EPFL-ECEO/CanadaFireSat-Raw

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.

• —📝 Published paper from ISPRS (ArXiv Version)<br>
• —💿 Dataset repository on GitHub <br>
• —🤖 Model repository on GitHub & Weights on Hugging Face
• —🟰 Another "Clean" version of the data with PARQUET files can be found at CanadaFireSat

Disclaimer: [23/06/2026] CanadaFireSat does not contain yet the ignition proxy predictors used in Appendix E.

📝 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.

<p align="center"> <img src="images/summary-canadafiresat.png"/> </p>

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 <br>
• —📅 Filter fires since 2015 aligning with Sentinel-2 imagery availability <br>
• —🛑 No restrictions are applied on ignition source or other metadata <br>
• —➕ 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 <br>
• —🗺️ 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 <br>
• —⚠️ We discard samples with: Fewer than 3 valid images | Less than 40 days of coverage <br>

🌦️🌲 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)

📊 CanadaFireSat Dataset Statistics (Without Test Hard):

📍 Samples Localisation:

<p align="center"> <div style="display: flex; justify-content: center; gap: 10px;"> <img src="images/possamples.png" alt="Positive Samples" width="45%"/> <img src="images/negsamples.png" alt="Negative Samples" width="45%"/> </div> </p>

<p align="center"> <b>Figure 1:</b> Spatial distribution of positive (left) and negative (right) wildfire samples. </p>

🛰️ Example of S2 time series:

<p align="center"> <img src="images/s2_tiles.png"/> </p>

<p align="center"> <b>Figure 2:</b> 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. </p>

Dataset Extraction

This dataset is organized into 6 folders containing compressed raw data for CanadaFireSat. You can directly access the one necessary for your model setting of interest and adapt the config paths of your models:

Dataset Structure

📊 Performance Analysis: In this table, we describe the models' performances across data settings and architectures.

¹ Prapas et al., 2023 ² Michail et al., 2025 ³ Yang et al., 2021

Citation

The paper has been published in the ISPRS Journal of Photogrammetry and Remote Sensing.

@article{porta2026canadafiresat,
  title={CanadaFireSat: Towards high-resolution wildfire forecasting with multiple modalities},
  author={Porta, Hugo and Dalsasso, Emanuele and McCarty, Jessica L and Tuia, Devis},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={239},
  pages={555--572},
  year={2026},
  publisher={Elsevier}
}

Contacts & Information

• —Curated by: Hugo Porta
• —Contact Email: hugo.porta@epfl.ch
• —Shared by: ECEO Lab
• —License: MiT License