GPU multi-processing fix:
- gpu.py: revert to CUDA_VISIBLE_DEVICES approach with lazy CuPy init
(Device.use() caused CUDA_ERROR_NO_BINARY_FOR_GPU on GPU 1)
- CuPy is imported lazily on first to_gpu() call, allowing
CUDA_VISIBLE_DEVICES to be set before CUDA context creation
- nvidia-smi used for GPU count detection (no CUDA import needed)
- pipeline.py: add tip message suggesting -w N when multiple GPUs detected
Rendering improvements:
- Title: split into bold title (14pt) + italic description (10pt)
- North arrow: moved inside data area (top-right) with transparent
background — no longer overlaps title
- Colorbar: full height (compass gap removed), ScalarFormatter with
useOffset=False to prevent scientific notation on small values
Performance:
- rendering.py: save matplotlib figure to BytesIO instead of temp PNG
file — eliminates disk I/O between matplotlib and PIL
- visualizations.py: cap max_dist at 300 for ray-tracing (SVF,
openness, aniso_open) — avoids 500+ iterations at 0.2m resolution
- pipeline.py: deduplicate n_gpus calculation in parallel path
GPU fix:
- Revert to CUDA_VISIBLE_DEVICES approach but with lazy CuPy init
- gpu.py: CuPy is no longer imported at module level; _init_gpu()
imports it lazily on first to_gpu() call. This allows workers to
set CUDA_VISIBLE_DEVICES before CuPy creates a CUDA context.
- gpu.py: detect GPU count via nvidia-smi (no CUDA context needed)
- pipeline.py: each worker sets CUDA_VISIBLE_DEVICES=N before CuPy
init, so each process uses only its assigned GPU
Rendering improvements:
- Title: split into bold title (14pt) + italic description (10pt)
instead of single 15pt bold block
- North arrow: moved inside data area (top-right corner) with
semi-transparent white background for readability over data
- Colorbar: full height (no gap for compass rose), added
ScalarFormatter(useOffset=False) to avoid scientific notation
- Colorbar compass rose gap removed since north arrow is now
inside the data area
The 80km context made the red LiDAR zone rectangle too small to see.
30km (15km radius) at zoom 12 gives a clear view where the 1km data
zone is visible as a distinct red rectangle in the inset map.
Multi-GPU:
- gpu.py: lazy CuPy initialization so CUDA_VISIBLE_DEVICES takes
effect before context creation in worker processes
- gpu.py: detect GPU count via nvidia-smi (no CUDA import needed)
- gpu.py: add set_active_gpu() to assign workers to specific GPUs
- pipeline.py: distribute files across GPUs (file % num_gpus) in
parallel mode so both GPUs are used simultaneously
- pipeline.py: log GPU count when multiple GPUs detected
Layout fixes:
- rendering.py: move scale bar left of location map to avoid overlap
(scale bar ends at fig_x=0.78, map starts at 0.82)
- rendering.py: expand location map inset to 0.16x0.13 fig coords
- rendering.py: return bounds from _download_location_map so imshow
extent matches the actual IGN tile coverage (80km context)
- ign.py: add min_zoom parameter to download_ign_tiles, fixing the
location map that was broken (zoom 10 blocked by hardcoded min_zoom=15)
- Replace RdBu_r with Crameri roma for all 6 relief-family colormaps
(curvature, mslrm, lrm, tpi, sailore, aniso_open): perceptually
uniform, CVD-friendly, dark center makes near-zero values visible
- Replace hsv with twilight for aspect: perceptually uniform cyclic
colormap with no hue discontinuity
- Add cmcrameri dependency (falls back to RdBu_r if unavailable)
- Fix IGN location map: pass min_zoom=8 to download_ign_tiles so zoom
10 context maps work (was hardcoded to min_zoom=15, blocking the
loop entirely for zoom_level=10)
- Expand location map context from 3x extent to 80km fixed radius for
better regional context at zoom 10
- Remove dead code: flow colormap entry, _nice_scale dead loop,
aspect='auto' in location map imshow, outdated PDF report order list
- Fix SVF docstring: 8 directions → 16 directions
- validate_laz: verify point data accessibility (not just headers) to detect
corrupted COPC files that pass header checks but fail on data reads
- classify_ground: fallback from CSF to SMRF when CSF produces no ground
points or PDAL errors (fixes 2/9 failing tiles)
- MSRM: preserve sign in weighted combination so RdBu_r colormap shows
both red (elevated) and blue (depressed) instead of red only
- Add Sky-View Factor (SVF) visualization: cos²(horizon angle) over 16
directions, excellent for archaeological earthwork detection
