rail-lidar-qa-mvp

# Rail LiDAR QA MVP ## Overview A local tool for validating LiDAR quality on railway infrastructure. Simulates drone flights over a PNOA LiDAR point cloud to detect coverage gaps, low-density zones, and shadow areas. ## Workflow 1. User loads `.laz` point cloud file 2. System reads point cloud and calculates general metrics 3. User selects analysis zone (e.g., 20m track + embankment) 4. Tool divides zone into 2D grid 5. For each cell: calculates density, elevation range, coverage 6. Simulates multiple drone passes from different positions 7. Generates QA map with color coding 8. Demo shows if capture is acceptable or needs repetition ## Drone Pass Strategy | Pass | Coverage | Purpose | |------|----------|---------| | P1 | Track axis | Global view of platform, track, ballast, embankment | | P2 | Right flank | Reduce shadows on embankment face, ditch, ballast edge | | P3 | Left flank | Confirm doubtful zones, improve density continuity | | P4 | Adaptive (optional) | Target red cells after P1-P3 | ## QA Traffic Light - **Green**: Sufficient density and continuous coverage - **Yellow**: Partial coverage, irregular density, needs review - **Red**: Gap, shadow, low density, insufficient capture ## Minimum Metrics - Total point count - Bounding box X/Y/Z - Elevation range - Mean points per square meter - Green cell percentage - Yellow cell percentage - Red cell percentage - QA score 0-100 ## Math Model ``` eje_via = principal_eigenvector(covariance(X, Y)) s = longitudinal projection on eje_via d = transverse projection on normal_via ``` Error reduction per pass: ``` e_i,k+1 = max(e_floor, e_i,k * (1 - g_k * visibility_i,k)) + anomaly_i ``` Planner prioritizes cells with highest residual error for adaptive P4. ## Tech Stack - Python: `laspy`, `lazrs` (LAZ reading), `numpy` (grid metrics) - Three.js: 3D point cloud visualization - Local HTTP server (Python stdlib) - Windows launcher (`run_mvp.bat`) ## Color Classification Points are classified visually: - Green: Vegetation (high NIR response) - Dark gray: Track/platform - Ochre: Ballast/substructure - Brown: Natural terrain/embankment - Dark blue: Shadow/occlusion/water ## European Sovereignty Narrative - Galileo Open Service as GNSS base - Galileo HAS for PPP high-precision corrections - EGNOS for operational integrity layer - Local processing, no external cloud - PNOA LiDAR (CNIG) as free base data ## Static Demo Generation ```bat python src\build_static.py ``` Creates `docs/` with Three.js viewer, preprocessed `sample_analysis.json`, and HTML report. Publishable to GitHub Pages or Vercel without uploading the `.laz` file. ## Pitfalls - MVP uses heuristic rules, not real AI - Track detection may be manual or configurable in v1 - Precision depends on sensor, GNSS, IMU, calibration, and pass geometry - Tool does NOT certify railway safety