File size: 3,375 Bytes
0e4b1c5 cfa4b76 0e4b1c5 cfa4b76 f61e88c cfa4b76 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | ---
license: cc-by-nc-4.0
library_name: pytorch
tags:
- remote-sensing
- building-damage-assessment
- post-disaster
- damage-typology
- dinov3
- disaster-response
pipeline_tag: image-segmentation
---
# Damage-TriageFormer (trained checkpoint)
Trained weights for **Damage-TriageFormer**, a foundation-model framework for
decision-relevant building damage typology from single post-event imagery. Given
a post-event RGB tile and building instance masks (footprints), the model assigns
each building one of five damage-typology classes:
| Class | Name |
|------:|------|
| 0 | Undamaged |
| 1 | Partial Roof Damage |
| 2 | Total Roof Damage |
| 3 | Partial Structural Damage |
| 4 | Total Structural Collapse |
## Files
- `best_model.pth` — the reported checkpoint, selected by validation macro-F1 in the footprint-conditioned setting.
- `config.json` — the exact training configuration used to produce it.
## Architecture
DINOv3 ViT-L/16 backbone (last 4 blocks fine-tuned) → Simple Feature Pyramid
(target stride 8, 128×128 feature map) → mask-pooled instance features → a
two-stage gated damage head (any-damage gate → 4-way damaged-class leaf), with an
auxiliary severity-regression head used during training.
## Training
30 epochs, AdamW (LR 5e-5 heads / 5e-6 backbone), effective batch 32 (2×A100 40GB,
DDP), label smoothing 0.1, EMA (decay 0.9995), long-tailed logit adjustment
(τ=1.0), inverse-square-root class weighting on the leaf head. Trained on
**DamageTriage-Bench** (Hurricane Michael 2018, Hurricane Helene 2024, and the
2025 Los Angeles wildfire complex).
## Results
Macro F1 **0.624** (validation) / **0.619** (held-out test) on the stratified
split; per-class test F1 of 0.91 (Undamaged) and 0.84 (Total Structural Collapse).
Total Roof Damage remains the hardest class (F1 ≈ 0.33).
## Usage
Load these weights with the training/inference code:
[github.com/YimingXiao98/Damage-TriageFormer](https://github.com/YimingXiao98/Damage-TriageFormer)
(MIT). The model expects a 1024×1024 post-event RGB tile and building instance
masks, and returns a 5-class damage-typology probability per building.
## Training data
DamageTriage-Bench: [huggingface.co/datasets/Ymx1025/DamageTriage-Bench](https://huggingface.co/datasets/Ymx1025/DamageTriage-Bench) (CC-BY-NC-4.0).
## Intended use and limitations
Screening-grade decision support for post-disaster triage, not a substitute for
engineering inspection. Footprint-conditioned (assumes building masks are
available). Rare and visually ambiguous roof-damage categories, especially Total
Roof Damage, are the least reliable. Trained on a single seed.
## License
Released under CC-BY-NC-4.0, consistent with the DamageTriage-Bench dataset it was
trained on. Subject to the redistribution terms of the underlying NOAA Emergency
Response Imagery and the source building-footprint layers.
## Citation
```bibtex
@misc{xiao2026damagetriageformerfoundationmodelframeworktypologybased,
title={Damage-TriageFormer: A Foundation-Model Framework for Typology-Based Building Damage Assessment from Mono-Temporal Imagery},
author={Yiming Xiao and Yu-Hsuan Ho and Sanjay Thasma and Junwei Ma and Ali Mostafavi},
year={2026},
eprint={2606.12248},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.12248},
}
```
|