- Add Anisotropic Openness: directional weighting (NW-SE/NE-SW) enhances
linear feature detection aligned with common settlement patterns
- Remove anomalies and flow visualizations (replaced by SVF + aniso_open)
- Location inset: use IGN topographic map at zoom 10 instead of simplified
France outline, with red rectangle marker and fallback
- Remove flow (hydrological accumulation) from VIZ_STEPS
- MSRM/TPI/roughness/anomalies: revert z-score (x-mean)/std to std normalization x/std
to preserve contrast and visibility of linear features (paths, ditches, trenches)
- MSRM: adaptive scales based on resolution, archaeological weight combination
- TPI: extend from 2 to 4 scales (3m/15m/50m/200m) with weighted combination
- Hillshade: 8 directions instead of 4, altitude 35° instead of 30°
- LRM: adaptive sigma based on resolution
- Openness: doubled radius (100m instead of 50m)
- Roughness: multi-scale (3m fine + 15m broad) instead of single 5x5 window
- Anomalies: uses MSRM multi-scale relief instead of single LRM 15m
- Wavelet: 8 adaptive scales, std normalization, archaeological weights
- Remove svf (Sky-View Factor) and local_dominance visualizations
- Add AVIF format support (default), quality 98
- Add multi-resolution support (-r 0.5,0.2)
- Improve Ctrl+C handling for immediate process termination
- Update rendering.py descriptions for all modified visualizations
- --resolution now accepts comma-separated values (e.g. 0.5,0.2)
- Additional resolutions get suffixed output dirs: basename_r0p2/
- DTM files are named basename_dtm_r0p2.tif for extra resolutions
- Ground classification is done once and shared across resolutions
- PDF report generation removed per user request
- Fix --file argument to accept full filenames with extensions
Previously --file LHD_FXX_...copc.laz would fail because it appended
extensions. Now tries exact filename match first, then falls back to
adding extensions.
- WebP output now uses quality=85 by default (down from lossless),
reducing file size by ~75% (35MB → 5-8MB per visualization)
- Added --quality N (1-100) and --lossless flags in CLI and run.sh
- Added --only and --skip to select/exclude specific visualizations
(e.g., --only hillshade,svf,lrm or --skip ortho,topo)
- VIZ_STEPS filtering is done in LidarArchaeoPipeline.__init__
- SharedDEM is skipped when all selected visualizations already exist
- Invalid visualization names are validated at startup with clear error
Fixes "zero-size array to reduction operation" crash on corrupt/incomplete
LAZ files. Added checks at each step:
- validate_laz(): check point_count > 0 via laspy header, parse PDAL
info JSON for point count when using PDAL fallback
- detect_ground_method(): return 'smrf' default if point cloud is empty
after PDAL conversion instead of crashing on np.max(empty_array)
- _read_with_pdal(): log warning and return None if converted file has
0 points
- create_dtm_fast(): fail gracefully if ground file has 0 points
- classify_ground(): check output file size after PDAL pipeline to
catch empty ground classifications early
Two optimizations to avoid ~2min wasted per file on re-runs:
1. pipeline.py: Check which visualizations need regeneration before
computing SharedDEM. If all WebP outputs exist, skip SharedDEM
entirely. If only IGN overlays need updating, also skip SharedDEM.
2. visualizations.py: Make SharedDEM attributes lazy (filled, gradient,
lrm_15) so only the data actually needed is computed. For example,
if only hillshade is regenerated, LRM at 15m is never calculated.
- Dockerfile: install PDAL 2.10.1 from conda-forge (was 2.3 from apt)
Ubuntu 22.04's PDAL 2.3 cannot read COPC v1.1 files from IGN LiDAR HD
- dtm.py: add _read_with_pdal() fallback for COPC files that laspy can't read
- dtm.py: validate_laz() now tries PDAL when laspy fails
- dtm.py: create_dtm_fast() and detect_ground_method() use PDAL fallback
- ign.py: auto-retry at lower zoom on 404 errors
- pipeline.py: check DTM resolution mismatch and regenerate if needed
- pipeline.py: propagate actual DTM resolution to visualizations
- pipeline.py: add --init to docker run for proper Ctrl+C signal handling
- Remove RRIM and Multi-Hillshade RGB visualizations
Validate file readability before PDAL classification. Corrupted/truncated
files are detected instantly via laspy header read and skipped with a clear
error message pointing to re-download, instead of wasting time on PDAL
and repair attempts that will fail anyway.
