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🎉 First Release - IGN LiDAR HD Processing Library

· 4 minutes de lecture
Simon Ducournau
Simon Ducournau
Développeur principal et chercheur

We're thrilled to announce the first official release of the IGN LiDAR HD Processing Library! This comprehensive Python toolkit transforms raw IGN LiDAR HD data into machine learning-ready datasets for Building Level of Detail (LOD) classification.

📺 Watch the Demo​

🎬 Watch on YouTube - See the complete workflow in action!

✨ What's New in v1.1.0​

🏗️ Core Features​

  • Complete LiDAR Processing Pipeline - From raw tiles to ML-ready patches
  • Multi-Level Classification - Support for LOD2 (15 classes) and LOD3 (30+ classes)
  • Rich Geometric Features - Surface normals, curvature, planarity, verticality
  • Smart Skip Detection - Resume interrupted workflows automatically
  • GPU Acceleration - Optional CUDA support for faster processing

🌍 Geographic Intelligence​

  • IGN WFS Integration - Direct tile discovery and download
  • Strategic Locations - Pre-configured urban, coastal, and rural zones
  • Coordinate Handling - Automatic Lambert93 ↔ WGS84 transformations
  • 50+ Curated Tiles - Diverse test dataset across France

⚡ Performance Optimizations​

  • Parallel Processing - Multi-worker batch operations
  • Memory Management - Chunked processing for large datasets
  • Format Flexibility - LAZ 1.4 or QGIS-compatible outputs
  • Architectural Styles - Automatic building style inference

🎯 Quick Start​

Get started in just a few commands:

# Install the library
pip install ign-lidar-hd

# Download LiDAR tiles
ign-lidar-hd download --bbox 2.0,48.8,2.1,48.9 --output tiles/

# Enrich with features
ign-lidar-hd enrich --input-dir tiles/ --output enriched/

# Create training patches
ign-lidar-hd process --input-dir enriched/ --output patches/ --lod-level LOD2

📊 What Makes This Special​

🔄 Complete Automated Workflow​

🚀 Performance That Scales​

  • CPU Processing: 4-12 tiles/hour
  • GPU Acceleration: Up to 10x faster feature computation
  • Smart Resumability: Never reprocess existing data
  • Parallel Workers: Automatic CPU core detection

🏛️ Real-World Applications​

This library enables research and applications in:

  • Urban Planning - Building component analysis
  • Architecture Research - Automated style classification
  • 3D City Modeling - LOD2/LOD3 reconstruction
  • Machine Learning - Large-scale point cloud datasets
  • GIS Integration - QGIS-compatible workflows

🎓 Learning Resources​

📖 Complete Documentation​

Our new documentation site includes:

  • Interactive Workflows - Visual step-by-step guides
  • Architecture Diagrams - System component overview
  • Performance Benchmarks - GPU vs CPU comparisons
  • Troubleshooting Guides - Decision trees for common issues

💡 Example Gallery​

  • Urban Processing - Dense city environments
  • Rural Analysis - Sparse natural areas
  • Coastal Zones - Mixed terrain challenges
  • PyTorch Integration - ML training pipelines

🔧 Technical Highlights​

Smart Skip System​

Never waste time reprocessing:

# First run - processes all files
ign-lidar-hd enrich --input tiles/ --output enriched/

# Second run - skips existing (instant)
ign-lidar-hd enrich --input tiles/ --output enriched/
# ✅ 0 processed, 25 skipped

Rich Data Output​

Each training patch includes:

  • 3D Coordinates - Precise spatial positioning
  • 30+ Features - Comprehensive geometric analysis
  • Building Labels - LOD2/LOD3 classifications
  • Metadata - Tile info, processing parameters

🌟 Community Impact​

This release represents months of development focused on:

  • ✅ Researcher Productivity - Streamlined data preparation
  • ✅ Reproducible Science - Standardized processing workflows
  • ✅ Open Source Values - MIT licensed, community-driven
  • ✅ Performance First - Production-ready optimization

🚀 What's Next​

We're already working on exciting features for future releases:

  • Cloud Processing - Azure/AWS integration
  • Advanced ML Models - Pre-trained classification networks
  • Real-time Processing - Streaming LiDAR analysis
  • International Support - Beyond France datasets

🤝 Get Involved​

Try It Out​

pip install ign-lidar-hd

Connect With Us​

🎉 Thank You​

A huge thanks to the IGN (Institut National de l'Information Géographique et Forestière) for providing access to the incredible LiDAR HD dataset, and to the open-source community for the tools that made this possible.

The future of LiDAR processing starts now! 🚀

Ready to transform your LiDAR data? Download the library and join the community building the next generation of geospatial ML tools.