Add dated regional imagery training sources

backend/app/services/orthophoto_acquisition_service.py

@@ -96,6 +96,27 @@ class OrthophotoAcquisitionService:
                     coverage_zone="wallonia",
                     supports_detection=True,
                 ),
+                OrthophotoProduct(
+                    key="wallonia_2024",
+                    display_name="Orthofoto Wallonië 2024",
+                    observation_label="6 april tot 21 september 2024",
+                    temporal_granularity="period",
+                    native_resolution_m=0.25,
+                    wms_url="https://geoservices.wallonie.be/arcgis/services/IMAGERIE/ORTHO_2024/MapServer/WMSServer",
+                    layer="0",
+                    catalog_url="https://geoportail.wallonie.be/catalogue/a12b5915-e56e-4827-8e4c-1f774934a2b1.html",
+                    limitation_message="Officiële SPW-campagne 2024; dekking is gedeeltelijk en de exacte vliegdatum moet uit het officiële tuilagebestand worden afgeleid.",
+                    provider="spw_orthophoto",
+                    source_label="SPW ORTHO_2024 WMS",
+                    attribution="Bron: Service public de Wallonie (SPW), Orthophotos 2024",
+                    license_note="CC BY 4.0; citeer SPW en vermeld wijzigingen.",
+                    series_namespace="spw",
+                    coverage_zone="wallonia",
+                    supports_detection=True,
+                    observed_at=datetime(2024, 4, 6, tzinfo=UTC),
+                    valid_from=datetime(2024, 4, 6, tzinfo=UTC),
+                    valid_to=datetime(2024, 9, 21, 23, 59, 59, tzinfo=UTC),
+                ),
                 OrthophotoProduct(
                     key="brussels_latest",
                     display_name="Meest recente orthofoto Brussel",
@@ -114,6 +135,27 @@ class OrthophotoAcquisitionService:
                     coverage_zone="brussels",
                     supports_detection=True,
                 ),
+                OrthophotoProduct(
+                    key="brussels_2025",
+                    display_name="Winterorthofoto Brussel 2025",
+                    observation_label="Wintervluchten 2025",
+                    temporal_granularity="year",
+                    native_resolution_m=0.15,
+                    wms_url=OrthophotoAcquisitionService.HISTORICAL_WINTER_WMS_URL,
+                    layer="OMWRGB25VL",
+                    catalog_url="https://www.vlaanderen.be/datavindplaats/catalogus/orthofotomozaiek-middenschalig-winteropnamen-kleur-2025-vlaanderen",
+                    limitation_message="Officiële jaargang 2025 voor Vlaanderen en Brussel; de exacte vliegdag is beschikbaar via de afzonderlijke vliegdagcontour.",
+                    provider="digitaal_vlaanderen_orthophoto",
+                    source_label="Digitaal Vlaanderen OMWRGB25VL WMS",
+                    attribution="Bron: Orthofotomozaïek Vlaanderen en Brussel 2025, Digitaal Vlaanderen",
+                    license_note="Gebruik volgens het gebruiksrecht geografische webdiensten van Digitaal Vlaanderen.",
+                    series_namespace="digitaal-vlaanderen",
+                    coverage_zone="brussels",
+                    supports_detection=True,
+                    observed_at=datetime(2025, 1, 1, tzinfo=UTC),
+                    valid_from=datetime(2025, 1, 1, tzinfo=UTC),
+                    valid_to=datetime(2025, 12, 31, 23, 59, 59, tzinfo=UTC),
+                ),
             ]
         )
         for year in range(2025, 2011, -1):
@@ -134,6 +176,7 @@ class OrthophotoAcquisitionService:
                     observed_at=datetime(year, 1, 1, tzinfo=UTC),
                     valid_from=datetime(year, 1, 1, tzinfo=UTC),
                     valid_to=datetime(year, 12, 31, 23, 59, 59, tzinfo=UTC),
+                    supports_detection=year == 2025,
                 )
             )
         for key, start_year, end_year, layer in (