When zoom 20 tiles are unavailable (rural areas), fall back to zoom 19, 18,
etc. down to 15. Breaks out immediately on first-tile 404 to avoid wasting
requests at unsupported zoom levels.
- Remove generate_rrim, generate_multi_hillshade, _compute_openness_both
- Remove corresponding VIZ_STEPS entries, COLORMAPS, RGB_LEGENDS, and tests
- Fix DTM resolution mismatch: existing DTM at different resolution is now
regenerated instead of silently reused
- Propagate actual DTM resolution to visualizations and rendering
- Add --init to docker run commands for proper signal handling on Ctrl+C
- Add .playwright-mcp/ to .gitignore
Same issue as pos_open/neg_open — lambdas in VIZ_STEPS need shared=None
parameter since the pipeline passes shared=shared to all visualization functions.
Three new visualizations complementing existing SVF/openness/LRM/MSRM:
- RRIM (Red Relief Image Map): RGB composite combining positive openness
(R), inverted slope (G), negative openness (B). Uses ray-tracing
to compute both openness values in a single pass.
- Multi-Hillshade RGB: 3 azimuths (315°, 135°, 45°) mapped to R/G/B
channels with slope blending. Color reveals structure orientation.
- Local Dominance: (dem - local_min) / (local_max - local_min) using
min/max filters. Measures local height position — complements openness.
Also adds:
- _compute_openness_both() helper for shared ray-tracing (used by RRIM)
- xp_maximum_filter() in gpu.py (GPU/CPU abstraction)
- Entries in COLORMAPS, RGB_LEGENDS, VIZ_STEPS, and is_rgb detection
- All NaN handling follows existing patterns (nan_mask restoration)
- Remove PMF from ground classification options (PDAL recommends SMRF over PMF)
- Auto-detection now uses CSF for urban/complex terrain instead of PMF
- Add z_std > 30m heuristic to auto-select CSF for complex terrain
- Fix pos_open/neg_open lambda missing 'shared' parameter (NameError in workers)
- Fix NaN mask not restored in hillshade, slope, aspect, curvature
(gradient-based products computed on filled DEM lost NaN transparency)
- Add nan_mask parameter to _save_tif for centralized NaN restoration
- DTM TIF kept by default (no longer deleted after WebP conversion)
- Fix gpu_cleanup import missing in visualizations.py (NameError in workers)
- Fix t_pdf referenced before assignment when PDF is skipped
- Skip classification+DTM when DTM exists regardless of --force
- --force now only regenerates WebP/PDF, not classification/DTM
- --force-classification forces reclassification when needed
- Add laspy repair fallback for corrupt LAZ files (EVLR errors)
- Keep DTM TIF by default for reuse (--no-keep-tif to delete)
- Increase space between image and bottom cartouche (0.12→0.19)
If the DTM .tif exists and --force is not set, skip both ground
classification and DTM generation entirely. Previously, the pipeline
would spend 3+ minutes reclassifying ground even when the DTM was
already present and would be reused anyway.
Also includes: SharedDEM cache, enhanced WebP cartouche (compass rose,
adaptive scale bar, enriched info bar), removed COG/viewer, UTF-8
fix for parallel workers, skip logic for DTM and PDF.
- Ajout de convert_to_cog() et generate_cog_metadata() dans rendering.py
- Nouveau module viewer.py: génération HTML MapLibre GL JS avec couches et opacité
- Nouveau module server.py: serveur FastAPI avec TiTiler pour tuiles COG
- Pipeline: étapes 5 (COGs) et 6 (viewer web) après le rapport PDF
- CLI: flag --no-viewer pour désactiver la génération du viewer
- run.sh: commande 'serve' pour démarrer le serveur sur port 8000
- Dockerfile: ajout de rio-cogeo, titiler.core, fastapi, uvicorn, piexif
- setup.py: point d'entrée lidar-server
