diff --git a/.gitattributes b/.gitattributes
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diff --git a/README.md b/README.md
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+---
+license: mit
+task_categories:
+  - image-classification
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - wildfire
+  - remote-sensing
+  - earth-observation
+  - canada
+  - benchmark
+  - hard-negative-mining
+size_categories:
+  - 100K<n<1M
+---
+
+# 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
+
+```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_<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`:
+
+```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.
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