Trois chantiers liés à la qualité et au coût des rendus :
- Calage vertical des faisceaux : les passes d'une tuile peuvent être biaisées
de quelques cm (±2,5 cm mesurés sur 1000_6882), créant des marches aux
recouvrements. Les offsets par PointSourceId sont mesurés sur les points
sol de la tuile (réf. médiane itérée) et retranchés ≥ 0,5 cm avant
rastérisation, avec sidecar de cache et application au plancher bare-earth.
- Openness décimée ×2 : lancé de rayons sur grille par blocs (max/min) puis
rééchantillonnage bilinéaire — 532 s → 40 s par tuile à 0,2 m sur CPU,
signal archéologique préservé. Réglable --openness-downsample.
- Génération webapp concentrée sur les couches affichées : les défauts
/api/generate, /api/preview et le sélecteur génèrent le panneau complet
(slope, aspect, pos_open) au lieu d'aspect seul.
Prevents indefinite hang if a worker gets stuck (deadlocked PDAL,
I/O stall). 2-hour safety net for the entire batch; remaining
futures are cancelled on timeout.
La webapp (carte + vignettes) et la génération de tuiles se déploient sur
deux machines : image légère Dockerfile.webapp (FastAPI + Pillow AVIF
natif + pyproj) sur Raspberry Pi, pipeline complet sur la machine de
traitement. LIDAR_GENERATION_URL délègue /api/generate, /api/preview et
/api/status ; /api/sync ramène les tuiles par rsync puis régénère
vignettes et index localement. Token partagé optionnel
(LIDAR_API_TOKEN/LIDAR_REMOTE_TOKEN). Retire du dépôt les journaux
internes (.swival, audit-findings) et les données (data/, notebooks/).
Doc : docs/DEPLOY_WEBAPP.md.
- webapp.py: FastAPI serving the continuous map (port 8973) with
/api/preview, /api/generate and /api/status; tiles are downloaded
from IGN and processed in a logged subprocess, tracked live in a
side "File de génération" panel that survives page reloads
- fetch_ign.py: download missing 1 km LiDAR HD tiles from the IGN
geoplateforme before processing
- index.py: tile thumbnails and 500 m subtiles are now invalidated by
mtime so regenerating a tile refreshes its cached images; progress
logging per tile
- dtm.py: back to the historical gap handling (small gaps filled by
fillnodata only, larger holes left as nodata rendered black);
lowest-return floor only via --bare-earth, IGN class selection via
--ign-classes
- cli.py: positional input now optional (--rebuild-index works alone)
- docker-compose.yml: serve (GPU, port 8973) and process services;
launch via docker compose only (documented in AGENTS.md/AGENTS.md)
- tests: 131 passing, incl. regressions for thumbnail staleness,
--rebuild-index without input, and nodata rendering
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
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)
- 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
- 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
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.
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.
- 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
- 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
- rendering.py: colorbar cassée quand NaN mask actif — créer un
ScalarMappable avec le cmap sauvegardé au lieu de rely sur
l'image RGBA qui n'a plus de cmap
- rendering.py: nettoyage du PNG temporaire avec try/finally et
missing_ok=True pour éviter les fichiers orphelins
- gpu.py: to_gpu() convertit en float32 au lieu de float64 pour
réduire la consommation mémoire GPU
- dtm.py: utiliser _file_basename() de pipeline.py au lieu de
dupliquer la logique d'extraction du basename
- pipeline.py: docstring corrigé (18 visualisations, pas 19)
- cli.py: --file supporte aussi les noms sans .copc
(recherche .copc.laz et .copc.las en plus de .laz et .las)
Les fichiers LiDAR HD IGN ont l'extension .copc.laz (double extension).
Path.stem ne retire que .laz, laissant .copc dans le basename.
Ajout de _file_basename() qui retire les extensions connues
dans l'ordre: .copc.laz, .copc.las, .laz, .las.
- _process_file_standalone: supprime temp_{basename} après chaque fichier
- process_all: supprime aussi les répertoires temp_* orphelins des workers
- 18 Go de fichiers .las orphelins supprimés manuellement
- 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 AGENTS.md (17 → 16 fonctions generate_*)
- 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
Ajout d'un FilePrefixFilter qui préfixe chaque message avec [basename]
quand un fichier est en cours de traitement. Les workers paralleles
affichent ainsi clairement quel fichier produit chaque log.
- multiprocessing.set_start_method('spawn') pour éviter la corruption
du contexte CUDA dans les processus forkés
- to_gpu() et xp_*_filter() attrapent les erreurs CUDA et tombent
sur CPU au lieu de crasher
- _gpu_available() vérifie que le GPU est utilisable avant chaque opération
- gpu_cleanup() attrape les exceptions au cas où le GPU serait indisponible
- Suppression de generate_solar (éclairage solaire) des visualisations
- Accélération GPU de hillshade, slope, aspect, curvature, depressions,
anomalies, roughness, texture GLCM, flow (sink filling)
- Nettoyage mémoire GPU entre visualisations (gpu_cleanup)
- Correction OOM texture GLCM: calcul entropie bin par bin au lieu d'un
tableau 3D massif sur GPU
- Correction bug: xp_minimum_filter manquant dans imports visualizations
- Option --file accepte plusieurs noms complets sans extension
- run.sh affiche l'aide si appelé sans arguments
- Option --test pour exécuter les tests unitaires dans Docker
- Filtre ReturnNumber>=1 intégré dans le pipeline PDAL (plus d'erreur SMRF)
- 60 tests unitaires: GPU, visualisations, rendering, DTM, pipeline, CLI
- Ajout pytest au Dockerfile
- Découpage du monolithe process_lidar.py (~2750 lignes) en package
lidar_pipeline/ avec 9 modules (gpu, dtm, visualizations, ign,
rendering, pipeline, cli, __init__, __main__)
- Logging configurable: -v (verbose avec timestamps) et --debug
(détails internes fichier:ligne)
- Option --force pour régénérer tous les fichiers (par défaut skip
les WebP existants)
- Option --file NOM pour traiter un seul fichier LAZ (tests rapides)
- ProcessPoolExecutor avec répertoires temporaires uniques par worker
- Suppression du code mort (geomorphons, hillshade_ne, nodata_mask)
- Aucun fichier TIFF résiduel après conversion WebP
- setup.py pour installation pip, stub process_lidar.py compatible
