AnonymousData4NeurIPS/MPCFire

FireMPC: A Pan-Canadian Wildfire Forecasting Benchmark

FireMPC is a pan-Canadian wildfire risk benchmark covering approximately one billion hectares across all fifteen Canadian terrestrial ecozones at 1 km daily resolution from 2000 to 2025, integrating 55 drivers across fuel, terrain, anthropogenic, and meteorological families.

This release provides four pre-built sample caches that share the same underlying data cube but differ in their training and test sample construction strategies, enabling controlled study of FWI-guided Hard Negative Mining (FWI-HNM) versus random negative sampling.

Repository Contents

MPCFire/
├── cache_A/             # Train: random sampling  | Test: FWI-HNM
├── cache_Y/             # Train: random sampling  | Test: random sampling
├── cache_G/             # Train: FWI-HNM          | Test: FWI-HNM
├── cache_H/             # Train: FWI-HNM          | Test: random sampling
└── Entire_Canada_Maps/  # Raw driver rasters, 13 modalities x 26 years (yearly tar archives)

Each cache directory contains exactly three files:

Variant Design

The four caches form a 2x2 ablation grid that decouples the negative-sampling strategy used during training from the strategy used during evaluation:

• —FWI-HNM (FWI-guided Hard Negative Mining) scores every non-fire candidate with a calibrated six-component CFFDRS composite (FFMC, DMC, DC, ISI, BUI, FWI), partitions the pool at the median, and combines hard negatives (fire-weather-matched non-ignitions) with representative negatives (low-danger baseline) in equal proportions.
• —Random sampling draws negatives uniformly from the non-fire candidate pool (firemask = 5).

Comparing rows isolates the effect of the training-pool construction; comparing columns isolates the effect of the evaluation-pool construction. The diagonal pair (cache_A, cache_G) corresponds to the standard production setup; the off-diagonal pair (cache_Y, cache_H) is used to verify that any FWI-HNM advantage reflects genuine boundary hardening rather than train-test distributional alignment.

Raw Driver Maps (Entire_Canada_Maps/)

Entire_Canada_Maps/ provides the underlying raster stack used to build the sample caches above. It covers the full Canadian landmass for 2000-2025 (26 years) and is organised into 13 modality subfolders that correspond to the drivers listed in Table 1 of the paper. Each subfolder contains one tar archive per year; each archive holds that year's daily (or annual / static) GeoTIFFs.

All rasters are stored with integer scaling (scaledInt) to reduce volume.

Subfolder Index

File Naming

Entire_Canada_Maps/<modality>/<modality>__<YYYY>.tar

After extraction, individual files are named YYYY_MM_DD.tif for daily modalities or <modality>_YYYY.tif for static / annual modalities.

Download and Extraction

from huggingface_hub import hf_hub_download
import tarfile, pathlib

tar_path = hf_hub_download(
    repo_id="AnonymousData4NeurIPS/MPCFire",
    repo_type="dataset",
    filename="Entire_Canada_Maps/ERA5/ERA5__2020.tar",
)
out = pathlib.Path("./ERA5_2020")
with tarfile.open(tar_path) as tf:
    tf.extractall(out)

To pull an entire modality:

from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="AnonymousData4NeurIPS/MPCFire",
    repo_type="dataset",
    allow_patterns=["Entire_Canada_Maps/ERA5/*"],
)

Splits

A temporal hold-out is used throughout:

The three-year test window deliberately covers the record-setting 2023 fire season alongside the more typical 2024 and 2025 seasons. All splits maintain a fixed 1:2 positive-to-negative ratio.

Sample identifiers and split assignments are stored in samples_variant_<X>.json inside each cache.

File Format

windows_<hash>.h5 is a single HDF5 file with pre-extracted 10-day driver windows and binary labels. The hash in the filename identifies the windowing configuration (10-day backward window, 1 km patches) and is shared across all four caches because the underlying input cube is identical; only the positive/negative selection differs per variant.

norm_stats.npz provides per-channel mean and standard deviation arrays. The fire-label channel and the categorical land-cover channel are excluded from z-score normalisation.

Reading the Data

Quick example using huggingface_hub:

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="AnonymousData4NeurIPS/MPCFire",
    repo_type="dataset",
    allow_patterns=["cache_G/*"],   # download a single variant
)

Then load the HDF5 file and the sample index:

import h5py, json, numpy as np

cache = f"{local_dir}/cache_G"
with open(f"{cache}/samples_variant_G.json") as f:
    samples = json.load(f)
norm = np.load(f"{cache}/norm_stats.npz")
h5 = h5py.File(next(p for p in __import__('os').listdir(cache) if p.endswith('.h5')), "r")

Citation

This dataset accompanies a paper currently under anonymous peer review. A citation entry will be added on acceptance.

License

Released under the MIT License. The dataset is built from publicly available products (MODIS, ERA5-Land, ASTER, WorldPop, OpenStreetMap); please consult the licenses of the upstream sources for redistribution of derivative products.