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:

File	Size	Description
windows_<hash>.h5	~14 GB	Pre-extracted 10-day input windows + labels for every sample (positives and negatives). One HDF5 file per cache.
samples_variant_<X>.json	~4.4 MB	Sample index: train / val / test split assignments, sample identifiers, and metadata.
norm_stats.npz	~2 KB	Channel-wise mean and standard deviation used for input normalisation. Skips the fire-mask channel and the categorical land-cover channel.

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:

	Test = FWI-HNM	Test = Random
Train = Random	cache_A	cache_Y
Train = FWI-HNM	cache_G	cache_H

• 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

Folder	Channels	Source	Cadence	Notes
DEMs/	DEM, Slope, Aspect (sin/cos), Hillshade, TPI, TWI (7 channels)	ASTER GDEM	Static	Elevation and derived terrain indices; broadcast across the daily axis
ERA5/	temperature_2m, u/v_component_of_wind_10m, snow_cover, total_precipitation_sum, surface_latent_heat_flux_sum, dewpoint_temperature_2m, surface_pressure, volumetric_soil_water_layer_1-4, temperature_2m_max, skin_temperature_max, potential_evaporation_sum, total_evaporation_sum, skin_reservoir_content, surface_net_solar_radiation_sum (18 channels)	ERA5-Land Daily Aggregate (ECMWF/ERA5_LAND/DAILY_AGGR)	Daily	Atmospheric reanalysis fields
FWI/	FFMC, DMC, DC, ISI, BUI, FWI (6 channels)	CFFDRS (ERA5-driven)	Daily	Canadian Forest Fire Weather Index components
MCD09CMG/	Coarse Resolution Brightness Temperature Bands 20 / 21 / 31 / 32 (4 channels)	MOD/MYD09CMG	Daily	Coarse-resolution composite brightness temperatures
MCD09GA/	Bands 1, 2, 3, 7 (4 channels)	MOD/MYD09GA	Daily	QA-filtered, gap-filled surface reflectance
MCD11A1/	LST_Day_1km, LST_Night_1km, Emis_31, Emis_32 (4 channels)	MOD/MYD11A1	Daily	QA-filtered, gap-filled land surface temperature and emissivity
MCD12Q1/	Land Cover Class	MCD12Q1	Annual	Land cover / land use class; broadcast across the daily axis
MCD14A1/	Active Fire (binary)	MOD/MYD14A1	Daily	Supervision target only; not included as a model input channel
MCD15A3H/	LAI, FPAR (2 channels)	MCD15A3H	Daily (from 4-day composite, interpolated)	Leaf area index / fraction of absorbed PAR
NDVI_EVI/	NDVI, EVI (2 channels)	MODIS-derived	Daily	Vegetation activity indices
OSMs/	Road / Powerline / Building / Water Density (4 channels)	OSM-derived	Static	Infrastructure accessibility; broadcast across the daily axis
VPD/	Vapor Pressure Deficit	ERA5-derived	Daily	Atmospheric moisture demand
Worldpop/	Population Density	WorldPop	Annual	Human population density; broadcast across the daily axis

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:

Split	Years
Train	2000 - 2019
Validation	2020 - 2022
Test	2023 - 2025

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.