- 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
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- 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)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- 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)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- 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
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Positions d'axes fixes (data_left/bottom/width/height_frac) pour alignement
pixel-parfait entre terrain et ortho/topo
- aspect='equal' au lieu de 'auto' pour conserver les proportions géographiques
- Colorbar descriptive pour les visualisations RGB (ortho/topo)
- Comblage des petits trous DTM (< 1m) via rasterio.fill.fillnodata
- Suppression de la visualisation "dépressions"
- Hillshade composite: 0.7*hillshade + 0.3*cos(slope)
- D8 flow accumulation accéléré par numba JIT (fallback Python)
- Flag --keep-tif pour conserver les TIFF intermédiaires
- --force supprime aussi les TIF existants avant régénération
- ETA affiché pendant la génération des visualisations
- Répertoires temp dans temp/ pour traitement parallèle
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Suppression de generate_texture() de visualizations.py
- Suppression de l'entrée 'texture' de VIZ_STEPS et COLORMAPS
- Suppression du test TestTexture
- Mise à jour README (19 → 18 visualisations)
- Mise à jour CLAUDE.md (17 → 16 fonctions generate_*)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Ajout classification automatique du sol (SMRF/PMF/CSF) avec détection
heuristique (ratio retours uniques > 0.6 → PMF urbain, sinon SMRF)
- Pré-traitement PDAL recommandé avant classification: ELM + outlier
removal (cell=5.0, threshold=2.0 adapté au calcaire rocailleux)
- Options CLI: --ground-classification {auto,smrf,pmf,csf} et
--force-classification pour forcer la reclassification
- Fix double logging (logger.propagate = False)
- Fix --force non transmis dans run.sh (réécriture parsing arguments)
- Fix warning numpy 'partition will ignore mask': conversion MaskedArray
en ndarray avant np.percentile()
- Ajout liblaszip8 + lazrs pour support LAZ dans Docker et laspy
- Tests unitaires pour PMF, CSF et auto-détection
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
