hotosm/earthquake-damage-assessment-model

kshitijrajsharma/dinov3-damage-assessment

Building-level disaster damage assessment. A frozen DINOv3 ViT-L/16 satellite backbone with a trainable UperNet decoder and two heads (localization + ordinal 4-class damage). Given building footprints and post-disaster imagery (optionally pre-disaster imagery), it assigns each building a damage level on the xBD Joint Damage Scale with a calibrated confidence.

Damage classes

no-damage, minor-damage, major-damage, destroyed.

Object-level metrics (val split, 6166 buildings)

class	F1
no-damage	0.923
minor-damage	0.5797
major-damage	0.6623
destroyed	0.8885

Macro F1 0.7634 | harmonic damage F1 0.7348.

The damage F1 is computed on building pixels only (background excluded). Numbers are in-distribution (the xView2 benchmark splits by tile, so the same events appear in train and test); cross-event generalisation to a fully unseen disaster is harder for the subtle minor/major classes.

Inputs and outputs

• Input: building footprints (GeoJSON) + post-disaster RGB GeoTIFF, optionally a pre-disaster GeoTIFF aligned to the post grid. Footprints must overlay the post image correctly.
• Output: the footprints annotated with damage_class, damage, confidence, review, and per-class probabilities.

Files

• model.onnx: self-contained inference graph (post, pre -> damage logits).
• model.ckpt: Lightning checkpoint for evaluation or further training.
• config.yaml: training configuration.
• calibration.json: confidence temperature (1.1719).

Confidence

Apply softmax(logits / 1.1719) for calibrated probabilities. Buildings below the confidence threshold are flagged for human review.

Backbone

DINOv3 ViT-L/16 (sat493m), frozen. Decoder, fusion, and heads are the only trained parameters (~24M). Trained on xBD (xView2), license CC BY-NC-SA 4.0; this model inherits the non-commercial share-alike terms.