa-icon.with("\u{e581}") +#let fa-tent-arrow-turn-left = fa-icon.with("\u{e580}") +#let fa-tent-double-peak = fa-icon.with("\u{e627}") +#let fa-tents = fa-icon.with("\u{e582}") +#let fa-terminal = fa-icon.with("\u{f120}") +#let fa-text = fa-icon.with("\u{f893}") +#let fa-text-height = fa-icon.with("\u{f034}") +#let fa-text-size = fa-icon.with("\u{f894}") +#let fa-text-slash = fa-icon.with("\u{f87d}") +#let fa-remove-format = fa-icon.with("\u{f87d}") +#let fa-text-width = fa-icon.with("\u{f035}") +#let fa-themeco = fa-icon.with("\u{f5c6}") +#let fa-themeisle = fa-icon.with("\u{f2b2}") +#let fa-the-red-yeti = fa-icon.with("\u{f69d}") +#let fa-thermometer = fa-icon.with("\u{f491}") +#let fa-theta = fa-icon.with("\u{f69e}") +#let fa-think-peaks = fa-icon.with("\u{f731}") +#let fa-thought-bubble = fa-icon.with("\u{e32e}") +#let fa-threads = fa-icon.with("\u{e618}") +#let fa-thumbs-down = fa-icon.with("\u{f165}") +#let fa-thumbs-up = fa-icon.with("\u{f164}") +#let fa-thumbtack = fa-icon.with("\u{f08d}") +#let fa-thumb-tack = fa-icon.with("\u{f08d}") +#let fa-thumbtack-slash = fa-icon.with("\u{e68f}") +#let fa-thumb-tack-slash = fa-icon.with("\u{e68f}") +#let fa-tick = fa-icon.with("\u{e32f}") +#let fa-ticket = fa-icon.with("\u{f145}") +#let fa-ticket-airline = fa-icon.with("\u{e29a}") +#let fa-ticket-perforated-plane = fa-icon.with("\u{e29a}") +#let fa-ticket-plane = fa-icon.with("\u{e29a}") +#let fa-ticket-perforated = fa-icon.with("\u{e63e}") +#let fa-tickets = fa-icon.with("\u{e658}") +#let fa-tickets-airline = fa-icon.with("\u{e29b}") +#let fa-tickets-perforated-plane = fa-icon.with("\u{e29b}") +#let fa-tickets-plane = fa-icon.with("\u{e29b}") +#let fa-ticket-simple = fa-icon.with("\u{f3ff}") +#let fa-ticket-alt = fa-icon.with("\u{f3ff}") +#let fa-tickets-perforated = fa-icon.with("\u{e63f}") +#let fa-tickets-simple = fa-icon.with("\u{e659}") +#let fa-tiktok = fa-icon.with("\u{e07b}") +#let fa-tilde = fa-icon.with("\u{7e}") +#let fa-timeline = fa-icon.with("\u{e29c}") +#let fa-timeline-arrow = fa-icon.with("\u{e29d}") +#let fa-timer = fa-icon.with("\u{e29e}") +#let fa-tire = fa-icon.with("\u{f631}") +#let fa-tire-flat = fa-icon.with("\u{f632}") +#let fa-tire-pressure-warning = fa-icon.with("\u{f633}") +#let fa-tire-rugged = fa-icon.with("\u{f634}") +#let fa-toggle-large-off = fa-icon.with("\u{e5b0}") +#let fa-toggle-large-on = fa-icon.with("\u{e5b1}") +#let fa-toggle-off = fa-icon.with("\u{f204}") +#let fa-toggle-on = fa-icon.with("\u{f205}") +#let fa-toilet = fa-icon.with("\u{f7d8}") +#let fa-toilet-paper = fa-icon.with("\u{f71e}") +#let fa-toilet-paper-blank = fa-icon.with("\u{f71f}") +#let fa-toilet-paper-alt = fa-icon.with("\u{f71f}") +#let fa-toilet-paper-blank-under = fa-icon.with("\u{e29f}") +#let fa-toilet-paper-reverse-alt = fa-icon.with("\u{e29f}") +#let fa-toilet-paper-check = fa-icon.with("\u{e5b2}") +#let fa-toilet-paper-slash = fa-icon.with("\u{e072}") +#let fa-toilet-paper-under = fa-icon.with("\u{e2a0}") +#let fa-toilet-paper-reverse = fa-icon.with("\u{e2a0}") +#let fa-toilet-paper-under-slash = fa-icon.with("\u{e2a1}") +#let fa-toilet-paper-reverse-slash = fa-icon.with("\u{e2a1}") +#let fa-toilet-paper-xmark = fa-icon.with("\u{e5b3}") +#let fa-toilet-portable = fa-icon.with("\u{e583}") +#let fa-toilets-portable = fa-icon.with("\u{e584}") +#let fa-tomato = fa-icon.with("\u{e330}") +#let fa-tombstone = fa-icon.with("\u{f720}") +#let fa-tombstone-blank = fa-icon.with("\u{f721}") +#let fa-tombstone-alt = fa-icon.with("\u{f721}") +#let fa-toolbox = fa-icon.with("\u{f552}") +#let fa-tooth = fa-icon.with("\u{f5c9}") +#let fa-toothbrush = fa-icon.with("\u{f635}") +#let fa-torii-gate = fa-icon.with("\u{f6a1}") +#let fa-tornado = fa-icon.with("\u{f76f}") +#let fa-tower-broadcast = fa-icon.with("\u{f519}") +#let fa-broadcast-tower = fa-icon.with("\u{f519}") +#let fa-tower-cell = fa-icon.with("\u{e585}") +#let fa-tower-control = fa-icon.with("\u{e2a2}") +#let fa-tower-observation = fa-icon.with("\u{e586}") +#let fa-tractor = fa-icon.with("\u{f722}") +#let fa-trade-federation = fa-icon.with("\u{f513}") +#let fa-trademark = fa-icon.with("\u{f25c}") +#let fa-traffic-cone = fa-icon.with("\u{f636}") +#let fa-traffic-light = fa-icon.with("\u{f637}") +#let fa-traffic-light-go = fa-icon.with("\u{f638}") +#let fa-traffic-light-slow = fa-icon.with("\u{f639}") +#let fa-traffic-light-stop = fa-icon.with("\u{f63a}") +#let fa-trailer = fa-icon.with("\u{e041}") +#let fa-train = fa-icon.with("\u{f238}") +#let fa-train-subway = fa-icon.with("\u{f239}") +#let fa-subway = fa-icon.with("\u{f239}") +#let fa-train-subway-tunnel = fa-icon.with("\u{e2a3}") +#let fa-subway-tunnel = fa-icon.with("\u{e2a3}") +#let fa-train-track = fa-icon.with("\u{e453}") +#let fa-train-tram = fa-icon.with("\u{e5b4}") +#let fa-train-tunnel = fa-icon.with("\u{e454}") +#let fa-transformer-bolt = fa-icon.with("\u{e2a4}") +#let fa-transgender = fa-icon.with("\u{f225}") +#let fa-transgender-alt = fa-icon.with("\u{f225}") +#let fa-transporter = fa-icon.with("\u{e042}") +#let fa-transporter-1 = fa-icon.with("\u{e043}") +#let fa-transporter-2 = fa-icon.with("\u{e044}") +#let fa-transporter-3 = fa-icon.with("\u{e045}") +#let fa-transporter-4 = fa-icon.with("\u{e2a5}") +#let fa-transporter-5 = fa-icon.with("\u{e2a6}") +#let fa-transporter-6 = fa-icon.with("\u{e2a7}") +#let fa-transporter-7 = fa-icon.with("\u{e2a8}") +#let fa-transporter-empty = fa-icon.with("\u{e046}") +#let fa-trash = fa-icon.with("\u{f1f8}") +#let fa-trash-arrow-up = fa-icon.with("\u{f829}") +#let fa-trash-restore = fa-icon.with("\u{f829}") +#let fa-trash-can = fa-icon.with("\u{f2ed}") +#let fa-trash-alt = fa-icon.with("\u{f2ed}") +#let fa-trash-can-arrow-up = fa-icon.with("\u{f82a}") +#let fa-trash-restore-alt = fa-icon.with("\u{f82a}") +#let fa-trash-can-check = fa-icon.with("\u{e2a9}") +#let fa-trash-can-clock = fa-icon.with("\u{e2aa}") +#let fa-trash-can-list = fa-icon.with("\u{e2ab}") +#let fa-trash-can-plus = fa-icon.with("\u{e2ac}") +#let fa-trash-can-slash = fa-icon.with("\u{e2ad}") +#let fa-trash-alt-slash = fa-icon.with("\u{e2ad}") +#let fa-trash-can-undo = fa-icon.with("\u{f896}") +#let fa-trash-can-arrow-turn-left = fa-icon.with("\u{f896}") +#let fa-trash-undo-alt = fa-icon.with("\u{f896}") +#let fa-trash-can-xmark = fa-icon.with("\u{e2ae}") +#let fa-trash-check = fa-icon.with("\u{e2af}") +#let fa-trash-clock = fa-icon.with("\u{e2b0}") +#let fa-trash-list = fa-icon.with("\u{e2b1}") +#let fa-trash-plus = fa-icon.with("\u{e2b2}") +#let fa-trash-slash = fa-icon.with("\u{e2b3}") +#let fa-trash-undo = fa-icon.with("\u{f895}") +#let fa-trash-arrow-turn-left = fa-icon.with("\u{f895}") +#let fa-trash-xmark = fa-icon.with("\u{e2b4}") +#let fa-treasure-chest = fa-icon.with("\u{f723}") +#let fa-tree = fa-icon.with("\u{f1bb}") +#let fa-tree-christmas = fa-icon.with("\u{f7db}") +#let fa-tree-city = fa-icon.with("\u{e587}") +#let fa-tree-deciduous = fa-icon.with("\u{f400}") +#let fa-tree-alt = fa-icon.with("\u{f400}") +#let fa-tree-decorated = fa-icon.with("\u{f7dc}") +#let fa-tree-large = fa-icon.with("\u{f7dd}") +#let fa-tree-palm = fa-icon.with("\u{f82b}") +#let fa-trees = fa-icon.with("\u{f724}") +#let fa-trello = fa-icon.with("\u{f181}") +#let fa-t-rex = fa-icon.with("\u{e629}") +#let fa-triangle = fa-icon.with("\u{f2ec}") +#let fa-triangle-exclamation = fa-icon.with("\u{f071}") +#let fa-exclamation-triangle = fa-icon.with("\u{f071}") +#let fa-warning = fa-icon.with("\u{f071}") +#let fa-triangle-instrument = fa-icon.with("\u{f8e2}") +#let fa-triangle-music = fa-icon.with("\u{f8e2}") +#let fa-triangle-person-digging = fa-icon.with("\u{f85d}") +#let fa-construction = fa-icon.with("\u{f85d}") +#let fa-tricycle = fa-icon.with("\u{e5c3}") +#let fa-tricycle-adult = fa-icon.with("\u{e5c4}") +#let fa-trillium = fa-icon.with("\u{e588}") +#let fa-trophy = fa-icon.with("\u{f091}") +#let fa-trophy-star = fa-icon.with("\u{f2eb}") +#let fa-trophy-alt = fa-icon.with("\u{f2eb}") +#let fa-trowel = fa-icon.with("\u{e589}") +#let fa-trowel-bricks = fa-icon.with("\u{e58a}") +#let fa-truck = fa-icon.with("\u{f0d1}") +#let fa-truck-arrow-right = fa-icon.with("\u{e58b}") +#let fa-truck-bolt = fa-icon.with("\u{e3d0}") +#let fa-truck-clock = fa-icon.with("\u{f48c}") +#let fa-shipping-timed = fa-icon.with("\u{f48c}") +#let fa-truck-container = fa-icon.with("\u{f4dc}") +#let fa-truck-container-empty = fa-icon.with("\u{e2b5}") +#let fa-truck-droplet = fa-icon.with("\u{e58c}") +#let fa-truck-fast = fa-icon.with("\u{f48b}") +#let fa-shipping-fast = fa-icon.with("\u{f48b}") +#let fa-truck-field = fa-icon.with("\u{e58d}") +#let fa-truck-field-un = fa-icon.with("\u{e58e}") +#let fa-truck-fire = fa-icon.with("\u{e65a}") +#let fa-truck-flatbed = fa-icon.with("\u{e2b6}") +#let fa-truck-front = fa-icon.with("\u{e2b7}") +#let fa-truck-ladder = fa-icon.with("\u{e657}") +#let fa-truck-medical = fa-icon.with("\u{f0f9}") +#let fa-ambulance = fa-icon.with("\u{f0f9}") +#let fa-truck-monster = fa-icon.with("\u{f63b}") +#let fa-truck-moving = fa-icon.with("\u{f4df}") +#let fa-truck-pickup = fa-icon.with("\u{f63c}") +#let fa-truck-plane = fa-icon.with("\u{e58f}") +#let fa-truck-plow = fa-icon.with("\u{f7de}") +#let fa-truck-ramp = fa-icon.with("\u{f4e0}") +#let fa-truck-ramp-box = fa-icon.with("\u{f4de}") +#let fa-truck-loading = fa-icon.with("\u{f4de}") +#let fa-truck-ramp-couch = fa-icon.with("\u{f4dd}") +#let fa-truck-couch = fa-icon.with("\u{f4dd}") +#let fa-truck-tow = fa-icon.with("\u{e2b8}") +#let