diff --git a/lidar_pipeline/visualizations.py b/lidar_pipeline/visualizations.py
index 596a0e9..7daaa83 100644
--- a/lidar_pipeline/visualizations.py
+++ b/lidar_pipeline/visualizations.py
@@ -1550,7 +1550,11 @@ def generate_anomaly_mask(dem_file, basename, vis_dir, resolution, shared=None,
         # With n_sigma=2.0 → 80th percentile: keep only the top 20% of signal.
         # This adapts to each tile's terrain instead of a fixed z-score cutoff.
         threshold_pct = max(50, min(95, 100 - n_sigma * 10))
-        threshold_val = np.percentile(combined[~nan_mask], threshold_pct)
+        combined_valid = combined[~nan_mask]
+        if combined_valid.size == 0:
+            logger.warning("  ✗ Détection anomalies : tuile 100 % nodata")
+            return None
+        threshold_val = np.percentile(combined_valid, threshold_pct)
         combined = np.clip(combined - threshold_val, 0, None)
 
         # Rescale survivors to 0–1