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🎉 First Release - IGN LiDAR HD Processing Library

· 4 min read
Simon Ducournau
Simon Ducournau
Lead Developer & Researcher

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.