Ymx1025/DamageTriage-Bench

DamageTriage-Bench

DamageTriage-Bench is a footprint-conditioned benchmark for per-building damage typing from single post-event aerial images. Its five classes distinguish roof from structural damage and partial from total affected extent:

Quick statistics

Files

.
├── images/                       # post-event RGB tiles
├── damage/                       # paired RGB damage-typology masks
├── photo_splits.json             # recommended photo-atomic split
├── stratified_splits.json        # superseded tile-level split
├── class_index.json              # per-class tile listings
└── README.md

images/<tile_id>.png and damage/<tile_id>.png are paired by filename.

Annotation format

Each damage file is a unified RGB polygon mask. Background is black; each non-background color gives the class of a labeled building fragment.

Connected components of a class color are treated as labeled fragments. The paper's reported metrics exclude components smaller than 30 pixels.

For damaged buildings, the annotation rubric first distinguishes roof-surface damage from damage extending into structural components. Within each branch, a 50% affected-area threshold separates partial from total damage. The threshold is a pragmatic annotation convention, not a validated operational decision threshold.

Recommended photo-atomic split

Use photo_splits.json for the revised paper results. Within each of the 12 acquisition sub-events, the source photo is the assignment unit: all tiles cut from one source photo remain in the same fold. The split contains 5,231 training, 1,124 validation, and 1,117 test tiles.

This protocol prevents fragments derived from one source photo from crossing folds. It does not reconstruct buildings clipped at tile boundaries, merge repeated fragments within a fold, or rule out the same physical building appearing in overlapping source photos. Reported instance metrics therefore use the visible labeled fragment as the evaluation unit.

stratified_splits.json is retained only to reproduce the originally submitted tile-level experiment. Under that superseded split, fragments of 5,027 audited buildings entered more than one fold; it should not be used for new comparisons.

Validation and test supports for the recommended split

These counts include labeled fragments with area of at least 30 pixels.

Revised reference results

On the recommended split, the revised paper reports:

Both are single-seed results. The crop model assumes the supplied building support and is not a footprint-free detector. Code is provided at <https://github.com/YimingXiao98/Damage-TriageFormer>.

Limitations and intended use

The benchmark supports research on footprint-conditioned damage typing. It does not validate autonomous habitability, safety, inspection-priority, or resource- allocation decisions. The three events do not establish generalization to a new disaster; leave-one-event-out performance in the paper is substantially lower. Rare roof-damage classes have small support, and verification-style annotation review is not equivalent to blind independent labeling.

Use of the imagery and footprint-derived labels remains subject to the terms of the underlying NOAA Emergency Response Imagery and source footprint layers.

License

CC BY-NC 4.0 for non-commercial research use, subject to the underlying source terms noted above.

Citation

@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}, 
}