fix: align WALOUS with official class codes

CHANGELOG.md

@@ -15,6 +15,10 @@
   temporal comparison. The map acquires comparable configured editions for
   the same selection so 2020-2023 evolution becomes available without manual
   dataset administration.
+- Corrected WALOUS to the official non-contiguous raster code set
+  `1,2,3,4,5,6,7,8,9,80,90`, including class labels, colours, semantic area
+  aggregation and exact SPW observation ranges. Live provisioning now rejects
+  unknown values without rejecting valid low woody-cover codes 80 and 90.
 - Added the current legal SPW Walloon flood-hazard polygons as a bounded
   authoritative vector product with class-aware hectare metrics and canonical
   persistence. No WMS pixels or modeled depths are fabricated.

backend/README.md

@@ -1613,9 +1613,10 @@ docker exec geointel python /app/scripts/provision_walous_sources.py \
 ```
 
 The provisioner verifies advertised archive sizes, safe ZIP structure,
-EPSG:3812, one band, 1 m cells, class values 1-11 and SHA-256 checksums. It
+EPSG:3812, one band, 1 m cells, the official non-contiguous class codes
-does not run at application startup. `GET .../datasets/walous/products`
+`1,2,3,4,5,6,7,8,9,80,90` and SHA-256 checksums. It does not run at
-therefore reports `source_not_provisioned` until both source files exist.
+application startup. `GET .../datasets/walous/products` therefore reports
+`source_not_provisioned` until both source files exist.
 
 For a bounded Walloon selection the browser persists the latest edition and
 all other configured comparable editions. `POST .../raster/walous/select`
@@ -1623,6 +1624,13 @@ returns cell-area hectares; the temporal API compares the same semantic metric
 keys for 2020 and 2023. WALOUS is land cover, not legal land use, ownership,
 tree count, timber volume or water volume.
 
+The class semantics follow the official raster codes, not display-list
+positions: 1 artificial ground, 2 above-ground construction, 3 railway, 4 bare
+soil, 5 surface water, 6 rotating herbaceous cover, 7 continuous herbaceous
+cover, 8/9 trees above 3 m and 80/90 woody cover up to 3 m. Observation ranges
+are retained from the SPW metadata rather than replaced by arbitrary year-end
+dates.
+
 Settings: `WALOUS_ENABLED`, `WALOUS_SOURCE_DIR`,
 `WALOUS_ANALYSIS_RESOLUTION_M`, `WALOUS_MAX_SIDE_M` and
 `WALOUS_MAX_PIXELS`. The SPW flood polygon adapter uses

backend/app/services/walous_land_cover_service.py

@@ -41,13 +41,15 @@ class WalousProduct:
     download_url: str
     source_sha256_filename: str
     accuracy_label: str
+    observation_start: datetime
+    observation_end: datetime
 
 
 class WalousLandCoverService:
     PROVIDER = "spw_walous_land_cover"
     SOURCE_CRS = "EPSG:3812"
     SOURCE_RESOLUTION_M = 1.0
-    SOURCE_VALUE_UNIT = "class_1_11"
+    SOURCE_VALUE_UNIT = "walous_class_code"
     THEME = "land_cover_use"
     METRIC_KIND = "categorical_area"
     NODATA = 255
@@ -58,31 +60,33 @@ class WalousLandCoverService:
         "geconfigureerde analyseresolutie. Oppervlakten zijn celgebaseerde schattingen; de kaart is landbedekking, "
         "geen juridisch landgebruik, eigendom, boomtelling of actuele terreinwaarneming."
     )
+    # WALOUS has 11 semantic classes, but its official raster codes are not a
+    # continuous 1..11 range. Codes 80 and 90 distinguish low woody cover.
