Add web map with zone generation API, side job queue, and restore historical DTM hole rendering

- webapp.py: FastAPI serving the continuous map (port 8973) with
  /api/preview, /api/generate and /api/status; tiles are downloaded
  from IGN and processed in a logged subprocess, tracked live in a
  side "File de génération" panel that survives page reloads
- fetch_ign.py: download missing 1 km LiDAR HD tiles from the IGN
  geoplateforme before processing
- index.py: tile thumbnails and 500 m subtiles are now invalidated by
  mtime so regenerating a tile refreshes its cached images; progress
  logging per tile
- dtm.py: back to the historical gap handling (small gaps filled by
  fillnodata only, larger holes left as nodata rendered black);
  lowest-return floor only via --bare-earth, IGN class selection via
  --ign-classes
- cli.py: positional input now optional (--rebuild-index works alone)
- docker-compose.yml: serve (GPU, port 8973) and process services;
  launch via docker compose only (documented in AGENTS.md/AGENTS.md)
- tests: 131 passing, incl. regressions for thumbnail staleness,
  --rebuild-index without input, and nodata rendering

lidar_pipeline/tests/test_visualizations.py

@@ -157,3 +157,61 @@ class TestRayTrace:
         )
         assert pos.shape == (4, 2, rows, cols)
         assert neg.shape == (4, 2, rows, cols)
+
+
+class TestNodataPreserved:
+    """Nodata préservé dans les rendus (comportement historique).
+
+    Les trous du MNT sont comblés en amont (create_dtm_fast, tous modes) ;
+    si un nodata subsiste malgré tout, hillshade/slope/aspect le restituent
+    (rendu noir en carte) au lieu d'inventer des valeurs interpolées.
+    """
+
+    @staticmethod
+    def _dem_with_hole(synthetic_dem, tmp_path):
+        import rasterio
+        with rasterio.open(synthetic_dem) as src:
+            arr = src.read(1).copy()
+            profile = src.profile.copy()
+        arr[80:120, 80:120] = np.nan
+        dem_hole = tmp_path / "dem_hole.tif"
+        profile.update(dtype='float32', nodata=float('nan'))
+        with rasterio.open(dem_hole, 'w', **profile) as dst:
+            dst.write(arr.astype('float32'), 1)
+        return dem_hole
+
+    def test_aspect_solo_preserves_nodata(self, synthetic_dem, tmp_path):
+        from lidar_pipeline.visualizations import generate_aspect
+        dem_hole = self._dem_with_hole(synthetic_dem, tmp_path)
+        out = generate_aspect(dem_hole, "solo", tmp_path, 5.0)
+        assert out is not None and out.exists()
+        import rasterio
+        with rasterio.open(out) as src:
+            data = src.read(1)
+        assert np.isnan(data[80:120, 80:120]).all(), "le trou doit rester en nodata"
+        # Le gradient au bord du trou propage NaN sur un anneau de 1 px :
+        # on vérifie une zone éloignée du trou
+        assert not np.isnan(data[0:40, 0:40]).any(), "NaN loin du trou"
+
+    def test_aspect_shared_preserves_nodata(self, synthetic_dem, tmp_path):
+        from lidar_pipeline.visualizations import SharedDEM, generate_aspect
+        dem_hole = self._dem_with_hole(synthetic_dem, tmp_path)
+        shared = SharedDEM(dem_hole, 5.0)
+        out = generate_aspect(dem_hole, "partage", tmp_path, 5.0, shared=shared)
+        assert out is not None and out.exists()
+        import rasterio
+        with rasterio.open(out) as src:
+            data = src.read(1)
+        assert np.isnan(data[80:120, 80:120]).all(), "le trou doit rester en nodata"
+        assert not np.isnan(data[0:40, 0:40]).any(), "NaN loin du trou"
+
+    def test_slope_and_hillshade_preserve_nodata(self, synthetic_dem, tmp_path):
+        from lidar_pipeline.visualizations import generate_slope, generate_hillshade
+        dem_hole = self._dem_with_hole(synthetic_dem, tmp_path)
+        import rasterio
+        for gen, name in ((generate_slope, "p"), (generate_hillshade, "h")):
+            out = gen(dem_hole, name, tmp_path, 5.0)
+            assert out is not None and out.exists()
+            with rasterio.open(out) as src:
+                data = src.read(1)
+            assert np.isnan(data[80:120, 80:120]).any(), f"{out.name} : trou disparu"