--- dataset_info: - config_name: Alberta features: - name: date dtype: timestamp[s] - name: doy sequence: int64 - name: 10x sequence: array3_d: shape: - 4 - 264 - 264 dtype: uint8 - name: 20x sequence: array3_d: shape: - 6 - 132 - 132 dtype: uint8 - name: 60x sequence: array3_d: shape: - 3 - 44 - 44 dtype: uint8 - name: loc dtype: array3_d: shape: - 2 - 264 - 264 dtype: float32 - name: labels dtype: array2_d: shape: - 264 - 264 dtype: uint8 - name: tab_cds dtype: array2_d: shape: - 8 - 6 dtype: float32 - name: tab_era5 dtype: array2_d: shape: - 8 - 45 dtype: float32 - name: tab_modis dtype: array2_d: shape: - 8 - 7 dtype: float32 - name: env_cds dtype: array4_d: shape: - 8 - 6 - 13 - 13 dtype: float32 - name: env_cds_loc dtype: array3_d: shape: - 13 - 13 - 2 dtype: float32 - name: env_era5 dtype: array4_d: shape: - 8 - 45 - 32 - 32 dtype: float32 - name: env_era5_loc dtype: array3_d: shape: - 32 - 32 - 2 dtype: float32 - name: env_modis11 dtype: array4_d: shape: - 8 - 3 - 16 - 16 dtype: float32 - 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4 - 264 - 264 dtype: uint8 - name: 20x sequence: array3_d: shape: - 6 - 132 - 132 dtype: uint8 - name: 60x sequence: array3_d: shape: - 3 - 44 - 44 dtype: uint8 - name: loc dtype: array3_d: shape: - 2 - 264 - 264 dtype: float32 - name: labels dtype: array2_d: shape: - 264 - 264 dtype: uint8 - name: tab_cds dtype: array2_d: shape: - 8 - 6 dtype: float32 - name: tab_era5 dtype: array2_d: shape: - 8 - 45 dtype: float32 - name: tab_modis dtype: array2_d: shape: - 8 - 7 dtype: float32 - name: env_cds dtype: array4_d: shape: - 8 - 6 - 13 - 13 dtype: float32 - name: env_cds_loc dtype: array3_d: shape: - 13 - 13 - 2 dtype: float32 - name: env_era5 dtype: array4_d: shape: - 8 - 45 - 32 - 32 dtype: float32 - name: env_era5_loc dtype: array3_d: shape: - 32 - 32 - 2 dtype: float32 - name: env_modis11 dtype: array4_d: shape: - 8 - 3 - 16 - 16 dtype: float32 - name: env_modis11_loc dtype: array3_d: shape: - 16 - 16 - 2 dtype: float32 - name: env_modis13_15 dtype: array4_d: shape: - 8 - 4 - 32 - 32 dtype: float32 - name: env_modis13_15_loc dtype: array3_d: shape: - 32 - 32 - 2 dtype: float32 - name: env_doy sequence: int64 - name: region dtype: string - name: tile_id dtype: int32 - name: file_id dtype: string - name: fwi dtype: float32 splits: - name: train num_bytes: 23689097608 num_examples: 4226 - name: validation num_bytes: 10380885424 num_examples: 1847 - name: test num_bytes: 15896916432 num_examples: 2511 - name: test_hard num_bytes: 15158568432 num_examples: 2184 download_size: 44783265265 dataset_size: 65125467896 configs: - config_name: Alberta data_files: - split: train path: Alberta/train-* - split: validation path: Alberta/validation-* - split: test path: Alberta/test-* - split: test_hard path: Alberta/test_hard-* - config_name: British Columbia data_files: - split: train path: British Columbia/train-* - split: validation path: British Columbia/validation-* - split: test path: British Columbia/test-* - split: test_hard path: British Columbia/test_hard-* - config_name: Manitoba data_files: - split: train path: Manitoba/train-* - split: validation path: Manitoba/validation-* - split: test path: Manitoba/test-* - split: test_hard path: Manitoba/test_hard-* - config_name: New Brunswick data_files: - split: train path: New Brunswick/train-* - split: test path: New Brunswick/test-* - split: test_hard path: New Brunswick/test_hard-* - config_name: Newfoundland and Labrador data_files: - split: train path: Newfoundland and Labrador/train-* - split: validation path: Newfoundland and Labrador/validation-* - split: test path: Newfoundland and Labrador/test-* - split: test_hard path: Newfoundland and Labrador/test_hard-* - config_name: Northwest Territories data_files: - split: train path: Northwest Territories/train-* - split: validation path: Northwest Territories/validation-* - split: test path: Northwest Territories/test-* - split: test_hard path: Northwest Territories/test_hard-* - config_name: Nova Scotia data_files: - split: train path: Nova Scotia/train-* - split: validation path: Nova Scotia/validation-* - split: test path: Nova Scotia/test-* - split: test_hard path: Nova Scotia/test_hard-* - config_name: Nunavut data_files: - split: train path: Nunavut/train-* - split: validation path: Nunavut/validation-* - split: test path: Nunavut/test-* - split: test_hard path: Nunavut/test_hard-* - config_name: Ontario data_files: - split: train path: Ontario/train-* - split: validation path: Ontario/validation-* - split: test path: Ontario/test-* - split: test_hard path: Ontario/test_hard-* - config_name: Quebec data_files: - split: train path: Quebec/train-* - split: validation path: Quebec/validation-* - split: test path: Quebec/test-* - split: test_hard path: Quebec/test_hard-* - config_name: Saskatchewan data_files: - split: train path: Saskatchewan/train-* - split: validation path: Saskatchewan/validation-* - split: test path: Saskatchewan/test-* - split: test_hard path: Saskatchewan/test_hard-* - config_name: Yukon data_files: - split: train path: Yukon/train-* - split: validation path: Yukon/validation-* - split: test path: Yukon/test-* - split: test_hard path: Yukon/test_hard-* license: mit task_categories: - image-segmentation tags: - environment - wildfire size_categories: - 100K - πŸ’Ώ Dataset repository on [GitHub](https://github.com/eceo-epfl/CanadaFireSat-Data)
- πŸ€– Model repository on [GitHub](https://github.com/eceo-epfl/CanadaFireSat-Model) & Weights on [Hugging Face](TBC) ## πŸ“ Summary Representation The main use of this dataset is to push for the development of algorithms towards high-resolution wildfire forecasting via multi-modal learning. Indeed, we show the potential through our experiments of models trained on satellite image time series (Sentinel-2) and with environmental predictors (ERA5, MODIS, FWI). We hope to emulate the community to benchmark their EO and climate foundation models on CanadaFireSat to investigate their downstream fine-tuning capabilities on this complex extreme event forecasting task.