fa-truck-utensils = fa-icon.with("\u{e628}") +#let fa-trumpet = fa-icon.with("\u{f8e3}") +#let fa-tty = fa-icon.with("\u{f1e4}") +#let fa-teletype = fa-icon.with("\u{f1e4}") +#let fa-tty-answer = fa-icon.with("\u{e2b9}") +#let fa-teletype-answer = fa-icon.with("\u{e2b9}") +#let fa-tugrik-sign = fa-icon.with("\u{e2ba}") +#let fa-tumblr = fa-icon.with("\u{f173}") +#let fa-turkey = fa-icon.with("\u{f725}") +#let fa-turkish-lira-sign = fa-icon.with("\u{e2bb}") +#let fa-try = fa-icon.with("\u{e2bb}") +#let fa-turkish-lira = fa-icon.with("\u{e2bb}") +#let fa-turn-down = fa-icon.with("\u{f3be}") +#let fa-level-down-alt = fa-icon.with("\u{f3be}") +#let fa-turn-down-left = fa-icon.with("\u{e331}") +#let fa-turn-down-right = fa-icon.with("\u{e455}") +#let fa-turn-left = fa-icon.with("\u{e636}") +#let fa-turn-left-down = fa-icon.with("\u{e637}") +#let fa-turn-left-up = fa-icon.with("\u{e638}") +#let fa-turn-right = fa-icon.with("\u{e639}") +#let fa-turntable = fa-icon.with("\u{f8e4}") +#let fa-turn-up = fa-icon.with("\u{f3bf}") +#let fa-level-up-alt = fa-icon.with("\u{f3bf}") +#let fa-turtle = fa-icon.with("\u{f726}") +#let fa-tv = fa-icon.with("\u{f26c}") +#let fa-television = fa-icon.with("\u{f26c}") +#let fa-tv-alt = fa-icon.with("\u{f26c}") +#let fa-tv-music = fa-icon.with("\u{f8e6}") +#let fa-tv-retro = fa-icon.with("\u{f401}") +#let fa-twitch = fa-icon.with("\u{f1e8}") +#let fa-twitter = fa-icon.with("\u{f099}") +#let fa-typewriter = fa-icon.with("\u{f8e7}") +#let fa-typo3 = fa-icon.with("\u{f42b}") +#let fa-u = fa-icon.with("\u{55}") +#let fa-uber = fa-icon.with("\u{f402}") +#let fa-ubuntu = fa-icon.with("\u{f7df}") +#let fa-ufo = fa-icon.with("\u{e047}") +#let fa-ufo-beam = fa-icon.with("\u{e048}") +#let fa-uikit = fa-icon.with("\u{f403}") +#let fa-umbraco = fa-icon.with("\u{f8e8}") +#let fa-umbrella = fa-icon.with("\u{f0e9}") +#let fa-umbrella-beach = fa-icon.with("\u{f5ca}") +#let fa-umbrella-simple = fa-icon.with("\u{e2bc}") +#let fa-umbrella-alt = fa-icon.with("\u{e2bc}") +#let fa-uncharted = fa-icon.with("\u{e084}") +#let fa-underline = fa-icon.with("\u{f0cd}") +#let fa-unicorn = fa-icon.with("\u{f727}") +#let fa-uniform-martial-arts = fa-icon.with("\u{e3d1}") +#let fa-union = fa-icon.with("\u{f6a2}") +#let fa-uniregistry = fa-icon.with("\u{f404}") +#let fa-unity = fa-icon.with("\u{e049}") +#let fa-universal-access = fa-icon.with("\u{f29a}") +#let fa-unlock = fa-icon.with("\u{f09c}") +#let fa-unlock-keyhole = fa-icon.with("\u{f13e}") +#let fa-unlock-alt = fa-icon.with("\u{f13e}") +#let fa-unsplash = fa-icon.with("\u{e07c}") +#let fa-untappd = fa-icon.with("\u{f405}") +#let fa-up = fa-icon.with("\u{f357}") +#let fa-arrow-alt-up = fa-icon.with("\u{f357}") +#let fa-up-down = fa-icon.with("\u{f338}") +#let fa-arrows-alt-v = fa-icon.with("\u{f338}") +#let fa-up-down-left-right = fa-icon.with("\u{f0b2}") +#let fa-arrows-alt = fa-icon.with("\u{f0b2}") +#let fa-up-from-bracket = fa-icon.with("\u{e590}") +#let fa-up-from-dotted-line = fa-icon.with("\u{e456}") +#let fa-up-from-line = fa-icon.with("\u{f346}") +#let fa-arrow-alt-from-bottom = fa-icon.with("\u{f346}") +#let fa-up-left = fa-icon.with("\u{e2bd}") +#let fa-upload = fa-icon.with("\u{f093}") +#let fa-up-long = fa-icon.with("\u{f30c}") +#let fa-long-arrow-alt-up = fa-icon.with("\u{f30c}") +#let fa-up-right = fa-icon.with("\u{e2be}") +#let fa-up-right-and-down-left-from-center = fa-icon.with("\u{f424}") +#let fa-expand-alt = fa-icon.with("\u{f424}") +#let fa-up-right-from-square = fa-icon.with("\u{f35d}") +#let fa-external-link-alt = fa-icon.with("\u{f35d}") +#let fa-ups = fa-icon.with("\u{f7e0}") +#let fa-up-to-bracket = fa-icon.with("\u{e66e}") +#let fa-up-to-dotted-line = fa-icon.with("\u{e457}") +#let fa-up-to-line = fa-icon.with("\u{f34d}") +#let fa-arrow-alt-to-top = fa-icon.with("\u{f34d}") +#let fa-upwork = fa-icon.with("\u{e641}") +#let fa-usb = fa-icon.with("\u{f287}") +#let fa-usb-drive = fa-icon.with("\u{f8e9}") +#let fa-user = fa-icon.with("\u{f007}") +#let fa-user-alien = fa-icon.with("\u{e04a}") +#let fa-user-astronaut = fa-icon.with("\u{f4fb}") +#let fa-user-beard-bolt = fa-icon.with("\u{e689}") +#let fa-user-bounty-hunter = fa-icon.with("\u{e2bf}") +#let fa-user-check = fa-icon.with("\u{f4fc}") +#let fa-user-chef = fa-icon.with("\u{e3d2}") +#let fa-user-clock = fa-icon.with("\u{f4fd}") +#let fa-user-cowboy = fa-icon.with("\u{f8ea}") +#let fa-user-crown = fa-icon.with("\u{f6a4}") +#let fa-user-doctor = fa-icon.with("\u{f0f0}") +#let fa-user-md = fa-icon.with("\u{f0f0}") +#let fa-user-doctor-hair = fa-icon.with("\u{e458}") +#let fa-user-doctor-hair-long = fa-icon.with("\u{e459}") +#let fa-user-doctor-message = fa-icon.with("\u{f82e}") +#let fa-user-md-chat = fa-icon.with("\u{f82e}") +#let fa-user-gear = fa-icon.with("\u{f4fe}") +#let fa-user-cog = fa-icon.with("\u{f4fe}") +#let fa-user-graduate = fa-icon.with("\u{f501}") +#let fa-user-group = fa-icon.with("\u{f500}") +#let fa-user-friends = fa-icon.with("\u{f500}") +#let fa-user-group-crown = fa-icon.with("\u{f6a5}") +#let fa-users-crown = fa-icon.with("\u{f6a5}") +#let fa-user-group-simple = fa-icon.with("\u{e603}") +#let fa-user-hair = fa-icon.with("\u{e45a}") +#let fa-user-hair-buns = fa-icon.with("\u{e3d3}") +#let fa-user-hair-long = fa-icon.with("\u{e45b}") +#let fa-user-hair-mullet = fa-icon.with("\u{e45c}") +#let fa-business-front = fa-icon.with("\u{e45c}") +#let fa-party-back = fa-icon.with("\u{e45c}") +#let fa-trian-balbot = fa-icon.with("\u{e45c}") +#let fa-user-headset = fa-icon.with("\u{f82d}") +#let fa-user-helmet-safety = fa-icon.with("\u{f82c}") +#let fa-user-construction = fa-icon.with("\u{f82c}") +#let fa-user-hard-hat = fa-icon.with("\u{f82c}") +#let fa-user-hoodie = fa-icon.with("\u{e68a}") +#let fa-user-injured = fa-icon.with("\u{f728}") +#let fa-user-large = fa-icon.with("\u{f406}") +#let fa-user-alt = fa-icon.with("\u{f406}") +#let fa-user-large-slash = fa-icon.with("\u{f4fa}") +#let fa-user-alt-slash = fa-icon.with("\u{f4fa}") +#let fa-user-lock = fa-icon.with("\u{f502}") +#let fa-user-magnifying-glass = fa-icon.with("\u{e5c5}") +#let fa-user-minus = fa-icon.with("\u{f503}") +#let fa-user-music = fa-icon.with("\u{f8eb}") +#let fa-user-ninja = fa-icon.with("\u{f504}") +#let fa-user-nurse = fa-icon.with("\u{f82f}") +#let fa-user-nurse-hair = fa-icon.with("\u{e45d}") +#let fa-user-nurse-hair-long = fa-icon.with("\u{e45e}") +#let fa-user-pen = fa-icon.with("\u{f4ff}") +#let fa-user-edit = fa-icon.with("\u{f4ff}") +#let fa-user-pilot = fa-icon.with("\u{e2c0}") +#let fa-user-pilot-tie = fa-icon.with("\u{e2c1}") +#let fa-user-plus = fa-icon.with("\u{f234}") +#let fa-user-police = fa-icon.with("\u{e333}") +#let fa-user-police-tie = fa-icon.with("\u{e334}") +#let fa-user-robot = fa-icon.with("\u{e04b}") +#let fa-user-robot-xmarks = fa-icon.with("\u{e4a7}") +#let fa-users = fa-icon.with("\u{f0c0}") +#let fa-users-between-lines = fa-icon.with("\u{e591}") +#let fa-user-secret = fa-icon.with("\u{f21b}") +#let fa-users-gear = fa-icon.with("\u{f509}") +#let fa-users-cog = fa-icon.with("\u{f509}") +#let fa-user-shakespeare = fa-icon.with("\u{e2c2}") +#let fa-user-shield = fa-icon.with("\u{f505}") +#let fa-user-slash = fa-icon.with("\u{f506}") +#let fa-users-line = fa-icon.with("\u{e592}") +#let fa-users-medical = fa-icon.with("\u{f830}") +#let fa-users-rays = fa-icon.with("\u{e593}") +#let fa-users-rectangle = fa-icon.with("\u{e594}") +#let fa-users-slash = fa-icon.with("\u{e073}") +#let fa-users-viewfinder = fa-icon.with("\u{e595}") +#let fa-user-tag = fa-icon.with("\u{f507}") +#let fa-user-tie = fa-icon.with("\u{f508}") +#let fa-user-tie-hair = fa-icon.with("\u{e45f}") +#let fa-user-tie-hair-long = fa-icon.with("\u{e460}") +#let fa-user-unlock = fa-icon.with("\u{e058}") +#let fa-user-visor = fa-icon.with("\u{e04c}") +#let fa-user-vneck = fa-icon.with("\u{e461}") +#let fa-user-vneck-hair = fa-icon.with("\u{e462}") +#let fa-user-vneck-hair-long = fa-icon.with("\u{e463}") +#let fa-user-xmark = fa-icon.with("\u{f235}") +#let fa-user-times = fa-icon.with("\u{f235}") +#let fa-usps = fa-icon.with("\u{f7e1}") +#let fa-ussunnah = fa-icon.with("\u{f407}") +#let fa-utensils = fa-icon.with("\u{f2e7}") +#let fa-cutlery = fa-icon.with("\u{f2e7}") +#let fa-utensils-slash = fa-icon.with("\u{e464}") +#let fa-utility-pole = fa-icon.with("\u{e2c3}") +#let fa-utility-pole-double = fa-icon.with("\u{e2c4}") +#let fa-v = fa-icon.with("\u{56}") +#let fa-vaadin = fa-icon.with("\u{f408}") +#let fa-vacuum = fa-icon.with("\u{e04d}") +#let fa-vacuum-robot = fa-icon.with("\u{e04e}") +#let fa-value-absolute = fa-icon.with("\u{f6a6}") +#let fa-van-shuttle = fa-icon.with("\u{f5b6}") +#let fa-shuttle-van = fa-icon.with("\u{f5b6}") +#let fa-vault = fa-icon.with("\u{e2c5}") +#let fa-vector-circle = fa-icon.with("\u{e2c6}") +#let fa-vector-polygon = fa-icon.with("\u{e2c7}") +#let fa-vector-square = fa-icon.with("\u{f5cb}") +#let fa-vent-damper = fa-icon.with("\u{e465}") +#let fa-venus = fa-icon.with("\u{f221}") +#let fa-venus-double = fa-icon.with("\u{f226}") +#let fa-venus-mars = fa-icon.with("\u{f228}") +#let fa-vest = fa-icon.with("\u{e085}") +#let fa-vest-patches = fa-icon.with("\u{e086}") +#let fa-viacoin = fa-icon.with("\u{f237}") +#let fa-viadeo = fa-icon.with("\u{f2a9}") +#let fa-vial = fa-icon.with("\u{f492}") +#let fa-vial-circle-check = fa-icon.with("\u{e596}") +#let fa-vials = fa-icon.with("\u{f493}") +#let fa-vial-virus = fa-icon.with("\u{e597}") +#let fa-viber = fa-icon.with("\u{f409}") +#let