     CLASS_LABELS = {
-        1: "Jaarlijks wisselende kruidlaag",
+        1: "Kunstmatige bodembedekking",
-        2: "Jaarronde kruidlaag",
+        2: "Kunstmatige constructies boven maaiveld",
-        3: "Naaldbomen hoger dan 3 m",
+        3: "Spoorweg",
-        4: "Loofbomen hoger dan 3 m",
+        4: "Kale bodem",
-        5: "Naaldbomen tot 3 m",
+        5: "Oppervlaktewater",
-        6: "Loofbomen tot 3 m",
+        6: "Jaarlijks wisselende kruidlaag",
-        7: "Kale bodem",
+        7: "Jaarronde kruidlaag",
-        8: "Oppervlaktewater",
+        8: "Naaldbomen hoger dan 3 m",
-        9: "Kunstmatige bodembedekking",
+        9: "Loofbomen hoger dan 3 m",
-        10: "Spoorweg",
+        80: "Naaldbomen tot 3 m",
-        11: "Kunstmatige constructies boven maaiveld",
+        90: "Loofbomen tot 3 m",
     }
     CLASS_COLORS = {
-        1: (236, 202, 73),
+        1: (155, 155, 155),
-        2: (161, 201, 78),
+        2: (183, 72, 67),
-        3: (28, 89, 51),
+        3: (68, 68, 68),
-        4: (52, 132, 72),
+        4: (194, 165, 119),
-        5: (78, 125, 70),
+        5: (44, 129, 185),
-        6: (107, 164, 87),
+        6: (236, 202, 73),
-        7: (194, 165, 119),
+        7: (161, 201, 78),
-        8: (44, 129, 185),
+        8: (28, 89, 51),
-        9: (155, 155, 155),
+        9: (52, 132, 72),
-        10: (68, 68, 68),
+        80: (78, 125, 70),
-        11: (183, 72, 67),
+        90: (107, 164, 87),
     }
 
     @staticmethod
@@ -101,6 +105,8 @@ class WalousLandCoverService:
                 ),
                 source_sha256_filename="walous_land_cover_2020_3812.sha256",
                 accuracy_label="Officiele globale nauwkeurigheid 83,30%",
+                observation_start=datetime(2020, 4, 1, tzinfo=UTC),
+                observation_end=datetime(2020, 4, 24, 23, 59, 59, tzinfo=UTC),
             ),
             WalousProduct(
                 key="walous_land_cover_2023",
@@ -115,6 +121,8 @@ class WalousLandCoverService:
                 ),
                 source_sha256_filename="walous_land_cover_2023_3812.sha256",
                 accuracy_label="Officiele globale nauwkeurigheid 87,10%",
+                observation_start=datetime(2023, 5, 27, tzinfo=UTC),
+                observation_end=datetime(2023, 6, 25, 23, 59, 59, tzinfo=UTC),
             ),
         )
         return {product.key: product for product in products}
@@ -145,8 +153,8 @@ class WalousLandCoverService:
                     catalog_url=product.catalog_url,
                     attribution=WalousLandCoverService.ATTRIBUTION,
                     license_note=WalousLandCoverService.LICENSE_NOTE,
-                    legend_min_label="WALOUS klasse 1",
+                    legend_min_label="WALOUS klasse 1 (kunstmatige bodem)",
-                    legend_max_label="WALOUS klasse 11",
+                    legend_max_label="WALOUS klasse 90 (loofbomen tot 3 m)",
                     included_source_values=list(WalousLandCoverService.CLASS_LABELS),
                     limitation_message=f"{WalousLandCoverService.LIMITATION} {product.accuracy_label}.",
                     coverage_zones=["wallonia"],
@@ -250,7 +258,12 @@ class WalousLandCoverService:
                 classes = set(np.unique(valid).astype(int).tolist())
                 unexpected = sorted(classes - set(WalousLandCoverService.CLASS_LABELS))
                 if unexpected:
-                    raise AppError(code="WALOUS_SOURCE_INVALID_VALUES", message="WALOUS contains classes outside the governed 1-11 legend", details={"unexpected_classes": unexpected}, status_code=409)
+                    raise AppError(
+                        code="WALOUS_SOURCE_INVALID_VALUES",
+                        message="WALOUS contains classes outside the governed 11-class code set",
+                        details={"unexpected_classes": unexpected},
+                        status_code=409,
+                    )
                 profile = {
                     "driver": "GTiff",
                     "width": width,
@@ -345,7 +358,7 @@ class WalousLandCoverService:
         source_sha256_path = source_path.with_name(product.source_sha256_filename)
         source_sha256 = source_sha256_path.read_text(encoding="ascii").strip().split()[0] if source_sha256_path.is_file() else None
         acquired_at = datetime.now(UTC)