## Sources We describe below the different sources necessary to build the CanadaFireSat benchmark. ### πŸ”₯πŸ“ Fire Polygons Source - πŸ’» National Burned Area Composite (NBAC πŸ‡¨πŸ‡¦): Polygons Shapefile downloaded from [CWFIS Datamart](https://cwfis.cfs.nrcan.gc.ca/home)
- πŸ“… Filter fires since 2015 aligning with Sentinel-2 imagery availability
- πŸ›‘ No restrictions are applied on ignition source or other metadata
- βž• Spatial aggregation: Fires are mapped to a 2.8 km Γ— 2.8 km grid | Temporal aggregation into 8-day windows ### πŸ›°οΈπŸ—ΊοΈ Satellite Image Time Series Source - πŸ›°οΈ Sentinel-2 (S2) Level-1C Satellite Imagery (2015–2023) from [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED)
- πŸ—ΊοΈ For each grid cell (2.8β€―km Γ— 2.8β€―km): Collect cloud-free S2 images (≀ 40% cloud cover) over a 64-day period before prediction
- ⚠️ We discard samples with: Fewer than 3 valid images | Less than 40 days of coverage
### 🌦️🌲 Environmental Predictors - 🌑️ Hydrometeorological Drivers: Key variables like temperature, precipitation, soil moisture, and humidity from ERA5-Land (11 km, available on [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_DAILY_AGGR)) and MODIS11 (1 km, available on [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A1)), aggregated over 8-day windows using mean, max, and min values. - 🌿 Vegetation Indices ([MODIS13](https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD13A1) and [MODIS15](https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD15A2H)): NDVI, EVI, LAI, and FPAR (500 m) captured in 8 or 16-day composites, informing on vegetation state. - πŸ”₯ Fire Danger Metrics ([CEMS](https://ewds.climate.copernicus.eu/datasets/cems-fire-historical-v1?tab=overview) previously on CDS): Fire Weather Index and Drought Code from the Canadian FWI system (0.25Β° resolution). - πŸ•’ For each sample, we gather predictor data from 64 days prior to reflect pre-fire conditions. ### 🏞️ Land Cover - ⛔️ Exclusively used for adversarial sampling and post-training analysis. - πŸ’Ύ Data extracted is the 2020 North American Land Cover 30-meter dataset, produced as part of the North American Land Change Monitoring System (NALCMS) (available on [Google Earth Engine](https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2020_REL_NALCMS)) ## πŸ“· Outputs ### πŸ“Š CanadaFireSat Dataset Statistics (Without Test Hard): | **Statistic** | **Value** | |----------------------------------------|---------------------------| | Total Samples | 177,801 | | Target Spatial Resolution | 100 m | | Region Coverage | Canada | | Temporal Coverage | 2016 - 2023 | | Sample Area Size | 2.64 km Γ— 2.64 km | | Fire Occurrence Rate | 39% of samples | | Total Fire Patches | 16% of patches | | Training Set (2016–2021) | 78,030 samples | | Validation Set (2022) | 14,329 samples | | Test Set (2023) | 85,442 samples | | Sentinel-2 Temporal Median Coverage | 55 days (8 images) | | Number of Environmental Predictors | 58 | | Data Sources | ERA5, MODIS, CEMS | ### πŸ“ Samples Localisation:

