--- license: mit task_categories: - image-classification - time-series-forecasting language: - en tags: - wildfire - remote-sensing - earth-observation - canada - benchmark - hard-negative-mining size_categories: - n>1T --- # 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_.h5` | ~14 GB | Pre-extracted 10-day input windows + labels for every sample (positives and negatives). One HDF5 file per cache. | | `samples_variant_.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//__.tar ``` After extraction, individual files are named `YYYY_MM_DD.tif` for daily modalities or `_YYYY.tif` for static / annual modalities. ### Download and Extraction ```python 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: ```python 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_.json` inside each cache. ## File Format `windows_.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`: ```python 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: ```python 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.