fa-video = fa-icon.with("\u{f03d}") +#let fa-video-camera = fa-icon.with("\u{f03d}") +#let fa-video-arrow-down-left = fa-icon.with("\u{e2c8}") +#let fa-video-arrow-up-right = fa-icon.with("\u{e2c9}") +#let fa-video-plus = fa-icon.with("\u{f4e1}") +#let fa-video-slash = fa-icon.with("\u{f4e2}") +#let fa-vihara = fa-icon.with("\u{f6a7}") +#let fa-vimeo = fa-icon.with("\u{f40a}") +#let fa-vimeo-v = fa-icon.with("\u{f27d}") +#let fa-vine = fa-icon.with("\u{f1ca}") +#let fa-violin = fa-icon.with("\u{f8ed}") +#let fa-virus = fa-icon.with("\u{e074}") +#let fa-virus-covid = fa-icon.with("\u{e4a8}") +#let fa-virus-covid-slash = fa-icon.with("\u{e4a9}") +#let fa-viruses = fa-icon.with("\u{e076}") +#let fa-virus-slash = fa-icon.with("\u{e075}") +#let fa-vk = fa-icon.with("\u{f189}") +#let fa-vnv = fa-icon.with("\u{f40b}") +#let fa-voicemail = fa-icon.with("\u{f897}") +#let fa-volcano = fa-icon.with("\u{f770}") +#let fa-volleyball = fa-icon.with("\u{f45f}") +#let fa-volleyball-ball = fa-icon.with("\u{f45f}") +#let fa-volume = fa-icon.with("\u{f6a8}") +#let fa-volume-medium = fa-icon.with("\u{f6a8}") +#let fa-volume-high = fa-icon.with("\u{f028}") +#let fa-volume-up = fa-icon.with("\u{f028}") +#let fa-volume-low = fa-icon.with("\u{f027}") +#let fa-volume-down = fa-icon.with("\u{f027}") +#let fa-volume-off = fa-icon.with("\u{f026}") +#let fa-volume-slash = fa-icon.with("\u{f2e2}") +#let fa-volume-xmark = fa-icon.with("\u{f6a9}") +#let fa-volume-mute = fa-icon.with("\u{f6a9}") +#let fa-volume-times = fa-icon.with("\u{f6a9}") +#let fa-vr-cardboard = fa-icon.with("\u{f729}") +#let fa-vuejs = fa-icon.with("\u{f41f}") +#let fa-w = fa-icon.with("\u{57}") +#let fa-waffle = fa-icon.with("\u{e466}") +#let fa-wagon-covered = fa-icon.with("\u{f8ee}") +#let fa-walker = fa-icon.with("\u{f831}") +#let fa-walkie-talkie = fa-icon.with("\u{f8ef}") +#let fa-wallet = fa-icon.with("\u{f555}") +#let fa-wand = fa-icon.with("\u{f72a}") +#let fa-wand-magic = fa-icon.with("\u{f0d0}") +#let fa-magic = fa-icon.with("\u{f0d0}") +#let fa-wand-magic-sparkles = fa-icon.with("\u{e2ca}") +#let fa-magic-wand-sparkles = fa-icon.with("\u{e2ca}") +#let fa-wand-sparkles = fa-icon.with("\u{f72b}") +#let fa-warehouse = fa-icon.with("\u{f494}") +#let fa-warehouse-full = fa-icon.with("\u{f495}") +#let fa-warehouse-alt = fa-icon.with("\u{f495}") +#let fa-washing-machine = fa-icon.with("\u{f898}") +#let fa-washer = fa-icon.with("\u{f898}") +#let fa-watch = fa-icon.with("\u{f2e1}") +#let fa-watch-apple = fa-icon.with("\u{e2cb}") +#let fa-watch-calculator = fa-icon.with("\u{f8f0}") +#let fa-watch-fitness = fa-icon.with("\u{f63e}") +#let fa-watchman-monitoring = fa-icon.with("\u{e087}") +#let fa-watch-smart = fa-icon.with("\u{e2cc}") +#let fa-water = fa-icon.with("\u{f773}") +#let fa-water-arrow-down = fa-icon.with("\u{f774}") +#let fa-water-lower = fa-icon.with("\u{f774}") +#let fa-water-arrow-up = fa-icon.with("\u{f775}") +#let fa-water-rise = fa-icon.with("\u{f775}") +#let fa-water-ladder = fa-icon.with("\u{f5c5}") +#let fa-ladder-water = fa-icon.with("\u{f5c5}") +#let fa-swimming-pool = fa-icon.with("\u{f5c5}") +#let fa-watermelon-slice = fa-icon.with("\u{e337}") +#let fa-wave = fa-icon.with("\u{e65b}") +#let fa-waveform = fa-icon.with("\u{f8f1}") +#let fa-waveform-lines = fa-icon.with("\u{f8f2}") +#let fa-waveform-path = fa-icon.with("\u{f8f2}") +#let fa-wave-pulse = fa-icon.with("\u{f5f8}") +#let fa-heart-rate = fa-icon.with("\u{f5f8}") +#let fa-wave-sine = fa-icon.with("\u{f899}") +#let fa-wave-square = fa-icon.with("\u{f83e}") +#let fa-waves-sine = fa-icon.with("\u{e65d}") +#let fa-wave-triangle = fa-icon.with("\u{f89a}") +#let fa-waze = fa-icon.with("\u{f83f}") +#let fa-web-awesome = fa-icon.with("\u{e682}") +#let fa-webflow = fa-icon.with("\u{e65c}") +#let fa-webhook = fa-icon.with("\u{e5d5}") +#let fa-weebly = fa-icon.with("\u{f5cc}") +#let fa-weibo = fa-icon.with("\u{f18a}") +#let fa-weight-hanging = fa-icon.with("\u{f5cd}") +#let fa-weight-scale = fa-icon.with("\u{f496}") +#let fa-weight = fa-icon.with("\u{f496}") +#let fa-weixin = fa-icon.with("\u{f1d7}") +#let fa-whale = fa-icon.with("\u{f72c}") +#let fa-whatsapp = fa-icon.with("\u{f232}") +#let fa-wheat = fa-icon.with("\u{f72d}") +#let fa-wheat-awn = fa-icon.with("\u{e2cd}") +#let fa-wheat-alt = fa-icon.with("\u{e2cd}") +#let fa-wheat-awn-circle-exclamation = fa-icon.with("\u{e598}") +#let fa-wheat-awn-slash = fa-icon.with("\u{e338}") +#let fa-wheat-slash = fa-icon.with("\u{e339}") +#let fa-wheelchair = fa-icon.with("\u{f193}") +#let fa-wheelchair-move = fa-icon.with("\u{e2ce}") +#let fa-wheelchair-alt = fa-icon.with("\u{e2ce}") +#let fa-whiskey-glass = fa-icon.with("\u{f7a0}") +#let fa-glass-whiskey = fa-icon.with("\u{f7a0}") +#let fa-whiskey-glass-ice = fa-icon.with("\u{f7a1}") +#let fa-glass-whiskey-rocks = fa-icon.with("\u{f7a1}") +#let fa-whistle = fa-icon.with("\u{f460}") +#let fa-whmcs = fa-icon.with("\u{f40d}") +#let fa-wifi = fa-icon.with("\u{f1eb}") +#let fa-wifi-3 = fa-icon.with("\u{f1eb}") +#let fa-wifi-strong = fa-icon.with("\u{f1eb}") +#let fa-wifi-exclamation = fa-icon.with("\u{e2cf}") +#let fa-wifi-fair = fa-icon.with("\u{f6ab}") +#let fa-wifi-2 = fa-icon.with("\u{f6ab}") +#let fa-wifi-slash = fa-icon.with("\u{f6ac}") +#let fa-wifi-weak = fa-icon.with("\u{f6aa}") +#let fa-wifi-1 = fa-icon.with("\u{f6aa}") +#let fa-wikipedia-w = fa-icon.with("\u{f266}") +#let fa-wind = fa-icon.with("\u{f72e}") +#let fa-window = fa-icon.with("\u{f40e}") +#let fa-window-flip = fa-icon.with("\u{f40f}") +#let fa-window-alt = fa-icon.with("\u{f40f}") +#let fa-window-frame = fa-icon.with("\u{e04f}") +#let fa-window-frame-open = fa-icon.with("\u{e050}") +#let fa-window-maximize = fa-icon.with("\u{f2d0}") +#let fa-window-minimize = fa-icon.with("\u{f2d1}") +#let fa-window-restore = fa-icon.with("\u{f2d2}") +#let fa-windows = fa-icon.with("\u{f17a}") +#let fa-windsock = fa-icon.with("\u{f777}") +#let fa-wind-turbine = fa-icon.with("\u{f89b}") +#let fa-wind-warning = fa-icon.with("\u{f776}") +#let fa-wind-circle-exclamation = fa-icon.with("\u{f776}") +#let fa-wine-bottle = fa-icon.with("\u{f72f}") +#let fa-wine-glass = fa-icon.with("\u{f4e3}") +#let fa-wine-glass-crack = fa-icon.with("\u{f4bb}") +#let fa-fragile = fa-icon.with("\u{f4bb}") +#let fa-wine-glass-empty = fa-icon.with("\u{f5ce}") +#let fa-wine-glass-alt = fa-icon.with("\u{f5ce}") +#let fa-wirsindhandwerk = fa-icon.with("\u{e2d0}") +#let fa-wsh = fa-icon.with("\u{e2d0}") +#let fa-wix = fa-icon.with("\u{f5cf}") +#let fa-wizards-of-the-coast = fa-icon.with("\u{f730}") +#let fa-wodu = fa-icon.with("\u{e088}") +#let fa-wolf-pack-battalion = fa-icon.with("\u{f514}") +#let fa-won-sign = fa-icon.with("\u{f159}") +#let fa-krw = fa-icon.with("\u{f159}") +#let fa-won = fa-icon.with("\u{f159}") +#let fa-wordpress = fa-icon.with("\u{f19a}") +#let fa-wordpress-simple = fa-icon.with("\u{f411}") +#let fa-worm = fa-icon.with("\u{e599}") +#let fa-wpbeginner = fa-icon.with("\u{f297}") +#let fa-wpexplorer = fa-icon.with("\u{f2de}") +#let fa-wpforms = fa-icon.with("\u{f298}") +#let fa-wpressr = fa-icon.with("\u{f3e4}") +#let fa-rendact = fa-icon.with("\u{f3e4}") +#let fa-wreath = fa-icon.with("\u{f7e2}") +#let fa-wreath-laurel = fa-icon.with("\u{e5d2}") +#let fa-wrench = fa-icon.with("\u{f0ad}") +#let fa-wrench-simple = fa-icon.with("\u{e2d1}") +#let fa-x = fa-icon.with("\u{58}") +#let fa-xbox = fa-icon.with("\u{f412}") +#let fa-xing = fa-icon.with("\u{f168}") +#let fa-xmark = fa-icon.with("\u{f00d}") +#let fa-close = fa-icon.with("\u{f00d}") +#let fa-multiply = fa-icon.with("\u{f00d}") +#let fa-remove = fa-icon.with("\u{f00d}") +#let fa-times = fa-icon.with("\u{f00d}") +#let fa-xmark-large = fa-icon.with("\u{e59b}") +#let fa-xmarks-lines = fa-icon.with("\u{e59a}") +#let fa-xmark-to-slot = fa-icon.with("\u{f771}") +#let fa-times-to-slot = fa-icon.with("\u{f771}") +#let fa-vote-nay = fa-icon.with("\u{f771}") +#let fa-x-ray = fa-icon.with("\u{f497}") +#let fa-x-twitter = fa-icon.with("\u{e61b}") +#let fa-y = fa-icon.with("\u{59}") +#let fa-yahoo = fa-icon.with("\u{f19e}") +#let fa-yammer = fa-icon.with("\u{f840}") +#let fa-yandex = fa-icon.with("\u{f413}") +#let fa-yandex-international = fa-icon.with("\u{f414}") +#let fa-yarn = fa-icon.with("\u{f7e3}") +#let fa-y-combinator = fa-icon.with("\u{f23b}") +#let fa-yelp = fa-icon.with("\u{f1e9}") +#let fa-yen-sign = fa-icon.with("\u{f157}") +#let fa-cny = fa-icon.with("\u{f157}") +#let fa-jpy = fa-icon.with("\u{f157}") +#let fa-rmb = fa-icon.with("\u{f157}") +#let fa-yen = fa-icon.with("\u{f157}") +#let fa-yin-yang = fa-icon.with("\u{f6ad}") +#let fa-yoast = fa-icon.with("\u{f2b1}") +#let fa-youtube = fa-icon.with("\u{f167}") +#let fa-z = fa-icon.with("\u{5a}") +#let fa-zhihu = fa-icon.with("\u{f63f}") diff --git a/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib-impl.typ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib-impl.typ new file mode 100644 index 00000000..c4ecba0b --- /dev/null +++ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib-impl.typ @@ -0,0 +1,61 @@ +// Currently, we assume there is no need to enable Pro sets for only a part of the document, +// so no method is provided to disable Pro sets +#let _fa_use_pro = state("_fa_use_pro", false) +#let fa-use-pro() = { + _fa_use_pro.update(true) +} + +/// Render a Font Awesome icon by its name or unicode +/// +/// Parameters: +/// - `name`: The name of the icon +/// - This can be name in string or unicode of the icon +/// - `solid`: Whether to use the solid version of the icon +/// - `fa-icon-map`: The map of icon names to unicode +/// - Default is a map generated from FontAwesome metadata +/// - *Not recommended* You can provide your own map to override it +/// - `..args`: Additional arguments to pass to the `text` function +/// +/// Returns: The rendered icon as a `text` element +#let fa-icon( + name, + solid: false, + fa-icon-map: (:), + ..args, +) = ( + context { + let default_fonts = ( + "Font Awesome 6 Free" + if solid { + " Solid" + }, + "Font Awesome 6 Brands", + ) + + if _fa_use_pro.get() { + // TODO: Help needed to test following fonts + default_fonts += ( + "Font Awesome 6 Pro" + if solid { + " Solid" + }, + "Font Awesome 6 Duotone", + "Font Awesome 6 Sharp" + if solid { + " Solid" + }, + "Font Awesome 6 Sharp Duotone" + if solid { + " Solid" + }, + ) + } + + text( + font: default_fonts, // If you see warning here, please check whether the FA font is installed + + // TODO: We might need to check whether this is needed + weight: if solid { 900 } else { 400 }, + // If the name is in the map, use the unicode from the map + // If not, pass the name and let the ligature feature handle it + fa-icon-map.at(name, default: name), + ..args, + ) + } +) diff --git a/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib.typ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib.typ new file mode 100644 index 00000000..4ceec243 --- /dev/null +++ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/lib.typ @@ -0,0 +1,81 @@ +//! typst-fontawesome +//! +//! https://github.com/duskmoon314/typst-fontawesome + +// Implementation of `fa-icon` +#import "lib-impl.typ": fa-icon, fa-use-pro + +// Generated icons +#import "lib-gen.typ": * + +// Re-export the `fa-icon` function +// The following doc comment is needed for lsp to show the documentation + +/// Render a Font Awesome icon by its name or unicode +/// +/// Parameters: +/// - `name`: The name of the icon +/// - This can be name in string or unicode of the icon +/// - `solid`: Whether to use the solid version of the icon +/// - `fa-icon-map`: The map of icon names to unicode +/// - Default is a map generated from FontAwesome metadata +/// - *Not recommended* You can provide your own map to override it +/// - `..args`: Additional arguments to pass to the `text` function +/// +/// Returns: The rendered icon as a `text` element +#let fa-icon = fa-icon.with(fa-icon-map: fa-icon-map) + +/// Render multiple Font Awesome icons together +/// +/// Parameters: +/// - `icons`: The list of icons to render +/// - Multiple types are supported: +/// - `str`: The name of the icon, e.g. `"square"` +/// - `array`: A tuple of the name and additional arguments, e.g. `("chess-queen", (solid: true, fill: white))` +/// - `arguments`: Arguments to pass to the `fa-icon` function, e.g. `arguments("chess-queen", solid: true, fill: white)` +/// - `content`: Any other content you want to render, e.g. `fa-chess-queen(solid: true, fill: white)` +/// - `box-args`: Additional arguments to pass to the `box` function +/// - `grid-args`: Additional arguments to pass to the `grid` function +/// - `fa-icon-args`: Additional arguments to pass to all `fa-icon` function +#let fa-stack( + box-args: (:), + grid-args: (:), + fa-icon-args: (:), + ..icons, +) = ( + context { + let icons = icons.pos().map(icon => { + if type(icon) == str { + fa-icon(icon, ..fa-icon-args) + } else if type(icon) == array { + let (name, args) = icon + fa-icon(name, ..fa-icon-args, ..args) + } else if type(icon) == arguments { + fa-icon(..icon.pos(), ..fa-icon-args, ..icon.named()) + } else if type(icon) == content { + icon + } else { + panic("Unsupported content. Please submit an issue for your use case.") + } + }) + + // Get the maximum width of the icons + let max-width = calc.max( + ..icons.map(icon => { + measure(icon).width + }), + ) + + box( + ..box-args, + grid( + align: center + horizon, + columns: icons.len() * (max-width,), + column-gutter: -max-width, + rows: 1, + ..grid-args, + ..icons + ), + ) + } +) \ No newline at end of file diff --git a/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/typst.toml b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/typst.toml new file mode 100644 index 00000000..0917fae2 --- /dev/null +++ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land _Cover_Land_Use_2006/.quarto/typst/packages/preview/fontawesome/0.5.0/typst.toml @@ -0,0 +1,8 @@ +[package] +name = "fontawesome" +version = "0.5.0" +entrypoint = "lib.typ" +authors = ["duskmoon (Campbell He)
+
+
+
+
+
+
+
+
+
+| Product features: | +
| Digital thematic map. Thematic classes based on CORINE LC nomenclature and GUS Legend. |
+
| Input data sources | +
| Earth Observation (EO) data with 2.5 m spatial resolution multispectral or pan-sharpened (multispectral merged with panchromatic) data. Multispectral data includes near-infrared band. Topographic Maps at a scale of 1: 50 000 or larger. COTS navigation data for the road network (methodology applied will be defined). Areas of Interest for Urban Atlas Mapping are determined by DG Regional Policy. Sealing layer based on FTS specifications for degree of sealing for level 3 classes 1.1.1 and 1.1.2 and level 4 classes 1.1.2.1, 1.1.2.2, 1.1.2.3 and 1.1.2.4. All input data should be described by metadata according to the INSPIRE metadata profile specifications and guidelines. |
+
| Ancillary data optional for all classes | +
| COTS navigation data: points of interest, land use, land cover, water areas. Google Earth (only for interpretation, not for delineation). Local city maps. |
+
| Ancillary data required for certain classes | +
| Local zoning data (e.g. cadastral data). Field check (on-site visit). Very high resolution imagery (better than 1 m ground resolution, e.g. aerial photographs). |
+
| Geometric resolution (Scale) | +
| 1:10 000; MinMU = 0.25 ha | +
| Geographic projection / Reference system | +
| As per user request but uniform within project area. | +
| Positional accuracy | +
| ± 5 m | +
| Thematic accuracy (in %) | +
| Minimum overall accuracy for level 1 class 1 "Artificial surfaces”: 85%. Minimum overall accuracy (all classes): 80%. Methodology for quality control has to be performed according to RD[1]. The minimum overall accuracy for level 1 class 1 "Artificial surfaces" must include both omission and commission errors with other classes within the larger urban zone (LUZ). |
+
| Update frequency | +
| t.b.d. | +
| Base data topicality | +
| t.b.d | +
| Delivery format | +
| Topologically correct GIS file. Single part features. |
+
| Data type | +
| Vector | +
+
+```
+
+\* example provided of the number for UA class 1.1.2.1
+
+```{=html}
+GSELUA_yy
+ 11210*
+| GSEL | +→ | +for GSELand, UA for Urban Atlas | +
| yy | +→ | +year of production (e.g. 08 for 2008) | +
| + | CORINE Class(es) [Level I, No.] |
+ Level(s) provided |
+ MinMU | +Thematic Accuracy |
+ Positional Pixel Accuracy |
+
| M1.1 Urban Atlas | +1 | +I-IV | +0.25 ha | +>= 85% | +<± 5 m | +
| 2-5 | ++ | 1 ha | +>= 80% | +<± 5 m | +|
| Overall Accuracy | +>= 80% | ++ | |||
| GSELand M1.1 Urban Atlas | +|||
| Urban Atlas No. |
+ Vector Data Code GSELUA_yy |
+ Nomenclature | +Additional Information |
+
| 1 | ++ | Artificial surfaces | ++ |
| 1.1 | ++ | Urban Fabric | ++ |
| 1.1.1 | +11100 | +Continuous Urban Fabric (S.L. > 80%) | +FTS¹ required | +
| 1.1.2 | +11200 | +Discontinuous Urban Fabric (S.L. 10% - 80%) | ++ |
| 1.1.2.1 | +11210 | +Discontinuous Dense Urban Fabric (S.L. 50% - 80%) | +FTS required | +
| 1.1.2.2 | +11220 | +Discontinuous Medium Density Urban Fabric (S.L. 30% - 50%) | +FTS required | +
| 1.1.2.3 | +11230 | +Discontinuous Low Density Urban Fabric (S.L. 10% - 30%) | +FTS required | +
| 1.1.2.4 | +11240 | +Discontinuous Very Low Density Urban Fabric (S.L. < 10%) | +FTS required | +
| 1.1.3 | +11300 | +Isolated structures | ++ |
| 1.2 | ++ | Industrial, commercial, public, military, private and transport units | ++ |
| 1.2.1 | +12100 | +Industrial, commercial, public, military and private units | +zoning data / field check recommended | +
| 1.2.2 | +12200 | +Road and rail network and associated land | +COTS² navigation data required | +
| 1.2.2.1 | +12210 | +Fast transit roads and associated land | +COTS navigation data required | +
| 1.2.2.2 | +12220 | +Other roads and associated land | +COTS navigation data required | +
| 1.2.2.3 | +12230 | +Railways and associated land | +COTS navigation data required | +
| 1.2.3 | +12300 | +Port areas | +zoning data / field check recommended | +
| 1.2.4 | +12400 | +Airports | +zoning data / field check recommended | +
| 1.3 | ++ | Mine, dump and construction sites | ++ |
| 1.3.1 | +13100 | +Mineral extraction and dump sites | ++ |
| 1.3.3 | +13300 | +Construction sites | ++ |
| 1.3.4 | +13400 | +Land without current use | ++ |
| 1.4 | ++ | Artificial non-agricultural vegetated areas | ++ |
| 1.4.1 | +14100 | +Green urban areas | ++ |
| 1.4.2 | +14200 | +Sports and leisure facilities | ++ |
| 2 | +20000 | +Agricultural areas, semi-natural areas and wetlands | +1 ha MMU | +
| 3 | +30000 | +Forests | +1 ha MMU | +
| 5 | +50000 | +Water | +1 ha MMU | +
+1. FTS = EEA Fast Track Sealing Layer. The assignment of the sealing levels (i.e. classes 1.1.2.1 - 1.1.2.4) shall be carried out using this layer. The QA check will check only if the technical approach agreed with DG REGIO is kept, but will not assess the absolute accuracy of these classes.
