README.md

---
license: mit
tags:
  - wildfire
  - geospatial
  - weather
  - earth-observation
  - foundation-models
  - evaluation
  - pytorch
pipeline_tag: image-segmentation
library_name: pytorch
pretty_name: WildFIRE-FM
---

# WildFIRE-FM

![WildFIRE-FM summary](assets/wildfire_fm_model_card.svg)

**WildFIRE-FM** is a wildfire-specialized regional reference backbone for 12-hour gridded wildfire occupancy prediction on a 5 km California grid. It is released with five seeded PyTorch checkpoints, model code, final-paper artifacts, and data-source notes. The raw data are **not** redistributed.

The model is intended as a reproducible reference backbone for fixed-contract wildfire evaluation, not as a general global wildfire forecasting product. It was trained with regional weather, active-fire supervision, static fuel/canopy/exposure layers, and event-level wildfire resources used by supporting tasks in the paper.

## Release Contents

![Release contents](assets/release_contents.svg)

**Weights.** Five seeded checkpoints are available at `models/wildfire_fm/checkpoints/seed_*/best_firms_prauc.pt`. Each file is listed with SHA-256 and byte size in `models/wildfire_fm/checkpoint_manifest.json`.

**Model code.** The compact U-Net definition is provided in `models/wildfire_fm/modeling_unet.py`, with a short loading example below.

**Paper artifacts.** The final manuscript PDF and the final paper figures/tables are included under `paper/` and `paper_outputs/`. Compact CSV/JSON summaries are under `artifacts/results/`.

**Data notes.** Data sources and access entry points are documented in `data_sources/DATA_SOURCES.md`; users must obtain source data from the original providers.

## Model Details

| Field | Value |
|---|---|
| Task | 12-hour gridded wildfire occupancy prediction |
| Grid | California regional grid, 5 km, EPSG:5070 |
| Inputs | 16 channels: weather fields, validity masks, static fuel/canopy/exposure layers |
| Architecture | Compact U-Net with occupancy and auxiliary spatial-support heads |
| Training split | June-August 2024 train, September 2024 validation, October 2024 test |
| Released seeds | 1, 7, 42, 99, 123 |

## Quick Load

```python
import torch
from models.wildfire_fm.modeling_unet import UNetSmallFlex

model = UNetSmallFlex(
    in_ch=16,
    base=32,
    dropout=0.1,
    norm_type="group",
    norm_groups=8,
    use_aux_spatial_head=True,
)
checkpoint = torch.load(
    "models/wildfire_fm/checkpoints/seed_1/best_firms_prauc.pt",
    map_location="cpu",
)
state = checkpoint.get("model", checkpoint)
model.load_state_dict(state)
model.eval()
```

The checkpoint expects the same 16-channel gridded input described in the paper and in `data_sources/DATA_SOURCES.md`. This repository does not include raw HRRR, FIRMS, LANDFIRE, WRC, LandScan, WFIGS, MTBS, or comparator feature caches.

## Evaluation Snapshot

The paper evaluates WildFIRE-FM and ten Earth-FM comparators under fixed task contracts. A few final-paper WildFIRE-FM values are:

- **Occupancy union F1:** `59.0656 ± 2.7372` percent.
- **Fire-spread AP:** `30.0900 ± 1.2500` percent.
- **Final burned-area log-RMSE:** `1.1657 ± 0.0126`, where lower is better.
- **Analog retrieval nDCG@10:** `0.5099 ± 0.0336`.
- **Smoke PM2.5 RMSE:** `4.4646 ± 0.0060`, where lower is better.
- **Extreme-heat RMSE-C:** `0.2179 ± 0.0043`, where lower is better.

The full final-paper tables are included as TeX blocks under `paper_outputs/tables/`.

### Fixed-Contract Checks From The Final Paper

**Head-selection regret.** This final-paper figure shows that choosing a lightweight head by a ranking metric can lose decision performance under the same frozen features.

![Head-selection regret](assets/selection_regret_final.png)

**Supporting-task rank map.** This final-paper figure shows that model ordering changes across burned area, analog retrieval, smoke PM2.5, and extreme heat task contracts.

![Supporting task rank map](assets/supporting_rank_map_final.png)

**Primary-task rank changes.** This final-paper figure summarizes rank changes across fixed primary-task contracts.

![Primary rank changes](assets/primary_rank_change_final.png)

## Data Sources

The study uses public or provider-hosted resources, but the processed data are not bundled here:

- NOAA HRRR fields for regional weather inputs.
- NASA FIRMS active-fire detections for occupancy supervision.
- LANDFIRE fuel and canopy layers for static landscape context.
- Wildfire Risk to Communities housing density and LandScan population for exposure context.
- WFIGS and MTBS event-level resources for burned-area and analog tasks.
- External Earth-FM/backbone assets for comparator features.

See `data_sources/DATA_SOURCES.md` for source roles and access links.

## Reproducing Released Paper Outputs

The lightweight check verifies the released final-paper artifacts from compact summaries. It does not require raw data or GPUs.

```bash
python3 scripts/reproduce_paper_outputs.py
```

Full raw-data reruns require separately downloaded source data, local feature caches, and cluster-specific paths. Sanitized reference scripts and a Slurm template are provided under `experiments/`.

## Repository Layout

```text
models/wildfire_fm/        model code, manifests, and checkpoint metadata
paper/                     final manuscript PDF and LaTeX source snapshot
paper_outputs/             final paper figures and TeX table blocks
artifacts/results/         compact CSV/JSON summaries for released outputs
experiments/               sanitized raw-rerun references and Slurm template
data_sources/              source-data roles and access notes
scripts/                   artifact verification and figure/table rebuild helpers
```

## Limitations

WildFIRE-FM is a regional reference model trained for the paper's fixed-contract comparisons. Use outside the California regional grid requires new preprocessing, validation, and contract-specific evaluation. The repository does not provide operational alerts, raw data, or third-party comparator weights.

## Citation

```bibtex
@misc{wildfire_fm_evaluation_contracts_2026,
  title = {Does Your Wildfire Prediction Model Actually Work, or Just Score Well?},
  author = {Yangshuang Xu and Yuyang Dai and Liling Chang and Qi Wang and Yushun Dong},
  year = {2026},
  note = {WildFIRE-FM model and fixed-contract wildfire evaluation artifacts}
}
```

The citation will be updated with arXiv metadata after the preprint is public.