beaconGIS — Building Damage Assessment
AI building-level damage classification from pre/post-disaster RGB imagery, using a Siamese U-Net ensemble running on ONNX Runtime.
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 = 0.7312. 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.