+2. COTS - Commercial Off-The-Shelf
+
+```
+
+
+```{=html}
+| Ref. data | +Sat. -image |
+
| TK | +COTS navigation data |
+
| FRC | +Net2Class | +|||
| 1 | +2 | +3 | +4 | +|
| 0 | +Manual | +|||
| 1 | +Manual | +|||
| 2 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 3 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 4 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 5 | +Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+ Estimated buffer width |
+
| 6 and above | +Mapping decided on city by city basis | +|||
Land use | +| Geographic description | EU, EFTA and West Balkan countries plus United Kingdom and Turkey | +| Temporal description | 2006 / 2012 / 2018 | +| Purpose | Provide the baseline of land use/land cover data on Functional Urban Areas extracted from VHR and other available imagery (and combined with in-situ data) to allow the study of the characteristics of these areas in comparison with major urban areas in the EU and EFTA countries. | +| Minimum Mapping Unit | 0.25 ha in urban areas (level 1)
1 ha in rural areas (level 2-5) | +| Minimum mapping width | 10 m between two objects for distinct mapping | +| Nomenclature | As provided in the Chapter 4.4. table 3 (containing 27 classes) | +| Projection | ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) | +| Delivery formats | Shapefile (2006), EsriFileGDB (2012), GeoPackage (2018) | +| Metadata | INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 | +| Positional accuracy | According to geo-location accuracy of satellite imagery delivered by ESA | +| Overall classification accuracy | ≥ 85 % in urban classes (class 1)
≥ 80 % in rural classes (classes 2 to 5)
≥ 80 % overall accuracy | + +The table provided in Annex 5.3 gives a more detailed description of the product specifications. + + + +### ADDITIONAL INFORMATION LAYERS + +The Urban Atlas Mapping Guide is related to the main product and by default refers to the Land Use / Land Cover (LU/LC) database. However, two additional information layers are also available starting from the 2012 reference year. A short description including the detailed specifications is proposed in this section. + +The first one is called Street Tree Layer (STL), a separate layer from the Urban Atlas LU/LC map produced within the level 1 urban mask for each FUA (partial coverage for 2012 reference year). It includes contiguous rows or patches of trees along the streets. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 1: Detailed specifications of Urban Atlas STL product +::: + +| | | +|-------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------| +| Title | Street Tree Layer | +| Abstract | Very high-resolution tree cover dataset of inside nomenclature level 1 areas. | +| INSPIRE themes | Land cover | +| Geographic description | EU, EFTA and West Balkan countries plus United Kingdom and Turkey | +| Temporal description | 2012 (partial coverage) / 2018 | +| Purpose | Mapping of contiguous rows or patches of trees covering inside the nomenclature level 1 areas. | +| Minimum Mapping Unit | 500 m² | +| Minimum mapping width | 10 m | +| Nomenclature | Tree (STL=1) or Nodata (99) | +| Projection | ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) | +| Delivery formats | Shapefile (2012), GeoPackage (2018) | +| Metadata | INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 | +| Positional accuracy | According to geo-location accuracy of satellite imagery delivered by ESA | +| Overall classification accuracy | > 80 % in urban classes | + +The second one is a Digital Height Model (DHM) providing building bloc height information. This consists of a 10 m x 10 m resolution raster layer containing height information generated for urban areas of selected cities (currently the core area of capitals of all countries covered by the Urban Atlas). The building height information is derived from IRS-P5 stereo images of the reference year 2012. It contains only the heights of the building bloc itself (i.e. trees are masked out). As described in the terms of reference, the mean absolute error of the height must be below 3m. This is verified by comparing the measured values with reference values and described in a validation report. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 2: Detailed specifications of Urban Atlas DHM product +::: + +| | | +|-----------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------| +| Title | Digital Height Model | +| Abstract | Very High-Resolution layer (grid, building or block footprint) containing building height in the core urban area | +| INSPIRE themes | Buildings | +| Geographic description | EU, EFTA and West Balkan countries plus United Kingdom and Turkey - core urban areas of capital cities only | +| Temporal description | 2012 | +| Purpose | Provide very high-resolution information (grid, building or block footprint) containing building height in the core urban area of capital cities in order to obtain a better insight into measuring urban density | +| Minimum Mapping Unit | 10 m x 10 m or better | +| Minimum mapping width | N.A. | +| Nomenclature | N.A. | +| Projection | ETRS89 Lambert Azimuthal Equal Area (LAEA) (EPSG 3035) | +| Delivery formats | Raster GeoTIFF 16-bit | +| Metadata | INSPIRE Metadata Implementing Rules: Technical Guidelines based on EN ISO 19115 and EN ISO 19119 | +| Horizontal accuracy | Half a pixel | +| Vertical accuracy | 3 m | +| Overall classification accuracy | N.A. | + +## GENERAL GUIDELINES + +### PRE-PROCESSING AND GEO-CODING OF EO DATA + +The EO data provided to the Service Provider should already be pre-processed. In order to reach the 1/10,000 expected scale, EO data should be provided with spatial resolution less than 5 meters. Service Provider should check the geometric quality of the delivered EO data, but no further pre-processing step is expected. The EO data for the European Urban Atlas 2012 is the optical VHR2 coverage over EU 2011-2013 (DWH_MG2b_CORE_03) available on Data Warehouse (DWH) of the European Space Agency (ESA). The EO data for the Urban Atlas 2018 is an optical VHR coverage over EU 2017-2019 also available on ESA DWH. Change detection task can be supported by Copernicus Sentinel-2 data (10 m resolution). + +### PRE-PROCESSING AND GEOMETRIC ADAPTATION OF NAVIGATION DATA + +The EO data are the basis for interpretation and classification. In case of geometrical differences between EO data and navigation data (COTS or OSM), the navigation data has to be corrected in line with the EO data. +The pre-processing and application of the navigation data (COTS or OSM) shall be done according to the methodology defined in Annex 5.2. + +### PRE-PROCESSING OF TOPOGRAPHIC MAPS + +Topographic maps can be used for interpretation of objects. Topographic maps should be used in digital form with precise geo-coding. The usage of printed (analogue) maps is not recommended. In case of geometrical differences between EO data and topographic maps, the erroneous data (either EO-data or topo-maps) needs to be identified using reliable datasets providing spatial reference information. The geometry of the mapping product shall then be congruent with the correct dataset. + +### CLASSIFICATION AND INTERPRETATION + +Application of automatic classification routines, such as segmentation and clustering, may be applied whenever appropriate: +> Automated segmentation and classification to achieve an initial differentiation between basic land cover classes (urban vs. forest vs. water vs. other land cover) is possible following a decision of the service providers. +> As the backbone for the object geometry, the navigation data network (COTS or OSM) is recommended but only with the method defined in the Annex. +Manual refinement and classification at the finest level of the LU/LC classification especially within urban areas by means of visual interpretation of EO data remain necessary to be compliant with the product specifications. For this purpose, the following sections provide the rules and principles to be applied. + +### USE OF COPERNICUS HRL IMPERVIOUSNESS + +Among the reference products from Copernicus Land Monitoring Services (CLMS), the pan-European High-Resolution Layer (HRL) Imperviousness (formerly named Fast Track Sealing layer, FTS) is used for the classification at level 4 of the residential or mixed use urban fabric units classified 1.1.2 from EO data + +The assignment of the imperviousness/soil sealing levels or density (i.e. classes 1.1.2.1 - 1.1.2.4) shall be carried out using the HRL Imperviousness layer or similar one. The Quality Assurance (QA) process will check only if the technical approach agreed with DG REGIO is respected but will not assess the absolute accuracy of these classes. + +### DATA FORMAT OF THE FINAL PRODUCT + +ESRI ArcGIS compatible or open source OGC-standard vector format with polygon topology: + +* Complete coverage in a single area feature layer. +* No overlapping polygons, gaps, duplicates or missing polygon labels or node overshoots. +* Final vectors need to have a smooth appearance (no pixel-shaped polygons are allowed). The smoothing shall be done by the service provider by methods preserving the geometry of objects. It is to ensure that smoothed vectors still comply with the minimum width and minimum mapping units required for objects. + +| CODE_yyyyy | +|---| +| 11210* | +\* Example provided of the number for UA class 1.1.2.1 + +| CODE | → | Legend code | +|-----------------------------------------|--------------|------------------------------------------------------------------------------------------------------------------------------------------------| +| yyyyy | → | Reference year (e.g. 2006, 2012 or 2018) | + +Column data format: +CODE_yyyyy: 5 digits in Long Integer format without decimal places (values allowed: 11100 to 92000 (all class codes)) +The complete description of the data format is provided in section 5.4. + +### INTERPRETATION RULES + +* The delineation is to be done on the EO data. EO data should be considered as the primary (guiding) data source. +* The interpretation of the object is done using: + * The EO data, topographic maps, navigation data (COTS or OSM) and other relevant ancillary data. + * Auxiliary information including local expertise. +* The interpreted area should be interpreted with a minimum 100 m extension (100 m buffer) to ensure accuracy and continuity of polygons. During the post-processing phase, a subset with the spatial extent of the final product will be generated. At the borders of this subset (i.e. the final product), polygons smaller than the MMU may be present. To be in line with this rule, please note that a 100m extension buffer has been applied over sea / ocean for the coastal FUAs for the 2012 & 2018 products. +* In areas where two or more scenes overlap, the most recent data must be used for delineation and interpretation. +* In case of cloud coverage over the most recent scene, the affected part (only this part!) shall be interpreted using a cloudless alternative scene. +* If two or more objects are overlapping at different levels, the top level is mapped continuously, e.g. road bridge over railway is mapped as seen, the railway polygon is split in two parts and the road is mapped as a continuous feature. +* In case of two or more objects overlapping at the same height level, the visually dominant and complete object (in use and shape) is mapped continuously. For example, a road / railway crossing viewed at the same height level: the railway shall be mapped continuously to maintain the network. The road shall be split in two parts. +* Analysis scale : 1:5 000 + +### LU/LC CHANGE DETECTION AND LAYER GENERATION + +In case of the required production of an update of the Urban Atlas LU/LC database for a historic or new reference year, change detection task shall be performed and an additional layer shall be produced by the service provider, the Land Use / Land Cover (LU/LC) change layer. For the European Urban Atlas, following the LU/LC product generation for the 2006 reference year, change detection were performed successively for generating the 2012 and 2018 products. +Change detection and characterization shall be performed considering the VHR ortho-rectified optical satellite imagery as the reference. Adopting a manual methodological approach based on the visual comparison between the images from two different reference years is always required, but it can also be combined with automatic change detection approaches (image-to-image or map-to-image) which should improve the performances. Otherwise, as the change layer shall only contain real changes between two reference years, any inconsistencies of LU/LC codes will not be considered as a change if due to thematic misclassification inherited from the LU/LC information related to the first reference year already available. In such a case, it is worth to correct LU/LC misclassifications related to this year and generate a revised status layer accordingly. +In the specific case of the change detection between 2012 and 2018 for the European Urban Atlas, LU/LC misclassifications from 2012 are corrected but only in the immediate surroundings of the actual LU/LC change area, which led to produce a revised LU/LC 2012 status layer. Otherwise, LU/LC changes from, to, or within urban areas are detected, extracted and characterized at the finest level of the nomenclature, in accordance with the MMUs listed below. However, LU/LC changes occurring within rural/natural areas are mapped only if the change occurs at level 1 of the nomenclature, then well characterized at the finest level (ex: change from 31000 to 23000). In other words, changes that occur within the same rural/natural areas are not mapped (for instance, changes from 21000 to 22000 are not identified and therefore not included in the change layer). +Changes are firstly reported in an intermediate layer identified as Urban Atlas 2012-2018 LU/LC which contains the LU/LC information for both reference dates, 2012 and 2018. More concretely, based on the 2012 LU/LC information, polygon features of detected changes are delineated and characterized for 2018. Then, the change layer is generated, but it is worth to highlight that it is not performed by a fully blind extraction of all area features for which LU/LC classification codes are different. Indeed, there are one exception for which the area features are not considered for generating the change layer: isolated structures logically classified as 11300 for 2012 reference year and classified as continuous or discontinuous urban fabric unit (11100 or 11200) for 2018 only because of urban expansion over the period but without any actual LU/LC change (contextual reason). This means that for getting real changes occurred between 2012 and 2018, the 2012-2018 change layer shall