Aerial LiDAR Classifier
Classify airborne, mobile mapping and railway LiDAR point clouds with deep learning (LitePT-L).
Classifies airborne, mobile mapping and railway LiDAR point clouds
(LAS / LAZ / COPC) with deep-learning models. Three classifiers:
Airborne (LitePT-L at 10 cm on NVIDIA GPUs, the UrbanFiltering 3D
SegFormer from NRCan's TreeAIBox on other computers; standard ASPRS
codes), Mobile mapping (LitePT-L at 5 cm: ground, low and high
vegetation, buildings, pole-like objects, vehicles, fences and
barriers, wires) and Railway (LitePT-L at 5 cm: ground and track bed,
rails, masts and poles, overhead wires, vegetation, fences). Mobile
mapping and railway class codes are editable; both need an NVIDIA
CUDA GPU.
External dependencies: PyTorch, laspy, lazrs, timm, scipy and, on
NVIDIA GPUs, spconv. A one-click setup installs them into a separate
per-user folder on first use (no admin rights, nothing to
configure), and the model weights download automatically. Files in
feet are converted from their CRS; tiling and streaming handle files
larger than RAM. Model weights are CC BY-NC 4.0 (railway: CC BY-NC-SA 4.0). Documentation:
https://github.com/akharroubi/AerialLidarClassifier
The plugin author teaches a live cohort, LiDAR Point Clouds
Processing in QGIS: https://maven.com/geomatics/qgis3d?utm_source=plugins.qgis.org&utm_medium=listing&utm_campaign=qgis3d