Dataset Card for CanadaFireSat 🔥🛰️

In this benchmark, we investigate the potential of deep learning with multiple modalities for high-resolution wildfire forecasting. Leveraging different data settings across two types of model architectures: CNN-based and ViT-based.

• 📝 Paper on ArXiv
  
• 💿 Dataset repository on GitHub
  
• 🤖 Model repository on GitHub & Weights on Hugging Face

📝 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.

🔥📍 Fire Polygons Source

• 💻 National Burned Area Composite (NBAC 🇨🇦): Polygons Shapefile downloaded from CWFIS Datamart
  
• 📅 Filter fires since 2015 aligning with Sentinel-2 imagery availability
  
• 🛑 No restrictions are applied on ignition source or other metadata
  
• ➕ Spatial aggregation: Fires are mapped to a 2.8 km × 2.8 km grid | Temporal aggregation into 8-day windows

🛰️🗺️ Satellite Image Time Series Source

• 🛰️ Sentinel-2 (S2) Level-1C Satellite Imagery (2015–2023) from Google Earth Engine
  
• 🗺️ For each grid cell (2.8 km × 2.8 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) and MODIS11 (1 km, available on Google Earth Engine), aggregated over 8-day windows using mean, max, and min values.
• 🌿 Vegetation Indices (MODIS13 and MODIS15): NDVI, EVI, LAI, and FPAR (500 m) captured in 8 or 16-day composites, informing on vegetation state.
• 🔥 Fire Danger Metrics (CEMS previously on CDS): Fire Weather Index and Drought Code from the Canadian FWI system (0.25° resolution).
• 🕒 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.
• 💾 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)

📷 Outputs

📊 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 × 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

📍 Samples Localisation:

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

🛰️ 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<int64>	-	Sentinel-2 Tiles Day of the Year
10x	sequence<array3_d>	(4, 264, 264)	Sentinel-2 10m bands
20x	sequence<array3_d>	(6, 132, 132)	Sentinel-2 20m bands
60x	sequence<array3_d>	(3, 44, 44)	Sentinel-2 60m bands
loc	array3_d<float32>	(2, 264, 264)	Latitude and Longitude grid
labels	array2_d<uint8>	(264, 264)	Fire binary label mask
tab_cds	array2_d<float32>	(8, 6)	Tabular CDS variables
tab_era5	array2_d<float32>	(8, 45)	Tabular ERA5 variables
tab_modis	array2_d<float32>	(8, 7)	Tabular MODIS products
env_cds	array4_d<float32>	(8, 6, 13, 13)	Spatial CDS variables
env_cds_loc	array3_d<float32>	(13, 13, 2)	Grid coordinates for CDS
env_era5	array4_d<float32>	(8, 45, 32, 32)	Spatial ERA5 variables
env_era5_loc	array3_d<float32>	(32, 32, 2)	Grid coordinates for ERA5
env_modis11	array4_d<float32>	(8, 3, 16, 16)	Spatial MODIS11 variables
env_modis11_loc	array3_d<float32>	(16, 16, 2)	Grid coordinates for MODIS11
env_modis13_15	array4_d<float32>	(8, 4, 32, 32)	Spatial MODIS13/15 variables
env_modis13_15_loc	array3_d<float32>	(32, 32, 2)	Grid coordinates for MODIS13/15)
env_doy	sequence<int64>	-	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
• Contact Email: [email protected]
• Shared by: ECEO Lab
• License: MiT License