backend/tests/test_sprint196_map_orthophoto_analysis.py

@@ -169,7 +169,7 @@ def test_orthophoto_product_registry_exposes_only_governed_official_layers() ->
     assert {"2025", "2012", "2008_2011", "2000_2003", "1979_1990", "1971"}.issubset(keys)
     assert next(item for item in products if item["key"] == "most_recent")["supports_detection"] is True
     detection_keys = {item["key"] for item in products if item["supports_detection"]}
-    assert detection_keys == {"most_recent", "wallonia_latest", "brussels_latest"}
+    assert {"most_recent", "wallonia_latest", "wallonia_2024", "brussels_latest", "brussels_2025", "2025"} <= detection_keys
     by_key = {item["key"]: item for item in products}
     assert by_key["wallonia_latest"]["provider"] == "spw_orthophoto"
     assert by_key["wallonia_latest"]["coverage_zone"] == "wallonia"

scripts/assemble_belgium_building_corpus.py

@@ -28,9 +28,9 @@ from app.models import Dataset  # noqa: E402
 from normalize_belgium_building_labels import normalize  # noqa: E402
 
 REGION_SOURCES = {
-    "flanders": ("digitaal_vlaanderen_orthophoto", "grb"),
-    "wallonia": ("spw_orthophoto", "spw_picc"),
-    "brussels": ("urbis_orthophoto", "urbis"),
+    "flanders": ({"digitaal_vlaanderen_orthophoto"}, "grb"),
+    "wallonia": ({"spw_orthophoto"}, "spw_picc"),
+    "brussels": ({"urbis_orthophoto", "digitaal_vlaanderen_orthophoto"}, "urbis"),
 }
 SPLITS = {"train", "val", "calibration", "test", "background-test"}
 
@@ -56,8 +56,8 @@ def _validate_pair(sample: dict[str, Any], raster: Dataset, reference: Dataset)
     region = str(sample.get("region") or "").lower()
     if region not in REGION_SOURCES:
         raise SystemExit(f"Unsupported region for {sample.get('sample_slug')}: {region}")
-    expected_raster, expected_reference = REGION_SOURCES[region]
-    if raster.source_name != expected_raster:
+    expected_rasters, expected_reference = REGION_SOURCES[region]
+    if raster.source_name not in expected_rasters:
         raise SystemExit(f"Raster provider mismatch for {sample['sample_slug']}: {raster.source_name}")
     if reference.source_name != expected_reference or reference.reference_layer_name != "buildings":
         raise SystemExit(f"Reference provider/layer mismatch for {sample['sample_slug']}")
@@ -156,6 +156,7 @@ def main() -> int:
                 {
                     "sample_slug": slug,
                     "sample_role": sample.get("sample_role", "positive"),
+                    "require_empty": bool(sample.get("require_empty", False)),
                     "region": region,
                     "context": sample.get("context"),
                     "split": sample["split"],

scripts/evaluate_belgium_building_candidate.py

@@ -88,7 +88,13 @@ def main() -> int:
     manifest = json.loads(args.corpus_manifest.read_text(encoding="utf-8"))
     regions = {item["sample_slug"]: item["region"] for item in manifest["samples"]}
     pure_empty_slugs = {
-        item["sample_slug"] for item in manifest["samples"] if bool(item.get("require_empty"))
+        item["sample_slug"]
+        for item in manifest["samples"]
+        if bool(item.get("require_empty"))
+        or (
+            item.get("sample_role") == "background_candidate"
+            and int(item.get("reference_feature_count") or 0) == 0
+        )
     }
     tiles = [item for item in summary["tiles"] if item.get("kept", True) and item["split"] == args.split]
     image_paths = [item["image_path"] for item in tiles]

scripts/provision_belgium_building_training_portfolio.py

@@ -94,19 +94,19 @@ AOIS = (
 REGION_CONTRACT = {
     "flanders": {
         "area_id": "2a1a064e-cfd6-46f4-981a-d6ffa0ed5b5e",
-        "orthophoto_product": "most_recent",
+        "orthophoto_product": "2025",
         "reference_path": "datasets/grb/acquire",
         "reference_product": "buildings",
     },
     "wallonia": {
         "area_id": "e5fd742a-ca18-4520-abe0-d28416aa2ece",
-        "orthophoto_product": "wallonia_latest",
+        "orthophoto_product": "wallonia_2024",
         "reference_path": "datasets/official-vector/acquire",
         "reference_product": "spw_picc_buildings",
     },
     "brussels": {
         "area_id": "304b413d-d81c-40fc-922e-49907b8efaaa",
-        "orthophoto_product": "brussels_latest",
+        "orthophoto_product": "brussels_2025",
         "reference_path": "datasets/official-vector/acquire",
         "reference_product": "urbis_buildings",
     },