-        observed_at = datetime(product.observation_year, 12, 31, 23, 59, 59, tzinfo=UTC)
+        observed_at = product.observation_end
         spatial_series_hash = hashlib.sha256(json.dumps({"bbox": identity["bbox_epsg4326"], "area_id": identity["area_id"], "resolution": identity["analysis_resolution_m"]}, sort_keys=True).encode()).hexdigest()[:24]
         dataset = DatasetService.import_raster_bytes(
             db,
@@ -357,8 +370,8 @@ class WalousLandCoverService:
             source_name=WalousLandCoverService.PROVIDER,
             temporal_series_key=f"spw:walous:land-cover:{spatial_series_hash}",
             observed_at=observed_at,
-            valid_from=datetime(product.observation_year, 1, 1, tzinfo=UTC),
+            valid_from=product.observation_start,
-            valid_to=observed_at,
+            valid_to=product.observation_end,
             temporal_granularity="year",
             source_version=product.source_version,
             source_metadata={
@@ -374,6 +387,8 @@ class WalousLandCoverService:
                 "source_value_unit": WalousLandCoverService.SOURCE_VALUE_UNIT,
                 "class_labels": WalousLandCoverService.CLASS_LABELS,
                 "observation_year": product.observation_year,
+                "observation_start": product.observation_start.isoformat(),
+                "observation_end": product.observation_end.isoformat(),
                 "valid_pixel_count": validation["valid_pixel_count"],
                 "classes_present": validation["classes_present"],
                 "bbox_epsg4326": bbox_4326,
@@ -480,12 +495,12 @@ class WalousLandCoverService:
 
         metric_specs = [
             ("land_cover_observed_area_ha", "Gekarteerde landbedekking", set(WalousLandCoverService.CLASS_LABELS)),
-            ("forest_cover_area_ha", "Boom- en bosbedekking", {3, 4, 5, 6}),
+            ("forest_cover_area_ha", "Boom- en bosbedekking", {8, 9, 80, 90}),
-            ("surface_water_area_ha", "Oppervlaktewater", {8}),
+            ("surface_water_area_ha", "Oppervlaktewater", {5}),
-            ("artificial_cover_area_ha", "Kunstmatige bedekking en constructies", {9, 10, 11}),
+            ("artificial_cover_area_ha", "Kunstmatige bedekking en constructies", {1, 2, 3}),
-            ("annual_herbaceous_cover_area_ha", "Jaarlijks wisselende kruidlaag", {1}),
+            ("annual_herbaceous_cover_area_ha", "Jaarlijks wisselende kruidlaag", {6}),
-            ("permanent_herbaceous_cover_area_ha", "Jaarronde kruidlaag", {2}),
+            ("permanent_herbaceous_cover_area_ha", "Jaarronde kruidlaag", {7}),
-            ("bare_soil_area_ha", "Kale bodem", {7}),
+            ("bare_soil_area_ha", "Kale bodem", {4}),
         ]
         metrics = [
             ThematicRasterMetric(

backend/tests/test_walous_land_cover_service.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from datetime import datetime, timezone
+import importlib.util
 from pathlib import Path
 from types import SimpleNamespace
 from uuid import uuid4
@@ -22,6 +23,15 @@ from app.services.temporal_analysis_service import TemporalAnalysisService
 from app.services.walous_land_cover_service import WalousLandCoverService
 
 
+def load_provisioner():
+    path = Path(__file__).resolve().parents[2] / "scripts" / "provision_walous_sources.py"
+    spec = importlib.util.spec_from_file_location("walous_source_provisioner_test", path)
+    assert spec and spec.loader
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
 class FakeQuery:
     def filter(self, *_args):
         return self
@@ -70,16 +80,15 @@ def make_source(path: Path) -> tuple[list[float], np.ndarray]:
     to_4326 = Transformer.from_crs("EPSG:3812", "EPSG:4326", always_xy=True)
     x, y = to_3812.transform(4.85, 50.45)
     transform = from_origin(x, y + 100, 1, 1)
-    values = np.ones((100, 100), dtype="uint8")
+    class_codes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 80, 90]
-    values[:, 20:40] = 4
+    values = np.empty((100, len(class_codes) * 20), dtype="uint8")
-    values[:, 40:50] = 8
+    for index, class_code in enumerate(class_codes):
-    values[:, 50:70] = 9
+        values[:, index * 20 : (index + 1) * 20] = class_code
-    values[:, 70:] = 2
     with rasterio.open(
         path,
         "w",
         driver="GTiff",
-        width=100,
+        width=values.shape[1],
         height=100,
         count=1,
