diff --git a/lidar_pipeline/dtm.py b/lidar_pipeline/dtm.py
index c298955..4464b8b 100644
--- a/lidar_pipeline/dtm.py
+++ b/lidar_pipeline/dtm.py
@@ -285,25 +285,12 @@ def create_dtm_fast(las_file, basename, dtm_dir, resolution):
         dtm = stat.statistic.T
         dtm = dtm[::-1, :]  # Flip Y so north is at top
 
-        # Interpolate NaN gaps using distance-weighted nearest-neighbor fill
+        # Keep NaN for zones without LiDAR data — no interpolation
         nan_count = np.count_nonzero(np.isnan(dtm))
         if nan_count > 0:
             total = dtm.size
             nan_pct = 100.0 * nan_count / total
-            logger.info(f"    {nan_count:,} pixels sans données ({nan_pct:.1f}%) — interpolation...")
-
-            from rasterio.fill import fillnodata
-            # rasterio.fill.fillnodata uses GDAL's interpolation:
-            # fills gaps from surrounding valid pixels with distance weighting
-            dtm_filled = fillnodata(dtm.astype(np.float32), mask=~np.isnan(dtm),
-                                    max_search_distance=max(width, height) // 4)
-            dtm = dtm_filled.astype(np.float64)
-
-            remaining = np.count_nonzero(np.isnan(dtm))
-            if remaining > 0:
-                logger.warning(f"    {remaining:,} pixels encore sans données après interpolation")
-            else:
-                logger.info(f"    ✓ Interpolation terminée — tous les trous comblés")
+            logger.info(f"    {nan_count:,} pixels sans données ({nan_pct:.1f}%)")
 
         # Save as GeoTIFF
         output_tif = dtm_dir / f"{basename}_dtm.tif"
diff --git a/lidar_pipeline/rendering.py b/lidar_pipeline/rendering.py
index 626ce34..6c2dd8b 100644
--- a/lidar_pipeline/rendering.py
+++ b/lidar_pipeline/rendering.py
@@ -323,13 +323,36 @@ def tif_to_png(tif_file, vis_dir, resolution):
                 valid_data = np.asarray(data.compressed() if hasattr(data, 'compressed') else data.flatten())
                 valid_data = valid_data[~np.isnan(valid_data)]
 
-            # Convert MaskedArray to plain ndarray (NaN-filled) to avoid numpy warnings
-            if isinstance(data, np.ma.MaskedArray):
-                data = np.ma.filled(data, np.nan)
+            # Track NaN mask before converting to plain ndarray
+            nan_mask = None
+            if not is_rgb:
+                if isinstance(data, np.ma.MaskedArray):
+                    nan_mask = data.mask.copy()
+                    data = np.ma.filled(data, np.nan)
+                elif np.any(np.isnan(data)):
+                    nan_mask = np.isnan(data)
+
+                # For rendering: replace NaN with neutral value to avoid interpolation halos
+                if nan_mask is not None and np.any(nan_mask) and len(valid_data) > 0:
+                    fill_value = float(np.median(valid_data))
+                    data[nan_mask] = fill_value
+                    nan_mask = nan_mask  # keep for later
 
             # Apply colormap
             data, cmap, title, legend_label, description, is_rgb_result = _apply_colormap(data, tif_file)
 
+            # Apply NaN mask: make zones without data transparent
+            has_nan_mask = nan_mask is not None and not is_rgb_result
+            if has_nan_mask:
+                # data is normalized 0-1 from _apply_colormap; apply cmap to get RGBA
+                cmap_obj = plt.get_cmap(cmap) if isinstance(cmap, str) else cmap
+                rgba = cmap_obj(data)  # (H, W, 4) float RGBA
+                rgba[nan_mask, 3] = 0.0  # transparent where no data
+                data = rgba
+                is_rgba = True
+            else:
+                is_rgba = False
+
             # Create figure — adapt width to data resolution for sharp rendering
             # At high res (5000+px wide), we need a larger figure to avoid downsampling artifacts
             fig_width = max(20, width / 150)
@@ -342,7 +365,7 @@ def tif_to_png(tif_file, vis_dir, resolution):
                          left=0.06, right=0.88, top=0.93, bottom=0.08)
 
             ax = fig.add_subplot(gs[0])
-            if is_rgb:
+            if is_rgba or is_rgb:
                 im = ax.imshow(data, aspect='equal', origin='upper',
                                interpolation='bilinear')
             else: