Semantic segmentation of aerial LiDAR point clouds (LAS / LAZ / COPC)
using the 3D SegFormer architecture, written to the standard ASPRS
'classification' dimension.

Primary classes (default ASPRS codes):
- Ground (ASPRS 2)
- Vegetation (ASPRS 5)
- Building (ASPRS 6)
- Wires / Powerlines (ASPRS 14)
- Poles (ASPRS 15)

Auxiliary classes (mapped to ASPRS 1 = Unclassified, the LAS spec
has no dedicated code for them):
- Vehicles
- Fences

Features:
- Native dock-based UI: drag-and-drop LAS / LAZ / COPC, layer picker, batch processing
- QGIS Processing algorithm for use in the Toolbox, Graphical Modeler and qgis_process CLI
- Background QgsTask execution with full progress and cancellation
- One-click dependency installer (downloads a portable Python +
uv into an isolated venv; does NOT touch QGIS's own Python)
- Automatic NVIDIA GPU detection (compute capability + driver
version) with the right CUDA wheel (cu121 / cu124 / cu126 /
cu128) selected per system; CPU fallback; Apple Silicon MPS
- On-demand model download with SHA-256 verification (uses
QgsBlockingNetworkRequest so the QGIS proxy is respected)
- Spatial tiling and streaming I/O for files larger than RAM
- Direct loading of classified outputs as QGIS point-cloud layers (PDAL)

Requirements / external dependencies:
- QGIS >= 3.34
- PyTorch + torchvision (2.x), laspy, lazrs, timm, numpy_indexed,
numpy (<2.0). These are NOT bundled - they are downloaded into
a per-user isolated virtual environment on first run (no admin
rights, no impact on QGIS's own Python). About 1-3 GB on disk
depending on whether a CUDA build is installed.
- Optional: an NVIDIA GPU with a driver new enough for the
chosen CUDA toolkit (see plugin documentation for the table).
- The bundled deep-learning model is downloaded on first use
(~18 MB) from the official TreeAIBox release.

Restrictions / licensing:
- The plugin source code is licensed under GPL-3.0-or-later.
- The downloaded model is licensed under CC BY-NC 4.0
(Creative Commons Attribution-NonCommercial 4.0). Commercial
users must obtain a separate licence from the model author.
- No spatial / regional restriction.

The model is the UrbanFiltering 3D SegFormer from the TreeAIBox project
(Zhouxin Xi, tested by Charumitha Selvaraj - Natural Resources Canada, NRCan),
Crown Copyright, Government of Canada, distributed under CC BY-NC 4.0:
https://github.com/NRCan/TreeAIBox