be used; combining both status layers and extracting the features which have different LU/LC classifications overtime will not provide this information. +A particular case: forest cuts. +As it is often quite difficult to predict the future LU/LC over an area resulting from a forest cut, the rule applied is the following: + +* Forest cuts are included in Class 31000 named 'Forests' by default as it is mentioned in Section 4.6, and systematically in case such areas are fully surrounded by wooded area. +* Forest cuts are included in another class as soon as they occur at the edge of the forest and that the context given by VHR reference imagery justifies such a choice (e.g. pastures, orchards, future construction site, etc.). + + + +### STREET TREE LAYER + +The Street Tree Layer 2012 (STL2012) and Street Tree Layer 2018 (STL2018) are separate layers from respectively the UA2012 and UA2018 LU/LC Layers, which are produced within the level 1 urban mask for each FUA. They include contiguous rows or patches of trees covering 500m² or more and with a minimum width (MinMW) of 10 m over "Artificial surfaces" (nomenclature class 1) inside FUA (i.e. rows of trees along the road network outside urban areas or forest adjacent to urban areas should not be included). +The following specific rules have to be applied: + +* Exception to the 10 m MinMW rule: in order to maintain continuity, units smaller than 10 m over a distance up to 10 m can still be included in STL (see figures below). + + + +* Border effect correction: road network crossing wooded area and connecting cities and villages is fully classified as STL due to tree crown cover by default. Therefore, post-processing step needs to be implemented for correcting this misclassification. + + + +An urban mask analysis to exclude road and railway networks connecting cities and villages shall be applied to solve this border effect. Urban Mask analysis needs to define a tolerance fixed to 50 m that well differentiate urban and interurban networks. Large railway infrastructures may be excluded from the mask, but this is not an issue knowing that trees are very rare in such areas. + + + +STL patches do not cover roads (codes 12210 and 12220) or railways (code 12230) connecting cities, that are outside the urban mask. + +* MMU rule (>500 m²) is applied to STL polygons (STL = 1) +* The STL layer contains trees (STL = 1) or No data (STL= 99). + + + +### DIGITAL HEIGHT MODEL LAYER (DHM) + +A 10 m raster layer containing height information is generated for core urban areas of capital cities of the EEA38 and the United Kingdom as part of the Urban Atlas project (see table 2), based on the methodology described hereafter. + +1. Base data +The input data source of stereo data is the IRS-P5 Cartosat-1. In May 2005, the Indian Space Research Organisation (ISRO) had launched the IRS-P5 Cartosat-1 satellite, equipped with the PAN-Aft and PAN-Fore instruments that form a dual-optics 2-line (forward and backward looking) along-track stereoscopic push broom scanner with a stereo angle of 31°, an original spatial resolution of 2.5 m and a swath width of 27 km. The satellite covers the entire globe in 1867 orbits on a 126-day cycle. +Because of its optimized stereo configuration (stereo acquisitions within seconds and optimized acquisition angles), IRS-P5 Cartosat-1 high-resolution stereo satellite imagery has proven to be perfectly suited for the creation of the digital surface models in the resolution and quality requested. +The very comprehensive data situation and the unique specifications guarantee a very good coverage of urban areas in Europe by one single data source. + +2. DSM generation +Based on the stereo images, a digital surface model (DSM) is generated, which represents the height of all objects above the earth's surface, i.e. plant/ forest canopy or the top of artificial constructions such as buildings. +The major steps are: + +* Selection of suitable stereo images +* Pixel-wise matching approach +* Bundle block adjustment and RPC improvement +* Automated outlier detection +* Void filling +* Process QA + +The result of the DSM processing chain is an accurate and reliable 3 m DSM (to get the full advantages of the higher resolution, e.g. for a better delineation of buildings), which will be further regularised to the final 10 m resolution in the nDSM generation step, to fit the product requirements. + +3. DTM generation +Digital terrain models (DTMs) represent the height of the bare earth's surface. In this case, the DTMs are generated from a DSM and non-ground features such as buildings, trees, bushes and vehicles have to be identified and their height measurements be removed from the data before interpolation. +In order to ensure meeting the DTM quality requirements even in complicated terrain situations and to ensure best possible accuracy of generated DTMs, it is useful to combine the extracted ground point results of different filter methods or apply regionally adjusted algorithm parameterizations. +The interpolation approach carried out in this project is ordinary Kriging. It is the most commonly used Kriging method, used in simulations for spatial data, commonly considered to best minimizes the variance of the estimation error. + +4. nDSM generation and refinement +The next step towards finally retrieving building block heights is the creation of a normalized Digital Surface Model (nDSM). An nDSM is the difference between a DTM and a DSM used to show height values of buildings, vegetation and other objects. + +nDSM = DSM - DTM + + + +As there are vegetation and other untilled areas included in the raw nDSM, hence a substantial further quality refinement with different ancillary datasets is necessary to generate a sharp and error-free digital building block height model. +Ancillary datasets consist of (i) built-up area, water and street classes of Urban Atlas 2012 and (ii) NDVI map for vegetation removal, calculated from Spot 5 seamless mosaics. + +The nDSM at this stage is a 3 m resolution intermediate product that is then resampled with a maximum upscaling process to the final 10 m horizontal resolution, and which is then finally sharpened by using the borders of the built-up area classes from Urban Atlas 2012. Values outside these classes are set to Zero. Each pixel represents a height value and no statistics have been calculated based on the building block heights. + +In a last refinement step the accuracy specifications are checked and if necessary, heights are adapted. + + + +5. Post processing and product finalization +In the final post-processing and product package generation step, the properly refined nDSM with building block heights, which are considered the final product (Digital Height Models DHM) are checked by an automated routine for adherence to the following basic technical and formal product specifications: + +* 10 m spatial resolution. +* ETRS89 - Lambert Azimuthal Equal Area (LAEA) projection. +* Consistent file format, naming and structure, class legends and color codes. +* Correct height values / background values. + +Each Building Block Height model (DHM) is clipped exactly to the AOI and assigned a folder with a corresponding name. As a further automated step, an INSPIRE compliant metadata file is generated for every product and a template for the delivery report is created for each DU and stored in the respective folder. + + + +### MINIMUM MAPPING UNITS AND EXCEPTIONS + +| CORINE Classes [Lev. I, No.] | Levels provided | MMU | +|----------------------------------------------------------------------------------------------------------|----------------------------------------------------------|-----------------------------------| +| Urban | 1 | I-IV | 0.25 ha | +| Rural | 2-5 | I-II | 1 ha | +| Street Tree Layer | - | - | 500 m² | + +Exception of MMU 0.25 / 1 ha: in case of a homogeneous area > MMU but divided in 2 or more polygons by the road or railway network, each part can be smaller to preserve the land cover information. However, no polygon can be smaller than 500 m² (e.g. a 1 ha forest divided in 4 polygons by the road network has to be mapped) except for polygons at the border of the FUA (>100 m²). +The minimum mapping width (MinMW) between 2 objects for distinct mapping is 10 m. +Exception of minimum width 10m of a mapping unit: class 12220 (MMW = 6m). +Exception of minimum width 10m of a mapping unit: to maintain continuity of linear structures, they can be mapped smaller than 10 m over a distance up to 50 m (see figure below). + + + +Priority mapping rules for areas smaller than the MMU: + +1. Smaller areas are visually added to the adjacent unit with the thematically closest class. Rule used for the artificial codes (1xxxx). +2. Smaller areas are added to the adjacent unit with the longest common border line, except to railways or roads (exception here: if an object is below the MMU size and completely surrounded by e.g. road or railway network it shall be aggregated with that surrounding traffic line). Rule used for the natural / semi-natural codes (2xxxx, 3xxxx, 4xxxx, 5xxxx) and all the polygons < 100m². + +Specific Minimum Mapping Units for LULC CHANGE layer : +In order to ensure that relevant LU/LC changes are appropriately extracted, the MMUs for the LU/LC change layer (e.g. 2012-2018) are defined as below: + +* Urban (class 1) to urban (class 1) = 0.1 ha +* Rural/natural (classes 2-5) to urban (class 1) = 0.1 ha +* Rural/natural (classes 2-5) to rural/natural (classes 2-5) = 0.25 ha +* Urban (class 1) to rural/natural (classes 2-5) = 0.25 ha + +Considering this MMUs, exceptions are made in case of areas where changes involve road and railway networks (classes 12210, 12220, 12230); polygon features classified as road or railway for one date or directly connected to such element are extracted even if area is lower than MMU in order to keep consistency of the transportation network. + +### ACCURACY ASSESSMENT AND VALIDATION + +In addition to the usual QA/QC procedure implementation, which implies visual checks for ensuring thematic and geometric positional accuracies and automatic checks for ensuring completeness and logical consistency during the post-processing phase of the production, accuracy assessment and validation must be performed independently from the production for providing the evidence that the results are fully compliant with the product specifications. +Thus, accuracy assessment follows a "double-blind" approach which means that production team and reference data team work completely independently. Reference database is obtained through the interpretation of samples which are selected based on a stratified random sampling of EEA 1km grid cell. The number of samples for each FUAs varies depending on the FUA size. The results of the reference interpretation provide the material to derive confusion matrixes, which are used to assess the accuracies. + +| CORINE Classes [Lev. I, No.] | Levels provided | Thematic Accuracy | Positional Pixel Accuracy | +|-----------------------------------------------------------|---------------------------------------|------------------------------------------|-----------------------------------------------------------| +| Urban | 1 | I-IV | >= 85% | <± 5 m | +| Rural | 2-5 | I-II | >= 80% | <± 5 m | +| Overall Accuracy | | | >= 80% | | + +For the DHM layer, Quality controls are conducted for each stage of the production: the DSM and DTM generation, the nDSM production, the masking and the nDSM / Building Block Height refinement. Thus, the emerging dataset is quality checked several times throughout the process. +Every product is accompanied by a DHM QC Report, which shows the results of the external Quality Check and an INSPIRE compliant xml file: [Building Height 2012 — Copernicus Land Monitoring Service](https://land.copernicus.eu/user-corner/technical-library/dhm-validation-report). + +## LU/LC NOMENCLATURE + +The Urban Atlas Land Use / Land Cover classification is derived from CORINE Land Cover and is composed of 27 classes distributing among 5 thematic groups, namely: +1. Artificial surfaces +2. Agricultural areas +3. Natural and (semi-)natural areas +4. Wetlands +5. Water +Four hierarchical levels are defined in the nomenclature for artificial surfaces (class 1) while only two are defined for the non-artificial ones (classes 2-5). The table below shows the LU/LC nomenclature providing class codes and descriptions and any information or ancillary data source useful for the product generation in addition to EO data. +A decision matric below illustrate the rules followed to apply LULC nomenclature to each identified feature. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 3: UA LULC nomenclature (in bold, classes without any further subdivision) +::: + +```{=html} +
| UA No. | +Code | +Nomenclature | +Additional Information | +
| 1 | +Artificial surfaces | +||
| 1.1 | +Urban Fabric | +||
| 1.1.1 | +11100 | +Continuous urban fabric (S.L. > 80%) | +HRL IMD required | +
| 1.1.2 | +Discontinuous Urban Fabric (S.L. 10% - 80%) | ++ | |
| 1.1.2.1 | +11210 | +Discontinuous dense urban fabric (S.L. 50% - 80%) | +HRL IMD required | +
| 1.1.2.2 | +11220 | +Discontinuous medium density urban fabric (S.L. 30% - 50%) | +HRL IMD required | +
| 1.1.2.3 | +11230 | +Discontinuous low density urban fabric (S.L. 10% - 30%) | +HRL IMD required | +
| 1.1.2.4 | +11240 | +Discontinuous very low density urban fabric (S.L. < 10%) | +HRL IMD required | +
| 1.1.3 | +11300 | +Isolated structures | ++ |
| 1.2 | +Industrial, commercial, public, military, private and transport units | +||
| 1.2.1 | +12100 | +Industrial, commercial, public, military and private units | +zoning data / field check recommended | +
| 1.2.2 | +Road and rail network and associated land | +COTS or OSM data required | +|
| 1.2.2.1 | +12210 | +Fast transit roads and associated land | +COTS or OSM data required | +
| 1.2.2.2 | +12220 | +Other roads and associated land | +COTS or OSM data required | +
| 1.2.2.3 | +12230 | +Railways and associated land | +COTS or OSM data required | +
| 1.2.3 | +12300 | +Port areas | +zoning data / field check recommended | +
| 1.2.4 | +12400 | +Airports | +zoning data / field check recommended | +
| 1.3 | +Mine, dump and construction sites | +||
| 1.3.1 | +13100 | +Mineral extraction and dump sites | ++ |
| 1.3.3 | +13300 | +Construction sites | ++ |
| 1.3.4 | +13400 | +Land without current use | ++ |
| 1.4 | +Artificial non-agricultural vegetated areas | +||
| 1.4.1 | +14100 | +Green urban areas | ++ |
| 1.4.2 | +14200 | +Sports and leisure facilities | ++ |
| 2 | +Agricultural areas | +1 ha MMU | +|
| 2.1 | +21000 | +Arable land (annual crops) | ++ |
| 2.2 | +22000 | +Permanent crops | ++ |
| 2.3 | +23000 | +Pastures | ++ |
| 2.4 | +24000 | +Complex and mixed cultivation | ++ |