Positive Samples Negative Samples

Figure 1: Spatial distribution of positive (left) and negative (right) wildfire samples.

### πŸ›°οΈ Example of S2 time series:

Figure 2: Row 1-3 Samples of Sentinel-2 input time series for 4 locations in Canada, with only the RGB bands with rescaled intensity. Row 4 Sentinel-2 images after the fire occurred. Row 5 Fire polygons used as labels with the Sentinel-2 images post-fire.

## Dataset Structure | Name | Type | Shape | Description | |----------------------|---------------------------|---------------------------------|-------------------------------------| | `date` | `timestamp[s]` | - | Fire Date | | `doy` | `sequence` | - | Sentinel-2 Tiles Day of the Year | | `10x` | `sequence` | (4, 264, 264) | Sentinel-2 10m bands | | `20x` | `sequence` | (6, 132, 132) | Sentinel-2 20m bands | | `60x` | `sequence` | (3, 44, 44) | Sentinel-2 60m bands | | `loc` | `array3_d` | (2, 264, 264) | Latitude and Longitude grid | | `labels` | `array2_d` | (264, 264) | Fire binary label mask | | `tab_cds` | `array2_d` | (8, 6) | Tabular CDS variables | | `tab_era5` | `array2_d` | (8, 45) | Tabular ERA5 variables | | `tab_modis` | `array2_d` | (8, 7) | Tabular MODIS products | | `env_cds` | `array4_d` | (8, 6, 13, 13) | Spatial CDS variables | | `env_cds_loc` | `array3_d` | (13, 13, 2) | Grid coordinates for CDS | | `env_era5` | `array4_d` | (8, 45, 32, 32) | Spatial ERA5 variables | | `env_era5_loc` | `array3_d` | (32, 32, 2) | Grid coordinates for ERA5 | | `env_modis11` | `array4_d` | (8, 3, 16, 16) | Spatial MODIS11 variables | | `env_modis11_loc` | `array3_d` | (16, 16, 2) | Grid coordinates for MODIS11 | | `env_modis13_15` | `array4_d` | (8, 4, 32, 32) | Spatial MODIS13/15 variables | | `env_modis13_15_loc`| `array3_d` | (32, 32, 2) | Grid coordinates for MODIS13/15) | | `env_doy` | `sequence` | - | Environment Variables Day of the Year| | `region` | `string` | - | Canadian Province or Territory | | `tile_id` | `int32` | - | Tile identifier | | `file_id` | `string` | - | Unique file identifier | | `fwi` | `float32` | - | Tile Fire Weather Index | ## Citation The paper is currently under review with a preprint available on ArXiv. ``` @article{porta2025canadafiresat, title={CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities}, author={Porta, Hugo and Dalsasso, Emanuele and McCarty, Jessica L and Tuia, Devis}, journal={arXiv preprint arXiv:2506.08690}, year={2025} } ``` ## Contacts & Information - **Curated by:** [Hugo Porta](https://scholar.google.com/citations?user=IQMApuoAAAAJ&hl=fr) - **Contact Email:** hugo.porta@epfl.ch - **Shared by:** [ECEO Lab](https://www.epfl.ch/labs/eceo/) - **License:** MiT License