LiDAR Python Tutorial: Ground Classification, DTM and Canopy Height Model from a Point Cloud

Turn a raw airborne LiDAR point cloud (10.6 million points) into a bare-earth terrain model, a surface model and a canopy height map, in plain Python. We write the ground classifier ourselves, a progressive morphological filter in under 20 lines with NumPy and SciPy, check it against the vendor's labels (93% agreement, 98% of ground points recovered), and export GeoTIFFs and a classified LAZ ready for QGIS. The same script runs unchanged on any LAS or LAZ tile. Chapters: 00:00 Intro 01:10 Setup and data 02:32 Load and explore 03:59 Ground classification 06:59 Terrain, surface and canopy height 09:24 Cross-section check 09:58 Export to QGIS 10:36 Parameters and pitfalls 11:24 Next episode Data: Autzen Stadium tile, an open sample published by the PDAL project https://github.com/PDAL/data/tree/main/autzen Code: https://github.com/IbrahimUWA/geoai-lab-tu... Install: pip install laspy lazrs numpy scipy matplotlib rasterio Run: python lidar_pipeline.py input.laz output_folder Method: Zhang et al. (2003), "A progressive morphological filter for removing nonground measurements from airborne LIDAR data", IEEE Transactions on Geoscience and Remote Sensing 41(4). GeoAI Lab: drones, LiDAR and machine learning for the land. A new tutorial every week, all open data and free software, so you can follow along on your own machine. Narration in this episode uses a neural text-to-speech voice. Next episode: detecting individual trees in drone imagery with deep learning. Next episode: DeepForest Tutorial: Detect Individual Trees in Drone Imagery with Deep Learning (CPU only)    • DeepForest Tutorial: Detect Individual Tre...   Full playlist:    • GeoAI Lab tutorials: drones, LiDAR and mac...   #LiDAR #Python #GIS #PointCloud #RemoteSensing #QGIS