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Utilisation de base

This guide covers the essential workflows pour traiter IGN LiDAR HD data into jeux de données prêts pour l'apprentissage automatique.

Vue d'ensemble​

The IGN LiDAR HD processing workflow consists of three main steps:

  1. Téléchargement - Obtenir les tuiles LiDAR depuis les serveurs IGN
  2. Enrichissement - Ajouter des caractéristiques de composants de bâtiment aux points
  3. Traitement - Extraire des patches pour l'apprentissage automatique

🔄 Workflow complet Pipeline​

📊 Data Transformation Flow​

Étape 1: Téléchargement LiDAR Tiles​

Téléchargement LiDAR tiles for your area of interest:

# Téléchargement tiles for Paris center
ign-lidar-hd download \
  --bbox 2.25,48.82,2.42,48.90 \
  --output /chemin/vers/tuiles_brutes/ \
  --max-tiles 10

Paramètres​

  • --bbox: Boîte englobante au format min_lon,min_lat,max_lon,max_lat
  • --output: Répertoire pour sauvegarder downloaded tiles
  • --max-tiles: Nombre maximum de tuiles to download (optionnel)

Sortie​

Téléchargemented tiles are saved as LAZ files:

tuiles_brutes/
├── LIDARHD_FXX_0123_4567_LA93_IGN69_2020.laz
├── LIDARHD_FXX_0124_4567_LA93_IGN69_2020.laz
└── ...

Étape 2: Enrichissement with Building Features​

Add building component classification features to the point clouds:

# Enrichissement tiles with building features
ign-lidar-hd enrich \
  --input-dir /chemin/vers/tuiles_brutes/ \
  --output /chemin/vers/tuiles_enrichies/ \
  --mode full \
  --num-workers 4

Paramètres​

  • --input-dir: Directory containing raw LAZ tiles
  • --output: Répertoire pour sauvegarder enriched tiles
  • --mode: Feature extraction mode (currently only building)
  • --num-workers: Nombre de workers parallèles (optionnel)

Sortie​

Enrichissemented tiles contain additional point attributes for building classification:

tuiles_enrichies/
├── LIDARHD_FXX_0123_4567_LA93_IGN69_2020.laz  # With building features
├── LIDARHD_FXX_0124_4567_LA93_IGN69_2020.laz
└── ...

Chaque point dispose maintenant de 30+ caractéristiques géométriques pour la classification des composants de bâtiment.

Étape 3: Extraction patches​

Extract small patches suitable for machine learning:

# Extract patches for LOD2 building classification
ign-lidar-hd process \
  --input /chemin/vers/tuiles_enrichies/ \
  --output /chemin/vers/patches/ \
  --lod-level LOD2 \
  --patch-size 10.0 \
  --num-workers 4

Paramètres​

  • --input: Directory containing enriched LAZ tiles
  • --output: Répertoire pour sauvegarder extracted patches
  • --lod-level: Classification level (LOD2 or LOD3)
  • --patch-size: Patch size in meters (default: 10.0)
  • --num-workers: Nombre de workers parallèles (optionnel)

Sortie​

Patches are saved as NPZ files with point clouds and labels:

patches/
├── tile_0123_4567/
│   ├── patch_0001.npz
│   ├── patch_0002.npz
│   └── ...
├── tile_0124_4567/
│   └── ...

Each patch contains:

  • Point coordinates (X, Y, Z)
  • Geometric features (30+ attributes)
  • Building component labels
  • Patch metadata

Niveaux de classification​

LOD2 (15 Classes)​

Basic building components suitable for urban analysis:

  • Wall, Roof, Ground, Vegetation
  • Window, Door, Balcony, Chimney
  • And 7 more classes...

LOD3 (30 Classes)​

Detailed building components for architectural analysis:

  • All LOD2 classes plus:
  • Roof details (tiles, gutters, dormers)
  • Facade elements (shutters, decorative features)
  • And 15 additional detailed classes...

Workflow complet Exemple​

Here's a complete example processing the 13th arrondissement of Paris:

# 1. Téléchargement tiles
ign-lidar-hd download \
  --bbox 2.32,48.82,2.38,48.86 \
  --output data/tuiles_brutes/ \
  --max-tiles 20

# 2. Enrichissement with features
ign-lidar-hd enrich \
  --input-dir data/tuiles_brutes/ \
  --output data/tuiles_enrichies/ \
  --mode full \
  --num-workers 6

# 3. Extract patches
ign-lidar-hd process \
  --input data/tuiles_enrichies/ \
  --output data/patches/ \
  --lod-level LOD2 \
  --patch-size 10.0 \
  --num-workers 6

Expected processing time for 20 tiles:

  • Téléchargement: ~15 minutes (depends on network)
  • Enrichissement: ~45 minutes (with 6 workers)
  • Traitement: ~30 minutes (with 6 workers)

Chargement des données​

Once you have patches, load them for machine learning:

import numpy as np

# Load a single patch
data = np.load('patches/tile_0123_4567/patch_0001.npz')
points = data['points']        # Shape: (N, 3) - X, Y, Z
features = data['features']    # Shape: (N, 30+) - Geometric features
labels = data['labels']        # Shape: (N,) - Building component labels

print(f"Patch has {len(points)} points")
print(f"Feature dimensions: {features.shape[1]}")
print(f"Unique labels: {np.unique(labels)}")

Considérations sur la mémoire​

For large datasets, monitor memory usage:

# Check memory usage during processing
htop

# Reduce workers if memory is limited
ign-lidar-hd process --num-workers 2

# Traitement tiles one by one for very large tiles
ign-lidar-hd process --num-workers 1

See the Memory Optimization Guide for detailed memory management strategies.

Détection intelligente de saut​

All commands automatically skip existing outputs:

# Run the same command twice - second run skips existing files
ign-lidar-hd download --bbox 2.32,48.82,2.38,48.86 --output data/tuiles_brutes/
# First run: Téléchargements new tiles
# Second run: Skips existing tiles automatically

# Force reprocessing with --force flag
ign-lidar-hd process --input data/enriched/ --output data/patches/ --force

See the Smart Skip Features guide for details.

Dépannage​

Téléchargement Issues​

# Check network connectivity
ping geoservices.ign.fr

# Verify bbox coordinates (should be in France)
# Valid range: longitude 1-8, latitude 42-51

Traitementing Errors​

# Check file permissions
ls -la /chemin/vers/tiles/

# Verify LAZ file integrity
lasinfo tile.laz

# Reduce workers if getting memory errors
ign-lidar-hd process --num-workers 1

Missing Features​

# Verify enrichment completed successfully
lasinfo enriched_tile.laz | grep "extra bytes"

# Re-enrich if features are missing
ign-lidar-hd enrich --input-dir raw/ --output enriched/ --force

Prochaines étapes​