WildFIRE-FM

A wildfire-specialized reference backbone for fixed-contract Earth-FM transfer evaluation

Five seeded PyTorch checkpoints, paper-aligned evaluation artifacts, final figure previews, and source-data access notes for 12-hour wildfire occupancy prediction on a 5 km California grid.

Read the paper PDF · Download checkpoints · Load the model · Inspect task-contract results

5 Seeded Checkpoints · 16-Channel Gridded Input · 12-Hour Occupancy Lead · 6 Fixed Task-Contract Views

Why WildFIRE-FM · Release Navigation · Visual Tour · Quick Start · Task Snapshot · Data Sources · How to Cite

WildFIRE-FM overview

Release Navigation

Paper PDF

Read the compiled manuscript that defines the fixed-contract wildfire transfer setting and reports the final results.

Model Checkpoints

Access five seeded WildFIRE-FM weights with manifest metadata and SHA-256 hashes for release auditing.

Model Code

Inspect the compact U-Net implementation used to load the released wildfire occupancy backbone.

Quick Start

Clone the repository, instantiate the model, load a seeded checkpoint, and run the artifact check.

Task Snapshot

View the final-paper task-contract summary for occupancy, spread, retrieval, burned area, smoke, and heat.

Numeric Artifacts

Use sanitized CSV and JSON summaries that back the public model-card tables and figure previews.

Visual Tour

Browse final-paper previews for matching-rule sensitivity, selection regret, and task-form ranking changes.

Data Sources

See the public provider links and roles for HRRR, FIRMS, LANDFIRE, WRC, LandScan, WFIGS, and MTBS.

Repository Layout

Find where checkpoints, manifests, scripts, paper outputs, and release documentation live in this Hub repo.

Why WildFIRE-FM

Wildfire Earth-FM transfer scores depend strongly on the contract used for comparison: task form, metric, matching rule, spatial scope, and head-selection criterion. The paper studies these choices by holding outputs or features fixed and then changing only the evaluation contract.

WildFIRE-FM is the in-region reference model used in those comparisons. It is trained for 12-hour gridded wildfire occupancy on a California 5 km grid, then evaluated under the same task-specific contracts as the transferred Earth-FM backbones.

This Hub repository is model-first. It provides the released weights, model-loading code, compact result summaries, paper previews, and source-data access notes needed to inspect the release boundary before running larger local reproductions.

Key Features

Wildfire-specialized reference model: Compact U-Net for 12-hour occupancy prediction on a projected California grid.
Five seeded checkpoints: Seeds 1, 7, 42, 99, and 123 are released with manifest hashes.
Fixed-contract artifacts: Compact summaries cover matching-rule, head-selection, and task-form comparisons from the final paper.
Source-data aware release: Provider links and data roles are documented for each public resource used by the study.
Compiled paper PDF: The readable manuscript is available directly from the Hub release.
Paper-aligned previews: Figure assets summarize selection regret, supporting-task rank changes, and primary-task rank changes.

Reference model for wildfire transfer

WildFIRE-FM combines regional weather, active-fire supervision, and static fuel, canopy, housing, and population context on the same grid used by the occupancy contract.

Contract-first evaluation artifacts

The released summaries keep metric roles separate across decision, overlap, ranking, regression, and supporting-task checks.

Local-data reproducibility path

Full raw-data reruns use provider-hosted sources and local feature caches. The Hub release documents those inputs and provides lightweight artifact checks.

Final-paper result previews

The model card highlights the paper's fixed-output, fixed-feature, and task-form findings without requiring manuscript source files.

Visual Tour

Release contents

Release contents. The Hub repository is organized around weights, model code, source-data notes, compact artifacts, and final-paper visual previews.

Head-selection regret

Head-selection regret. Choosing a lightweight head by a ranking metric can lose decision performance under the same frozen features.

Supporting task rank map

Supporting-task rank map. Backbone ordering changes across burned area, analog retrieval, smoke PM2.5, and extreme heat task contracts.

Primary rank changes

Primary-task rank changes. Primary-task rankings change under fixed matching-rule and task-form choices.

At A Glance

Signal	Release value
Core task	12-hour gridded wildfire occupancy prediction
Region/grid	California regional grid, 5 km, EPSG:5070
Input channels	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 checkpoints	5 seeded PyTorch checkpoints
Data access path	Source links and roles documented in `data_sources/`
Paper artifact path	Compiled PDF in `paper/`; manuscript source managed in the authors' paper workspace

Hugging Face Release

This Hub repository is a model release with paper-aligned reproducibility artifacts.

