[general]
name=beaconGIS — Building Damage Assessment
qgisMinimumVersion=3.16
qgisMaximumVersion=4.99
description=AI building-level damage classification from pre/post-disaster RGB imagery, using a Siamese U-Net ensemble running on ONNX Runtime.
version=1.0.0
author=azeldev
email=azel.dev03@gmail.com
about=Classifies buildings into No Damage, Minor, Major, or Destroyed using a two-network deep-learning pipeline (LocalizationUNet + SiameseUNet, SeResNeXt-50 (32x4d) encoder, two-model ensemble) trained on the full public xView2/xBD dataset with Inria Aerial Image Labeling pretraining. xView2 official scorer Combined F1 = 0.7358. Designed for disaster-response teams: includes AOI clipping, CPU Fast Mode for non-GPU machines, automatic CRS-aware pre/post alignment, GSD normalization, offline template-driven SitRep generator (no LLM API), GeoPackage / GeoTIFF mask / JSON sidecar export. Runs entirely on ONNX Runtime (no PyTorch install required). Model weights (~234 MB fp16, CC BY-NC-SA 4.0) are downloaded on first use from the plugin's GitHub release page over plain HTTPS with SHA-256 verification — no account or token required. Outputs are draft assessments for human review — not authoritative ground truth.
tracker=https://github.com/azeldev/beaconGIS/issues
repository=https://github.com/azeldev/beaconGIS
homepage=https://github.com/azeldev/beaconGIS
tags=raster,vector,change detection,building damage,xview2,xbd,deep learning,disaster response,humanitarian,satellite imagery,onnx,siamese unet,remote sensing
category=Raster
icon=icon.png
experimental=False
deprecated=False
hasProcessingProvider=False
# plugin_dependencies is intentionally NOT set — that field is for other
# QGIS plugin names, not pip packages. The plugin auto-installs its pip
# dependencies on first run via change_detector._check_dependencies(),
# preferring onnxruntime-directml on Windows for GPU acceleration without
# requiring CUDA Toolkit.
changelog=
    1.0.0 (2026-06-02):
      - Initial public release on the QGIS Plugin Repository.
      - Two-network architecture: LocalizationUNet + SiameseUNet ensemble (M1+M2).
      - SeResNeXt-50 (32x4d) encoder, ImageNet -> Inria Aerial -> xBD training.
      - Combined F1 = 0.7358 (xView2 official scorer, full plugin pipeline).
      - Two-model softmax-averaged ensemble for more robust per-pixel predictions.
      - CPU Fast Mode for laptops without GPU (~8x speedup, ~0.5pt F1 cost).
      - GSD normalization to ~0.5 m/px training scale.
      - Watershed-based touching-building separation with user-tunable strength.
      - Optional 4-way TTA (default off), Hanning-blended tile inference, AOI clipping.
      - GeoPackage / per-pixel mask GeoTIFF / JSON metadata sidecar exports.
      - Assessment Reports dock with offline template-driven SitRep generation (no LLM API).
      - Satellite imagery downloader dock (ESRI World Imagery / OSM).
      - Distribution: ONNX Runtime (no PyTorch required at runtime).
      - Weights distributed via GitHub releases with SHA-256 verification.
      - GPU acceleration via onnxruntime-directml or onnxruntime-gpu (auto-detected).