| 3 | +Natural and (semi-)natural areas | +1 ha MMU | +|
| 3.1 | +31000 | +Forests | ++ |
| 3.2 | +32000 | +Herbaceous vegetation aassociations | ++ |
| 3.3 | +33000 | +Open spaces with little or no vegetation | ++ |
| 4 | +40000 | +Wetlands | +1 ha MMU | +
| 5 | +50000 | +Water | +1 ha MMU | +
| 9.1 | +91000 | +No data (Clouds and shadows) | ++ |
| 9.2 | +92000 | +No data (Missing imagery) | ++ |
| FRC | +Net2Class | +|||
| 1 | +2 | +3 | +4 | +|
| 0 | +Manual | +|||
| 1 | +Manual | +|||
| 2 | +Estimated buffer width | +Estimated buffer width | +Estimated buffer width | +Estimated buffer width | +
| 3 | +Estimated buffer width | +Estimated buffer width | +Estimated buffer width | +Estimated buffer width | +
| 4 | +Estimated buffer width | +Estimated buffer width | +Estimated buffer width | +Estimated buffer width | +
| 5 | +Estimated buffer width | +Estimated buffer width | +Estimated buffer width | +Estimated buffer width | +
| 6 and above | +Mapping decided on city by city basis | +|||
| Product features | +|
| Digital thematic map. Thematic classes based on CORINE LC nomenclature and Copernicus Urban Service Legend |
+ |
| Input data sources | +|
| Earth Observation (EO) data with 2 to 4 m spatial resolution multispectral or pan-sharpened (multispectral merged with panchromatic) data. Multispectral data includes near-infrared band.10m resolution Sentinel-2 EO data for the rural 2012-2018 change detections. Topographic Maps at a scale of 1: 50 000 or larger. Navigation data (COTS or OSM) for the road network. Areas of Interest for Urban Atlas (UA) Mapping are determined by DG REGIO. Soil Sealing / Imperviousness Degree (IMD) based on HRL Imperviousness specifications for degree of sealed soils (IMD 0-100%) for level 3 classes 1.1.1 and 1.1.2 and level 4 classes 1.1.2.1, 1.1.2.2, 1.1.2.3 and 1.1.2.4. All input data should be described by metadata according to the INSPIRE metadata profile specifications and guidelines. |
+ |
| Ancillary data optional for all classes | +|
| Navigation data (COTS or OSM): points of interest, land use, land cover, water areas. Google Earth or other relevant available database (only for interpretation, not for delineation). Local city maps |
+ |
| Ancillary data optional for all classes | +|
| Local zoning data (e.g. cadastral data). Field check (on-site visit). Very high resolution imagery (better than 1 m ground resolution, e.g. aerial photographs). |
+ |
| Geometric resolution (Scale) | +|
| 1:10 000 | +|
| Geographic projection / Reference System | +|
| ETRS_1989_LAEA (for UA2012) | +|
| Positional accuracy | +|
| ± 5 m | +|
| Thematic accuracy (in %) | +|
| Minimum Overall Accuracy (OA)for level 1 class 1 "Artificial surfaces: 85% Minimum OA (Rural surfaces and Overall): 80%. Methodology for quality control has to be performed according to RD[1]. The minimum OA for level 1 class 1 "Artificial surfaces" must include both omission and commission errors with other classes within the Functional Urban Areas (FUA). |
+ |
| Update frequency | +|
| Every 6 years | +|
| Base data topicality | +|
| Not specified | +|
| Delivery format | +|
| Topologically correct GIS files. Single part features. |
+ |
| Data type | +|
| Vector | +
- Insertion of attribute tables description for Urban Atlas 2006 revised, Urban Atlas 2012, Changes 2006-2012, Urban Atlas 2006-2012
- New unique projection: ETRS_1989_LAEA
- Exception of MinMW for the Roads: 6 m | +| V4.1 | 03/05/2016 | - Urban Atlas 2012 Mapping Guide with European Commission Template
- Insertion of Section 4.6: Description of mapping units for the Urban Atlas Street Tree Layer | +| V4.2 | 04/05/2016 | - Minor corrections | +| V4.3 | 24/06/2016 | - Insertion of new “no data” classes: 91000 No data (Clouds and shadows) / 92000 No data (Missing imagery) | +| V4.4 | 05/07/2016 | - Minor corrections | +| V4.5 | 27/07/2016 | - Insertion of Section 4.7 dedicated on the change layer including the specific MMU for this layer
- Insertion of the Table of Content at the beginning of the document | +| V4.6 | 03/08/2016 | - Removal of the confusing sentence in section 4.2.9. about Minimum mapping units: "Maximum mapping width (MaxMW) between 2 objects for mapping together 10 m"
- MaxMW was also removed from the list of abbreviations | +| V4.7 | 28/10/2016 | - Insertion of report information and Change records | +| V4.8 | 02/12/2016 | - Insertion of a versioning on the cover page and on the footer
- New formulation for the sentences of the priority rules, section 4.2.10
- Insertion of a figure for the Street Tree Layer minimum mapping unit, section 4.8
- Modification of the STL no data code from 91000/92000 to 99, section 5.4.5. | +| V5.0 | 24/02/2017 | - Paging corrections | +| V6.0 | 06/06/2019 | - Some minor precisions in classes description.
- Updates to include the 2018 exercise. | +| V6.1 | 28/02/2020 | - Update of Reference Documents
- Update on section 4.1 Product description (addition of DHM product)
- Update on section 4.2 General Guidelines (redesign of section to include latest changes on change layer, STL product)
- Update on section 5.4 product types and field descriptions | +| V6.2 | 21/09/2021 | - Add of a section about the digital height model layer (DHM) | +| V6.3 | 06/01/2023 | - Clarifications on descriptions for classes 1.1.3. and 1.4.1. | + +Source of photos: Spot Image S.A, provided under EC/ESA GSC-DA; includes material +©CNES, Distribution Spot Image S.A., all rights reserved +© European Union, 2020 +Reproduction is authorized provided the source is acknowledged. + + diff --git a/Files_to_convert/processed_documents/output/Mapping_Guide_Land_Cover_Land_Use_and_Street_Tree_Layer_2012_and_2018/Mapping_Guide_Land_Cover_Land_Use_and_Street_Tree_Layer_2012_and_2018.source.pdf b/Files_to_convert/processed_documents/output/Mapping_Guide_Land_Cover_Land_Use_and_Street_Tree_Layer_2012_and_2018/Mapping_Guide_Land_Cover_Land_Use_and_Street_Tree_Layer_2012_and_2018.source.pdf new file mode 100644 index 00000000..bce8e59d Binary files /dev/null and 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@@ +{ + "overall": "warn", + "checks": [ + { + "name": "frontmatter", + "status": "ok", + "summary": "all required fields present", + "metric": null, + "findings": [] + }, + { + "name": "structural_counts", + "status": "warn", + "summary": "figures 12/13; tables 22 in .qmd (~26 source region(s), rough \u2014 see table_coverage)", + "metric": null, + "findings": [ + { + "message": "13 figures detected but 12 image refs in .qmd", + "severity": "warn", + "location": "figures" + } + ] + }, + { + "name": "figure_placement", + "status": "warn", + "summary": "12/13 detected figures placed, 1 unreferenced", + "metric": 92.3, + "findings": [ + { + "message": "detected figure FIG_1 not placed in the .qmd", + "severity": "warn", + "location": "" + } + ] + }, + { + "name": "text_coverage", + "status": "warn", + "summary": "text coverage 99.6% (938/942 sentences; 4 missing)", + "metric": 99.6, + "findings": [ + { + "message": "missing: EXECUTIVE S U M M A R Y .", + "severity": "warn", + "location": "" + }, + { + "message": "missing: height of all objects above the earth\u2019s surface, i.e.", + "severity": "warn", + "location": "" + }, + { + "message": "missing: Sports fields (also outside the settlement area).", + "severity": "warn", + "location": "" + }, + { + "message": "missing: Most of the parcels contain individual cultivation areas with fruits or vegetables, as well as a shed for tools and shel", + "severity": "warn", + "location": "" + } + ] + }, + { + "name": "table_coverage", + "status": "ok", + "summary": "26 source table(s); weighted word coverage 99.4% (simple avg 98.4%)", + "metric": 99.4, + "findings": [] + }, + { + "name": "oversized_tables", + "status": "ok", + "summary": "no oversized source tables", + "metric": null, + "findings": [] + }, + { + "name": "wide_table_legibility", + "status": "ok", + "summary": "no tables shrunk below 8pt", + "metric": null, + "findings": [] + } + ] +} \ No newline at end of file diff --git a/Files_to_convert/processed_documents/output/Mapping_Guide_Land_Cover_Land_Use_and_Street_Tree_Layer_2012_and_2018/verify_report.md b/Files_to_convert/processed_documents/output/Mapping_Guide_Land_Cover_Land_Use_and_Street_Tree_Layer_2012_and_2018/verify_report.md new file mode 100644 index 00000000..9c47aca0 --- /dev/null +++ b/Files_to_convert/processed_documents/output/Mapping_Guide_Land_Cover_Land_Use_and_Street_Tree_Layer_2012_and_2018/verify_report.md @@ -0,0 +1,46 @@ +# Verify report + +**Overall: warn** + +## ✅ frontmatter — ok + +all required fields present + +## ⚠️ structural_counts — warn + +figures 12/13; tables 22 in .qmd (~26 source region(s), rough — see table_coverage) + +- **warn** (figures): 13 figures detected but 12 image refs in .qmd + +## ⚠️ figure_placement — warn + +12/13 detected figures placed, 1 unreferenced + +_metric: 92.3_ + +- **warn**: detected figure FIG_1 not placed in the .qmd + +## ⚠️ text_coverage — warn + +text coverage 99.6% (938/942 sentences; 4 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"2016-01-01" +category: uncategorized +--- +This publication is a Technical report by the Joint Research Centre (JRC), the European Commission's science and knowledge service. It aims to provide evidence-based scientific support to the European policy-making process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of this publication. + +**Contact information** +Name: Carlo Lavalle +Address: European Commission, Joint Research Centre, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy +E-mail: carlo.lavalle@jrc.ec.europa.eu +Tel.: +39 0332 78 5231 + +**JRC Science Hub** +[https://ec.europa.eu/jrc](https://ec.europa.eu/jrc) + +**Land-Use-based Integrated Sustainability Assessment Modelling Platform** +[https://ec.europa.eu/jrc/en/luisa](https://ec.europa.eu/jrc/en/luisa) + +JRC103756 + +EUR 28194 EN + +| | | | | +|------------------|------------------------------------------------------------------------------|------------------------------------------------|--------------------------------------------------------| +| PDF | ISBN 978-92-79-63484-0 | ISSN 1831-9424 | doi:10.2791/06831 | + +European Union, 2016 + +Reproduction is authorised provided the source is acknowledged. + +How to cite: Batista e Silva F and Poelman H (2016) Mapping population density in Functional Urban Areas - A method to downscale population statistics to Urban Atlas polygons. JRC Technical Report no. EUR 28194 EN. doi:10.2791/06831. + +All images European Union 2016, except front cover photo from Oliver Wendel, licensed under Creative Commons Zero. Source: unsplash.com + +# Abstract + +Urban Atlas 2012 is a powerful geographical dataset that describes land use/land cover at high spatial resolution for nearly 700 European Functional Urban Areas of more than 50,000 inhabitants in 31 European countries (EU28 + EFTA). The objective of the work described in this report was to enrich the Urban Atlas dataset by including estimates of residential population at the vector polygon level. + +The estimation was done by downscaling, or disaggregating, census population reported at country-specific geometries (‘source geometry’) to the Urban Atlas land use/land cover polygons (‘target geometry’). The downscaling method can be described as a ‘smart’ areal interpolation which combined land use/land cover information from Urban Atlas, building densities from the European Settlement Map and census data. For numerous Functional Urban Areas for which population data were previously only available at coarse resolution (e.g. municipality, or 1 Km² grid cells), these newly released estimates represent a significant increase in spatial resolution, enabling diverse fine scale analyses for the whole Urban Atlas dataset. + +With the free-of-charge distribution of this dataset, the number of potential users and applications of the Urban Atlas can further increase. Detailed maps of population density are very important for the study and characterisation of urban areas, and are essential inputs for urban and infrastructure planning and management, disaster risk assessment and mitigation, social policies, and analysis of quality of life and well-being. Moreover, this work further expands the knowledge base of JRC's LUISA territorial modelling platform, used to assess regional and local impacts of European trends, policies and investments. + +# Introduction + +## The importance of population density maps + +Detailed maps of population density are very important for the study and characterisation of urban areas. Such maps help urban practitioners design plans and policies that are based on knowledge of the urban area's population distribution profiles. Detailed and accurate information of population distribution is relevant for urban planning in general, particularly for planning of infrastructures, management of waste and utilities, disaster risk assessment and mitigation, social policies, quality of life and well-being, to mention just a few areas of application. + +## Complete and consistent data for comparable assessments + +Complete and consistent data of population densities across European cities is useful if one needs to compare aspects from different cities in the wider continental context. As such, the coverage of population density maps ought to be large and representative and the maps must be as comparable as possible. This double challenge is becoming more feasible to overcome with the increasing availability of data in large quantities and high quality with which such comparable population density maps can be constructed. + +One key issue is that a definition of a city or urban area based on its official administrative boundaries is a poor and misleading one. Urban areas are functional spaces which often do not match with politically defined boundaries. The Organisation for Economic Co-operation and Development (OECD) and the European Commission (EC) have recently established a harmonized definition of Functional Urban Areas (FUA) which takes into account both the population distribution and densities (urban form) and the commuting patterns to the urban centre. In simple terms, a Functional Urban Area is therefore defined as the contiguous set of municipalities which have at least 50% of their population in the urban centre (defined as the contiguous set of urban cells of 1 Km by 1 Km with a population density of at least 1500 inhabitants/Km² and a total population of at least 50,000 inhabitants), plus the surrounding municipalities for which at least 15% of the employed persons commute to the main municipality of the urban agglomeration)[^1]. + +## Mapping land uses and population for European Functional Urban Areas + +In their continuous efforts to improve the knowledge base for the European urban areas, the EC