fix: read signed WALOUS rasters safely

backend/app/services/walous_land_cover_service.py

@@ -247,7 +247,10 @@ class WalousLandCoverService:
                 band = source.read(1, window=window, out_shape=(height, width), masked=True, resampling=Resampling.nearest)
                 output_transform = from_bounds(*bounds, width, height)
                 outside_scope = geometry_mask([mapping(clipped_geometry)], out_shape=(height, width), transform=output_transform, invert=False)
-                raw = np.asarray(band.filled(WalousLandCoverService.NODATA), dtype="uint8")
+                # The official 2023 GeoTIFF is signed int8 while GDAL exposes
+                # its nodata sentinel as 255. Filling before widening would
+                # therefore reject the sentinel as out of range for int8.
+                raw = np.asarray(np.ma.getdata(band), dtype="uint8")
                 invalid = np.ma.getmaskarray(band) | outside_scope
                 if source.nodata is not None:
                     invalid |= np.isclose(raw.astype("float64"), float(source.nodata))
@@ -476,7 +479,7 @@ class WalousLandCoverService:
                     raise AppError(code="WALOUS_SELECTION_OUTSIDE_DATASET", message="Selection does not overlap the persisted WALOUS raster", status_code=422)
                 clipped, transform = mask(source, [mapping(geometry)], crop=True, filled=False, indexes=[1])
                 band = np.ma.asarray(clipped[0])
-                raw = np.asarray(band.filled(WalousLandCoverService.NODATA), dtype="uint8")
+                raw = np.asarray(np.ma.getdata(band), dtype="uint8")
                 selected = geometry_mask([mapping(geometry)], out_shape=raw.shape, transform=transform, invert=True)
                 valid = selected & ~np.ma.getmaskarray(band) & (raw != WalousLandCoverService.NODATA)
                 values = raw[valid]
@@ -553,7 +556,7 @@ class WalousLandCoverService:
             scale = min(1.0, max_dimension / max(source.width, source.height))
             width, height = max(1, round(source.width * scale)), max(1, round(source.height * scale))
             values = source.read(1, out_shape=(height, width), masked=True, resampling=Resampling.nearest)
-            raw = np.asarray(values.filled(WalousLandCoverService.NODATA), dtype="uint8")
+            raw = np.asarray(np.ma.getdata(values), dtype="uint8")
             rgba = np.zeros((height, width, 4), dtype="uint8")
             for value, color in WalousLandCoverService.CLASS_COLORS.items():
                 selected = raw == value

backend/tests/test_walous_land_cover_service.py

@@ -75,13 +75,18 @@ class FakeSession:
         return row
 
 
-def make_source(path: Path) -> tuple[list[float], np.ndarray]:
+def make_source(
+    path: Path,
+    *,
+    dtype: str = "uint8",
+    nodata: int = 255,
+) -> tuple[list[float], np.ndarray]:
     to_3812 = Transformer.from_crs("EPSG:4326", "EPSG:3812", always_xy=True)
     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)
     class_codes = [1, 2, 3, 4, 5, 6, 7, 8, 9, 80, 90]
-    values = np.empty((100, len(class_codes) * 20), dtype="uint8")
+    values = np.empty((100, len(class_codes) * 20), dtype=dtype)
     for index, class_code in enumerate(class_codes):
         values[:, index * 20 : (index + 1) * 20] = class_code
     with rasterio.open(
@@ -91,10 +96,10 @@ def make_source(path: Path) -> tuple[list[float], np.ndarray]:
         width=values.shape[1],
         height=100,
         count=1,
-        dtype="uint8",
+        dtype=dtype,
         crs="EPSG:3812",
         transform=transform,
-        nodata=255,
+        nodata=nodata,
     ) as target:
         target.write(values, 1)
     min_lon, min_lat = to_4326.transform(x, y)
@@ -169,6 +174,40 @@ def test_walous_acquisition_reads_real_classes_and_persists_provenance(tmp_path:
     assert captured["valid_to"] == captured["observed_at"]
 
 
+def test_walous_acquisition_accepts_official_signed_int8_nodata(tmp_path: Path, monkeypatch) -> None:
+    bbox, _values = make_source(
+        tmp_path / "walous_land_cover_2023_3812.tif",
+        dtype="int8",
+        nodata=-128,
+    )
+    project = Project(id=uuid4(), name="Belgium")
+    output_id = uuid4()
+    captured = {}
+
+    def persist(_db, **kwargs):
+        captured.update(kwargs)
+        return SimpleNamespace(id=output_id)
+
+    monkeypatch.setattr(DatasetService, "import_raster_bytes", persist)
+    result = WalousLandCoverService.acquire(
+        FakeSession(project),
+        project.id,
+        ThematicRasterAcquireRequest(
+            bbox={"min_x": bbox[0], "min_y": bbox[1], "max_x": bbox[2], "max_y": bbox[3], "crs": "EPSG:4326"},
+            product_key="walous_land_cover_2023",
+            force_refresh=True,
+        ),
+        settings=settings(tmp_path),
+    )
+
+    assert result["output_dataset_id"] == str(output_id)
+    assert captured["source_metadata"]["classes_present"] == [1, 2, 3, 4, 5, 6, 7, 8, 9, 80, 90]
+    with rasterio.MemoryFile(captured["content"]) as memory:
+        with memory.open() as derived:
+            assert derived.dtypes == ("uint8",)
+            assert derived.nodata == 255
+
+
 def test_walous_analysis_returns_semantic_area_metrics(tmp_path: Path, monkeypatch) -> None:
     bbox, _values = make_source(tmp_path / "walous_land_cover_2023_3812.tif")
     project = Project(id=uuid4(), name="Belgium")