         dtype="uint8",
@@ -89,7 +98,7 @@ def make_source(path: Path) -> tuple[list[float], np.ndarray]:
     ) as target:
         target.write(values, 1)
     min_lon, min_lat = to_4326.transform(x, y)
-    max_lon, max_lat = to_4326.transform(x + 100, y + 100)
+    max_lon, max_lat = to_4326.transform(x + values.shape[1], y + values.shape[0])
     return [min_lon, min_lat, max_lon, max_lat], values
 
 
@@ -113,6 +122,17 @@ def test_walous_registry_reports_real_provisioning_state(tmp_path: Path) -> None
     assert after["walous_land_cover_2023"]["native_resolution_m"] == 1.0
     assert after["walous_land_cover_2023"]["analysis_resolution_m"] == 10.0
     assert after["walous_land_cover_2023"]["coverage_zones"] == ["wallonia"]
+    assert after["walous_land_cover_2023"]["included_source_values"] == [1, 2, 3, 4, 5, 6, 7, 8, 9, 80, 90]
+    assert after["walous_land_cover_2023"]["source_value_unit"] == "walous_class_code"
+
+
+def test_walous_provisioner_accepts_official_non_contiguous_class_codes(tmp_path: Path) -> None:
+    source_path = tmp_path / "walous_land_cover_2023_3812.tif"
+    make_source(source_path)
+
+    validation = load_provisioner().validate_raster(source_path)
+
+    assert validation["sample_classes"] == [1, 2, 3, 4, 5, 6, 7, 8, 9, 80, 90]
 
 
 def test_walous_acquisition_reads_real_classes_and_persists_provenance(tmp_path: Path, monkeypatch) -> None:
@@ -141,10 +161,12 @@ def test_walous_acquisition_reads_real_classes_and_persists_provenance(tmp_path:
     assert result["output_dataset_id"] == str(output_id)
     assert result["resolution_m"] == 10
     assert captured["source_name"] == "spw_walous_land_cover"
-    assert captured["source_metadata"]["classes_present"] == [1, 2, 4, 8, 9]
+    assert captured["source_metadata"]["classes_present"] == [1, 2, 3, 4, 5, 6, 7, 8, 9, 80, 90]
     assert captured["provenance_metadata"]["resampling"] == "nearest"
     assert captured["temporal_series_key"].startswith("spw:walous:land-cover:")
-    assert captured["observed_at"].year == 2023
+    assert captured["observed_at"].date().isoformat() == "2023-06-25"
+    assert captured["valid_from"].date().isoformat() == "2023-05-27"
+    assert captured["valid_to"] == captured["observed_at"]
 
 
 def test_walous_analysis_returns_semantic_area_metrics(tmp_path: Path, monkeypatch) -> None:
@@ -193,6 +215,9 @@ def test_walous_analysis_returns_semantic_area_metrics(tmp_path: Path, monkeypat
     assert metrics["forest_cover_area_ha"] > 0
     assert metrics["surface_water_area_ha"] > 0
     assert metrics["artificial_cover_area_ha"] > 0
+    assert metrics["annual_herbaceous_cover_area_ha"] > 0
+    assert metrics["permanent_herbaceous_cover_area_ha"] > 0
+    assert metrics["bare_soil_area_ha"] > 0
     assert "water_volume" in result["unsupported_metrics"]
 
 

docs/API_CONTRACTS.md

@@ -494,10 +494,12 @@ becomes true only when the checksum-validated source GeoTIFF exists below
 
 Reads a bounded window from one provisioned official 1 m WALOUS source,
 applies nearest-neighbour resampling to the configured analysis resolution,
-masks `bbox intersect Area`, validates class values 1-11 and persists a normal
+masks `bbox intersect Area`, validates the official non-contiguous class-code
-raster Dataset through `DatasetService`. URLs, paths, classes and resolutions
+set `1, 2, 3, 4, 5, 6, 7, 8, 9, 80, 90` and persists a normal raster Dataset
-are not caller-controlled. Equal spatial requests for 2020 and 2023 share one
+through `DatasetService`. URLs, paths, classes and resolutions are not
-temporal series key.
+caller-controlled. Equal spatial requests for 2020 and 2023 share one temporal
+series key. The exact official observation ranges are retained as
+2020-04-01/2020-04-24 and 2023-05-27/2023-06-25.
 
 ### POST `/api/v1/projects/{project_id}/datasets/{dataset_id}/raster/walous/select`
 

docs/CODEX_EXECUTION_LOG.md

@@ -3654,6 +3654,12 @@ Validation:
 
 ## Post-V1 national coverage completion: Wallonia (2026-07-22)
 
+- Live Tower provisioning exposed an incorrect assumption that 11 WALOUS
+  classes implied numeric codes 1 through 11. The official SPW legend and the
+  downloaded 2020 raster confirm codes `1,2,3,4,5,6,7,8,9,80,90`. Corrected
+  validation, class semantics, colours and all hectare aggregations; added a
+  provisioner regression containing codes 80/90 and retained exact source
+  observation ranges.