Directorate-General for Regional and Urban Policy (DG REGIO) and the Directorate-General for Growth, in coordination with the European Space Agency and the European Environment Agency (EEA), supported the creation of the Urban Atlas (UA) dataset. The Urban Atlas is a collection of high-resolution digital and vector land use/land cover (LULC) maps, covering hundreds of European FUAs. The UA is now part of the range of Copernicus land monitoring services, and is freely available to the public. + +At the request and with the support of DG REGIO, the Directorate-General Joint Research Centre (JRC) enriched the UA dataset by estimating the number of residents in each vector polygon for all the covered FUAs. The estimated residential population remains as an additional attribute to the land-use classification, which is hoped to broaden the range of potential applications of the Urban Atlas dataset, contributing to new analyses and assessments in different thematic fields[^2]. Moreover, these new estimates are an additional element of the knowledge base of JRC's LUISA territorial modelling platform, used to assess regional and local impacts of European trends, policies and investments[^3]. + +The methodology used to perform the estimation combines the land-use information present in the UA dataset itself with two other key data inputs: population data derived from the latest censuses by the National Statistical Institutes (NSIs) and built-up densities derived from the JRC's European Settlement Map (ESM). By combining these three data inputs by means of Geographical Information System (GIS) operations, it is possible to produce highly detailed and comparable population density maps for all the Functional Urban Areas available in the Urban Atlas. + +The estimation of residential population for the Urban Atlas polygons has already been conducted and documented in the past by JRC and REGIO[^4]. The main differences of the current work vis-à-vis the earlier experience are synthetized in table 1. In summary, in the current estimation exercise the base year is updated from 2006 to 2011-2012, the coverage in terms of number FUAs has more than doubled, population for main age-groups is present for FUAs in a selected number of countries, and improvements to the estimation methodology have been implemented, particularly by including an improved and updated representation of built-up densities and by recording the number of residents as integer numbers, instead of continuous. + +[^1]: See Dijkstra and Poelman (2012) for additional details. +[^2]: See a recent example of how population estimates for UA polygons have been used to evaluate access to green areas in Europe's cities in Poelman (2016). +[^3]: See Lavalle et al. (2016) and [https://ec.europa.eu/jrc/en/luisa](https://ec.europa.eu/jrc/en/luisa). +[^4]: See Batista e Silva et al. (2013a) for related report. + +## Aim and structure of the report + +The aim of this report is to describe the methodology and source data used to estimate residential population in each built-up polygon of the Urban Atlas 2012 dataset. The remainder of the report is organized as follows: section 2 describes in detail the input data and methodology applied; section 3 describes the resulting product and discusses data quality issues; section 4 wraps-up the work done and sets the scene for future developments in the field of high-resolution population mapping. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 1. Summary of main differences between the 2013 and the 2016 releases of the Urban Atlas population estimates. +::: + +| | 2013 release | 2016 release | +|------------------------------------------------------------------------|------------------------------------------------------------|--------------------------------------------------------------------| +| Urban Atlas version | 2006 | 2012 | +| No. of countries covered | 27 (EU27) | 31 (EU28 + EFTA) | +| No. of FUAs covered | 301 | 697 | +| Source of population data| NSIS + GEOSTAT + Others | NSIS + GEOSTAT | +| Reference year | 2006 | 2011 | +| Age-group data | No | Yes (selected countries)* | +| Source for built-up density| EEA Soil Sealing Layer | JRC European Settlement Map | +| Reference year | 2006 | 2012 | +| Methodology | Areal weighting / Dasymetric | Areal weighting / Dasymetric | +| Number type | Continuous (float) | Discrete (integer) | + +Notes: +\* Belgium, Spain, Italy, Malta, Netherlands, Slovenia, and UK (except Scotland). + +# Data and methods + +## Key concepts and definitions + +### Population data and population maps + +In very broad terms, population data can be stored in a conventional table, relational database, or geographical database. The latter form of storing population data is the most convenient because it links population counts to the respective spatial reporting units, their shape, size and geographical location. Population maps are visual representations of population distribution or density, and can nowadays be easily constructed with specialized GIS-software used to display, manage and manipulate geographical databases. + +### Zoning systems + +Population maps are therefore composed of population counts for a set of individual geographical units which, altogether, form a geographical zoning system. There are various types of geographical zoning systems: administrative, statistical, analytical, and regular. + +Administrative zoning systems are those composed of geographical units relevant for political and administrative purposes, such as countries, regions, municipalities or even parishes. Statistical zoning systems are those which are created deliberately for the collection and dissemination of statistical data. Examples are the European nomenclature of territorial units for statistics (NUTS), or census tracts. For practical reasons, there is often a correspondence between administrative and statistical zoning systems. Analytical zoning systems are those whose boundaries are constructed with certain underlying thematic considerations. That is the case of tessellations based on land use/land cover, soil types, climate or biogeographical regions. Finally, regular zoning systems are tessellations of space based on equally shaped and sized geographical units, e.g. a grid of squared-cells of a pre-established size. + +### Residential, night-time and day-time population + +Another important preliminary consideration is the definition of residential population. Residential population refers to the number of people who declare to reside in a given location. As such, when mapping residential population we are essentially mapping the distribution of population during the night time, assuming that most people stay in their declared places of residence during the night for shelter and rest, and excluding the fraction of people who work outside their residences during night time. Conversely, the location of population during the day is determined by the location of economic, social and leisure facilities which pull population off their residences, driving commuting flows and other forms of daily trips. Day-time population distribution thus varies greatly from night-time distribution. Contrary to night-time population, which can be straightforwardly inferred by official statistics on residential population, it is extremely more challenging to infer day-time population distribution. While research is currently on-going at the JRC to advance as well in the mapping of day-time population[^5], that is not addressed in the report herein. + +### The 'dasymetric' mapping method + +The estimation of residential population for the Urban Atlas polygons is done by a 'smart' areal interpolation procedure whereby counts of residents provided at a given 'source' geometry (regular grids, census tracts or commune boundaries) are transferred to the 'target' geometry, i.e. in this case the Urban Atlas polygons. The transfer, downscaling, or disaggregation, of the population counts from source to target geometry is done by means of GIS and tabular operations, whereby source geometry, target geometry, and population covariates (‘ancillary' data) are combined. The combination of population counts available at a relatively coarse spatial geometry with covariates of population distribution such as land uses, building footprints, night-time lights, or street segments to generate more detailed maps of population density is typically referred in the literature as ‘dasymetric' mapping[^6]. + +The following sections describe in more detail the different types of input data used and how they have been combined in this work. + +[^5]: The JRC exploratory research project 'ENACT' is exploring methods to construct multi-temporal population distribution grid maps that reflect both daily and seasonal variations, leveraging new and unconventional ('big') data sources. +[^6]: See Batista e Silva et al. (2013b) for a review of the dasymetric method. + +## Input data + +### Source data: census population reported at various zoning systems + +Source data refers to the original population values reported at a given zoning system (e.g. regular grids, census tracts or commune boundaries). The spatial resolution/detail of the source data influences significantly the accuracy of the final disaggregated population maps, and so it is convenient to use the finest available source of population data for each FUA or country. As preferred option, high resolution ‘bottom-up grids' (< 1 km² cell size) were chosen as source data. Bottom-up grids refer to population values assigned to fine grid systems of regular squared cells, usually based on geo-referenced point data obtained through field surveys/censuses conducted by NSIs. Because such grids are unfortunately only available for a few countries, other sources of population data were sought, namely census tracts and commune boundaries. For the majority of the countries, however, the GEOSTAT grid 2011 was used as source data. The GEOSTAT grid is a population dataset maintained by Eurostat which reports residential population for each 1 Km² grid cell. This grid is based on the censuses carried in most countries in 2011, and largely produced by aggregating point-based population records[^7]. + +### Target data: Urban Atlas land use polygons + +Target data is herein defined as the set of polygonal entities for which population needs to be estimated. In this case, the target data are the polygons of the Urban Atlas dataset. The Urban Atlas 2012 is a collection of high-resolution maps of land use/land cover covering 697 Functional Urban Areas of over 50,000 inhabitants from 31 European countries (EU28 + EFTA). The UA has a thematic resolution of 27 LULC classes compatible with the CORINE Land Cover nomenclature. In terms of spatial resolution, the UA is characterized by a minimum mapping unit (MMU) of 0.25 hectares for all artificial surfaces and 1 hectare for all the remaining LULC classes (agriculture, forest, wetlands and water). The UA is produced using Earth Observation (EO) data, road network datasets and topographic maps. The delineation of LULC polygons is done by using a mix of automatic methods, visual interpretation and local expertise[^8]. The UA is available for free download in the Copernicus Land Monitoring Services[^9]. + +### Ancillary data: European Settlement Map's built-up densities + +Lastly, as ancillary data to guide the population disaggregation process, the European Settlement Map was used. The ESM is an EO-derived, 10 metre-resolution raster dataset representing built-up areas. The pixel values represent the share of pixel covered by any built-up structure, and was produced by the JRC using innovative machine learning techniques that relate morphological and textural characteristics present in the imagery with the occurrence of built-up structures. + +In the herein work, the ESM was used to populate each UA polygon with a built-up area estimate, which is then used as a weight in the population disaggregation process. This of course assumes a positive relationship between share of built-up area and population density. An important shortcoming of the ESM is that it only captures the horizontal density of built-up, while missing the vertical dimension. The implication is that the volume of built-up (a better predictor of population presence) cannot be derived. Building heights could be derived, for example, from airborne LIDAR sensor data, but their acquisition for all the European space is still unviable[^10]. + +Table 2 summarizes the data used in this project. + +[^7]: Further information available at: [http://ec.europa.eu/eurostat/statistics-explained/index.php/Population_grids](http://ec.europa.eu/eurostat/statistics-explained/index.php/Population_grids) +[^8]: For additional details on the technical specifications and methodologies applied to produce the UA, refer to the report European Commission (2016). +[^9]: [http://land.copernicus.eu/local/urban-atlas/urban-atlas-2012/](http://land.copernicus.eu/local/urban-atlas/urban-atlas-2012/) +[^10]: The Spanish Instituto Geografico Nacional provides free access to a LIDAR data for Spain ([http://pnoa.ign.es](http://pnoa.ign.es)), but this is still a relatively isolated initiative among European countries. + +::: {.tbl-caption} +```{=typst} +#set text(size: 9pt, fill: rgb("#3E6893")) +``` +Table 2. Input data used. +::: + +```{=html} +
| Data category | +Description | +Reference year | +Coverage | +|
| Source | +Residential population | +Type 1: High resolution bottom-up grids (<1km) | +2011* | +Estonia, Finland, Slovenia** | +
| Type 2: Census tracts | +Belgium, Cyprus***, Spain, Italy, Malta, Netherlands, Poland, Portugal, United Kingdom | +|||
| Type 3: Municipal/parish boundaries | +Luxembourg, Latvia | +|||
| Type 4: Medium resolution bottom-up or hybrid grid (1km) (GEOSTAT grid) | +The remainder EU28 + EFTA countries | +|||
| Target | +Urban Atlas polygons (only the polygons presumed to be populated) | +2011-2013 | +EU28 + EFTA | +|
| Ancillary | +European Settlement Map | +2012 | +EU28 + EFTA | +|
| Product Overview | |
|---|---|
| HRL Forest | Active |
) and a NESTED TABLES subsection.
+(v3 added per-cell ALIGNMENT, FONT SIZE handling, and the ALLOWED STYLING guardrail
+over v2 — see the COLORS / ALIGNMENT / FONT SIZE / ALLOWED STYLING subsections.)
+
+## System Instruction
+
+You are an expert technical-document converter. You are given a PDF of a
+Copernicus Land Monitoring Service technical document. Convert it faithfully to
+a single Quarto Markdown (.qmd) document.
+
+PLACEHOLDERS (most important rule):
+Every figure/illustration in this PDF has been replaced by a grey rectangular box
+containing a label of the form FIG_1, FIG_2, FIG_3, … At each such box, output an
+image reference EXACTLY in this form, on its own line:
+
+ 
+