README.md

---
license: cc-by-nc-4.0
language:
- en
tags:
- remote-sensing
- damage-assessment
- segmentation
- building-instance-classification
- post-disaster
- satellite-imagery
size_categories:
- 1K<n<10K
task_categories:
- image-segmentation
- image-classification
pretty_name: DamageTriage-Bench
configs:
- config_name: default
  data_files:
  - split: train
    path: "stratified_splits.json"
  - split: validation
    path: "stratified_splits.json"
  - split: test
    path: "stratified_splits.json"
---

# DamageTriage-Bench

Per-building post-disaster damage assessment benchmark for satellite
imagery, with a fine-grained 5-class **typology** taxonomy (no damage,
partial/total roof damage, partial/total structural damage) rather than
the FEMA-style 4-class severity scale used by xBD.

## Quick stats

| | |
|---|---|
| Tiles | 7,472 (1024 × 1024 PNG, sub-meter GSD) |
| Total building instances | ≈ 76,500 |
| Damage classes | 5 (typology) |
| Disasters | 3 (Hurricane Michael 2018, Hurricane Helene 2024, 2025 LA Palisades/Eaton wildfire complex) |
| Sub-events | 12 |
| Train / Val / Test split | 5,229 / 1,120 / 1,123 tiles (stratified per sub-event) |

## Directory layout

```
.
├── images/                       # 7,472 post-event RGB tiles (1024×1024 PNG)
├── damage/                       # 7,472 per-tile unified RGB polygon masks
├── stratified_splits.json        # Train / Val / Test split (canonical)
├── class_index.json              # Per-class tile listings
└── README.md (this file)
```

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

## Annotation format

Annotations are **unified RGB polygon masks**: a single 1024 × 1024
RGB mask per tile, where each pixel's colour identifies the damage
class of the building instance it belongs to (or background).

| Colour (R, G, B) | Class |
|---|---|
| `(0, 0, 0)` | Background / ignore |
| `(255, 255, 255)` | Undamaged |
| `(0, 255, 83)` | Partial Roof Damage |
| `(246, 255, 11)` | Total Roof Damage |
| `(255, 138, 18)` | Partial Structural Damage |
| `(255, 0, 0)` | Total Structural Collapse |

Connected components of the same colour correspond to building
instances. Polygons do not overlap.

### Per-class instance counts (val / test)

| Class | Val (n) | Val (%) | Test (n) | Test (%) |
|---|---:|---:|---:|---:|
| 0. Undamaged | 9,288 | 73.1 | 9,346 | 74.6 |
| 1. Partial Roof Damage | 1,657 | 13.0 | 1,561 | 12.5 |
| 2. Total Roof Damage | 165 | 1.3 | 145 | 1.2 |
| 3. Partial Structural Damage | 543 | 4.3 | 471 | 3.8 |
| 4. Total Structural Collapse | 1,062 | 8.3 | 999 | 8.0 |
| **Total** | **12,715** | **100** | **12,522** | **100** |

The training split follows the same long-tail distribution with the
remaining ~60,000 instances.

## Disasters and sub-events

12 acquisition sub-events span three disasters:

| Sub-event | Hazard | Train | Val | Test | Total |
|---|---|---:|---:|---:|---:|
| `wildfire_1` | WF (LA) | 220 | 47 | 47 | 314 |
| `wildfire_2` | WF (LA) | 208 | 45 | 44 | 297 |
| `wildfire_3` | WF (LA) | 92 | 20 | 20 | 132 |
| `wildfire_4` | WF (LA) | 191 | 41 | 41 | 273 |
| `wildfire_5` | WF (LA) | 201 | 43 | 43 | 287 |
| `wildfire_6` | WF (LA) | 86 | 18 | 19 | 123 |
| `wildfire_7` | WF (LA) | 146 | 31 | 32 | 209 |
| `wildfire_8` | WF (LA) | 99 | 21 | 22 | 142 |
| `hurricane_michael_2018` | HUR | 1,263 | 271 | 270 | 1,804 |
| `hurricane_helene_2024_v1` | HUR | 220 | 47 | 48 | 315 |
| `hurricane_helene_2024_v2` | HUR | 860 | 184 | 185 | 1,229 |
| `hurricane_helene_2024_late` | HUR | 1,643 | 352 | 352 | 2,347 |

The eight `wildfire_*` sub-events partition the 2025 Los Angeles
Palisades / Eaton wildfire complex into spatially disjoint regions.
The three `hurricane_helene_2024_*` sub-events correspond to separate
Helene acquisitions. `hurricane_michael_2018` covers the
2018-10-11 NOAA Emergency Response Imagery for Hurricane Michael.

## Annotation rubric

Building polygons were manually annotated at the per-instance level
by trained annotators following a damage-typology rubric jointly
developed with structural-engineering domain experts.

Class 0 denotes buildings with no visible roof or structural damage.
For damaged buildings, labels follow a two-step rule:

1. Does visible damage extend below the roof surface into structural
   components? If **no**, the instance is *roof damage*. If **yes**,
   it is *structural damage*.
2. Within each branch, a **50 %** affected-area threshold separates
   *partial* from *total*. Area is estimated relative to the visible
   roof or building footprint.

Each polygon is assigned exactly one of the five typology classes.

## Splits

`stratified_splits.json` defines a per-sub-event 70 / 15 / 15 split.
Within each sub-event, tiles are partitioned so that every split has
the same per-sub-event proportions as the full dataset. Splits are
over **tiles**, not over building instances.

Test fold is never read during training or model selection.

```python
import json
splits = json.load(open("stratified_splits.json"))
splits["seed"]                    # 42
splits["ratios"]                  # [0.7, 0.15, 0.15]
splits["all"]["train"][:5]        # first 5 train tile IDs
splits["events"][k]["test"][:5]   # first 5 test tile IDs from sub-event k
```

## How to load

```python
from datasets import load_dataset

# (After upload) the canonical loader.
ds = load_dataset("<your-hf-org>/DamageTriage-Bench")
```

For PyTorch training pipelines, the companion code repository
[<your-github-org>/dinov3-damage-assessment](.) provides a
`get_dataloaders()` entry point that handles the unified-mask
decoding and the stratified split.

## Reproducibility

The full training recipe that produced the headline macro-F1 = 0.619
on the test split is documented in the companion code repository
(`AGENTS.md` → §"v11 reference recipe").

## License

CC BY-NC 4.0 — non-commercial research use only.

## Acknowledgements

Imagery for the Michael event is sourced from the NOAA Emergency
Response Imagery program. Imagery for the Helene and LA wildfire
events is sourced from publicly released post-event capture flights;
sources and ground-sampling distance are listed per-event in the
companion paper.

## Citation

```bibtex
@article{damagetriage2026,
  title   = {Damage-TriageFormer: Post-Event Foundation Models for
             Decision-Relevant Building Damage Typology},
  author  = {Xiao, Yiming and Mostafavi, Ali},
  journal = {tba},
  year    = {2026},
}
```