 - Validated the official WALOUS 2020 and 2023 archives, their EPSG:3812 1 m
   raster contract, 11 classes, CC BY 4.0 attribution and published edition
   accuracy. Added a fail-closed operator provisioner with archive-size,

docs/DATA_SOURCES.md

@@ -790,6 +790,27 @@ Areas. They persist only through `DatasetService`; the browser never contacts
 either provider directly. Cross-zone selections remain split by authority and
 metric semantics.
 
+## Wallonia WALOUS land cover
+
+The official SPW `WAL_OCS_IA__2020` and `WAL_OCS_IA__2023` GeoTIFF archives
+provide comparable Walloon land-cover observations at native 1 m resolution in
+EPSG:3812. Runtime analysis reads only bounded windows from operator-
+provisioned, checksum-recorded source files and uses nearest-neighbour
+resampling for the governed 10 m analysis derivative.
+
+The 11 semantic classes use the non-contiguous source codes `1, 2, 3, 4, 5, 6,
+7, 8, 9, 80, 90`. In order these mean artificial ground, above-ground
+construction, railway, bare soil, surface water, rotating herbaceous cover,
+continuous herbaceous cover, conifer trees above 3 m, deciduous trees above 3
+m, conifer woody cover up to 3 m and deciduous woody cover up to 3 m. Codes 80
+and 90 must never be normalized to invented classes 10 and 11.
+
+The official temporal extents are 2020-04-01 through 2020-04-24 and 2023-05-27
+through 2023-06-25. Metrics are estimated hectares from classified cells. They
+are not legal land use, ownership, individual tree counts, timber volume or
+water volume. The official catalogue reports overall accuracy per edition and
+also warns that accuracy varies by class and place.
+
 ## Bathymetry, inland profiles and maritime scope
 
 The official VHA Digital Atlas profile-point layer is the first operational

docs/DATA_SPECIFICATION.md

@@ -308,6 +308,15 @@ review hashes, persists sampled official flight dates as the temporal evidence
 range and creates a new Dataset/DatasetVersion through DatasetService. It does
 not retroactively rewrite or delete legacy rows.
 
+WALOUS land-cover rasters retain EPSG:3812, their native 1 m source resolution,
+the derived analysis resolution, source checksum and exact observation range.
+The governed class domain is `{1,2,3,4,5,6,7,8,9,80,90}`. Codes 80 and 90 are
+valid official low-woody-cover classes; `10` and `11` are not substitutes.
+Bounded derivatives use `uint8`, nodata 255 and nearest-neighbour resampling.
+Area metrics group forest/tree cover as `{8,9,80,90}`, water as `{5}`,
+artificial cover as `{1,2,3}`, rotating herbaceous cover as `{6}`, continuous
+herbaceous cover as `{7}` and bare soil as `{4}`.
+
 ### Hydrological station observations
 
 Waterinfo observations are persisted as EPSG:4326 Point features, one station

scripts/provision_walous_sources.py

@@ -33,6 +33,7 @@ SOURCES = {
 }
 MAX_ARCHIVE_BYTES = 1_000_000_000
 MAX_EXTRACTED_BYTES = 50_000_000_000
+WALOUS_CLASS_CODES = {1, 2, 3, 4, 5, 6, 7, 8, 9, 80, 90}
 
 
 def sha256_file(path: Path) -> str:
@@ -110,9 +111,9 @@ def validate_raster(path: Path) -> dict:
         sample_width = min(2048, source.width)
         sample = source.read(1, out_shape=(sample_height, sample_width), masked=True, resampling=Resampling.nearest)
         values = np.unique(sample.compressed()).astype(int).tolist()
-        unexpected = sorted(set(values) - set(range(1, 12)))
+        unexpected = sorted(set(values) - WALOUS_CLASS_CODES)
         if unexpected:
-            raise RuntimeError(f"WALOUS sample contains classes outside 1-11: {unexpected}")
+            raise RuntimeError(f"WALOUS sample contains classes outside the official 11-class code set: {unexpected}")
         return {
             "path": str(path),
             "crs": str(source.crs),