fix(build_csv): fail loud on geographic DSM CRS and missing DSM coverage

src/lib/build_csv.py

@@ -171,6 +171,15 @@ def align_features_to_dsm(self, features, dsm):
                 f"Warning: Features CRS ({features.crs}) does not match DSM CRS ({dsm_crs}). Reprojecting features to DSM CRS."
             )
             features = features.to_crs(dsm_crs)
+        # Distances and buffers below assume metres. A geographic DSM CRS
+        # (degrees) silently truncates the x1000-scaled distance matrix to zero
+        # integers, yielding an arbitrary TSP route. Fail loud instead.
+        if features.crs is not None and not features.crs.is_projected:
+            raise ValueError(
+                f"DSM CRS ({dsm_crs}) is geographic (degrees). A projected CRS "
+                "(e.g. UTM) is required so distances and buffers are in metres. "
+                "Reproject the DSM to a projected CRS and retry."
+            )
         return features
 
     # -------------------------------------------------------------------------
@@ -209,6 +218,15 @@ def extract_features_elevations(self, features):
 
         stats = zonal_stats(gdf_buffers, self.dsm_path, stats=["max"])
         max_elev = [s["max"] for s in stats]
+        # zonal_stats returns max=None where a buffer falls entirely on DSM
+        # nodata (extent edge / hole). Left unguarded, None flows to the CSV as
+        # "None" and later float("None") crashes mission generation. Fail loud.
+        missing = [features.iloc[i].get("point_id", i) for i, v in enumerate(max_elev) if v is None]
+        if missing:
+            raise ValueError(
+                f"DSM has no data under {len(missing)} feature buffer(s) "
+                f"(point_id {missing[:10]}). Increase DSM coverage or reduce buffer_feature."
+            )
         features = features.copy()
         features["elev"] = max_elev
         return features
@@ -340,6 +358,11 @@ def extract_path_checkpoints(self, ordered_features):
             # Use zonal_stats to get max elevation in buffer
             stats = zonal_stats(buffer_gdf, self.dsm_path, stats=["max"])
             max_elev = stats[0]["max"]
+            if max_elev is None:
+                raise ValueError(
+                    f"DSM has no data under the path buffer between waypoints {i} and "
+                    f"{i + 1}. Increase DSM coverage or reduce buffer_path."
+                )
             # Get centroid of buffer for checkpoint
             centroid = buffer_gdf.centroid.iloc[0]
             checkpoints.append(

tests/test_build_csv_dsm.py

@@ -0,0 +1,60 @@
+"""DSM-coverage guards in BuildCSV — fail loud instead of flying a bad mission.
+
+Two safety guards:
+* a geographic DSM CRS would silently truncate the integer distance matrix to
+  zero and yield an arbitrary route;
+* a feature/path buffer over DSM nodata yields max=None, which otherwise
+  reaches the CSV as "None" and crashes mission generation on float("None").
+"""
+
+from types import SimpleNamespace
+
+import geopandas as gpd
+import numpy as np
+import pytest
+import rasterio
+from rasterio.transform import from_origin
+from shapely.geometry import Point
+
+from src.lib.build_csv import BuildCSV
+
+
+def test_geographic_dsm_crs_raises():
+    feats = gpd.GeoDataFrame(
+        {"point_id": [1, 2]},
+        geometry=[Point(-74.0, 45.0), Point(-74.001, 45.001)],
+        crs="EPSG:4326",
+    )
+    dsm = SimpleNamespace(crs=rasterio.crs.CRS.from_epsg(4326))  # geographic
+    with pytest.raises(ValueError, match="geographic"):
+        BuildCSV("f", "d").align_features_to_dsm(feats, dsm)
+
+
+def test_missing_dsm_coverage_raises(tmp_path):
+    # all-nodata projected DSM; any feature buffer over it -> max=None
+    dsm_path = tmp_path / "nodata.tif"
+    nodata = -9999.0
+    transform = from_origin(500000, 5000000, 1.0, 1.0)  # 1 m pixels, UTM-like
+    data = np.full((50, 50), nodata, dtype="float32")
+    with rasterio.open(
+        dsm_path,
+        "w",
+        driver="GTiff",
+        height=50,
+        width=50,
+        count=1,
+        dtype="float32",
+        crs="EPSG:32618",
+        transform=transform,
+        nodata=nodata,
+    ) as dst:
+        dst.write(data, 1)
+
+    feats = gpd.GeoDataFrame(
+        {"point_id": [7]},
+        geometry=[Point(500025, 4999975)],  # inside the raster
+        crs="EPSG:32618",
+    )
+    bc = BuildCSV("f", str(dsm_path), buffer_feature=3)
+    with pytest.raises(ValueError, match="no data"):
+        bc.extract_features_elevations(feats)