| --- |
| 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 |
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| **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 figure previews, numeric summaries, and data-source notes. The raw data are **not** redistributed. |
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| 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. |
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| ## Release Contents |
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| **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`. |
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| **Model code.** The compact U-Net definition is provided in `models/wildfire_fm/modeling_unet.py`, with a short loading example below. |
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| **Evaluation artifacts.** A compiled paper PDF, final-paper figure previews, and sanitized compact CSV/JSON summaries are included under `paper/`, `assets/`, `paper_outputs/`, and `artifacts/results/`. Manuscript TeX, BibTeX, and TikZ source files are intentionally not included in this model release. |
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| **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. |
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| ## 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 |
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| The paper evaluates WildFIRE-FM and ten Earth-FM comparators under fixed task contracts. The top card reports the best final-paper mean for each displayed task contract, with the winning backbone named in the card. The corresponding values are: |
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| - **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`, WildFIRE-FM; lower is better. |
| - **Analog retrieval nDCG@10:** `0.5099 ± 0.0336`, WildFIRE-FM. |
| - **Smoke PM2.5 RMSE:** `4.4403 ± 0.0488`, AlphaEarth; lower is better. |
| - **Extreme-heat RMSE-C:** `0.2179 ± 0.0043`, WildFIRE-FM; lower is better. |
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| The compiled paper PDF is available at `paper/wildfire_fm_evaluation_contracts.pdf`. The public release also includes sanitized CSV/JSON summaries used to audit the displayed values. Manuscript table TeX is not included. |
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| ### Fixed-Contract Checks From The Final Paper |
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| **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. |
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| **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. |
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| **Primary-task rank changes.** This final-paper figure summarizes rank changes across fixed primary-task contracts. |
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|  |
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| ## Data Sources |
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| The study uses public or provider-hosted resources, but the processed data are not bundled here: |
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| - 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. |
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| See `data_sources/DATA_SOURCES.md` for source roles and access links. |
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| ## Reproducing Released Paper Outputs |
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| The lightweight check verifies the released sanitized artifacts from compact summaries. It does not require raw data or GPUs. |
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|
| ```bash |
| python3 scripts/reproduce_paper_outputs.py |
| ``` |
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| 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/`. |
|
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| ## Repository Layout |
|
|
| ```text |
| models/wildfire_fm/ model code, manifests, and checkpoint metadata |
| paper/ compiled paper PDF only; no TeX source |
| paper_outputs/ final-paper figure PDFs retained for reproducibility |
| artifacts/results/ sanitized 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 |
| ``` |
|
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| ## Limitations |
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| 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} |
| } |
| ``` |
|
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| The citation will be updated with arXiv metadata after the preprint is public. |
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|