Folder	Purpose	Example
`models/wildfire_fm/`	Model code, manifests, checkpoint metadata	`models/wildfire_fm/modeling_unet.py`
`models/wildfire_fm/checkpoints/`	Five seeded PyTorch checkpoints	`seed_1/best_firms_prauc.pt`
`paper/`	Compiled paper PDF	`wildfire_fm_evaluation_contracts.pdf`
`assets/`	Hub-page visuals and final-paper previews	`selection_regret_final.png`
`paper_outputs/figures/`	Selected final-paper figure PDFs	`fig_rank_heatmap1.pdf`
`artifacts/results/`	Sanitized compact numeric summaries	`selection_regret_scope_sweep_20260505.csv`
`data_sources/`	Public data-source roles and access notes	`DATA_SOURCES.md`
`experiments/`	Sanitized raw-rerun references and Slurm template	`slurm/submit_template.sbatch`

Full local reruns require users to prepare the provider-hosted raw data and comparator assets described in data_sources/.

Quick Start

Clone the Hub repository or download the files you need:

git clone https://huggingface.co/RAI-Lab/Wildfire-FM
cd Wildfire-FM

Load a seeded checkpoint:

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()

Verify the public release artifacts:

python3 scripts/reproduce_paper_outputs.py

The checkpoint expects the same 16-channel gridded input described in the paper and in data_sources/DATA_SOURCES.md.

Task-Contract Snapshot

The card below reports the best final-paper mean for each displayed task contract, with the winning backbone named in the final column.

Task contract	Best final-paper mean	Winner
Occupancy union F1	`60.1506 ± 7.5865` percent	ClimaX
Fire-spread spatial F1	`80.9700 ± 2.0200` percent	WildFIRE-FM
Final burned-area log-RMSE	`1.1657 ± 0.0126`, lower is better	WildFIRE-FM
Analog retrieval nDCG@10	`0.5099 ± 0.0336`	WildFIRE-FM
Smoke PM2.5 RMSE	`4.4403 ± 0.0488`, lower is better	AlphaEarth
Extreme-heat RMSE-C	`0.2179 ± 0.0043`, lower is better	WildFIRE-FM

The release provides sanitized CSV/JSON summaries used to audit displayed values.

Data Sources

The study uses public or provider-hosted resources. Source roles and access paths are documented in data_sources/DATA_SOURCES.md.

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 and backbone assets for comparator features.

Repository Layout

WildFIRE-FM/
  README.md
  LICENSE
  requirements.txt
  models/wildfire_fm/
    modeling_unet.py
    checkpoint_manifest.json
    checkpoints/seed_*/best_firms_prauc.pt
  paper/
    wildfire_fm_evaluation_contracts.pdf
  assets/
    wildfire_fm_model_card.svg
    release_contents.svg
    selection_regret_final.png
    supporting_rank_map_final.png
    primary_rank_change_final.png
  paper_outputs/figures/
    matching.pdf
    fig_task_contract_tiles.pdf
    fig_selection_regret_scatter.pdf
    fig_rank_heatmap1.pdf
  artifacts/
    manifests/
    results/
  data_sources/
    DATA_SOURCES.md
  experiments/
    raw_reference/
    slurm/
  scripts/
    audit_release.py
    reproduce_paper_outputs.py

How To Cite

If you use WildFIRE-FM, the released checkpoints, the fixed-contract evaluation artifacts, or the paper-aligned scripts, please cite:

@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.

News

2026-05-18 - The model card was reorganized with a larger navigation panel and final-paper visual previews.
2026-05-13 - The Hub release added the compiled paper PDF without checklist pages.
2026-05-13 - Five seeded WildFIRE-FM checkpoints were uploaded with SHA-256 metadata.

Scope

WildFIRE-FM is scoped to the paper's California regional grid and fixed-contract wildfire transfer comparisons. Use in a new region or grid should start with local preprocessing, validation, and contract-specific evaluation. The release is designed for research inspection, artifact-level checking, and model-loading experiments rather than operational alerting.

License

This release is provided under the MIT license. External data and comparator assets remain governed by their original providers and terms of use.

WildFIRE-FM is released to make wildfire Earth-FM transfer comparisons easier to inspect, reproduce at the artifact level, and evaluate under explicit contracts.

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