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)
  
• 💿 Dataset repository on GitHub
  
• 🤖 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.

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)

📊 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 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:

Folder	Zip Name	File Name	Config Path (global_config.yaml)	Description
ENV	pos_spatial.*	[TILE ID]/[source].npy, [TILE ID]/[source]_locs.npy	pos_env_spa	Spatial Environmental Predictors for Positive Samples (Contains mean and std JSON files)
ENV	neg_spatial.*	[TILE ID]/[source].npy, [TILE ID]/[source]_locs.npy	neg_env_spa	Spatial Environmental Predictors for Negative Samples
ENV	neg_spatial_hard.*	[TILE ID]/[source].npy, [TILE ID]/[source]_locs.npy	neg_env_spa_hard	Spatial Environmental Predictors for Negative Samples (Test Hard)
ENV	pos_tabular.*	[TILE ID]/[source].csv	pos_env_tab	Tabular Environmental Predictors for Positive Samples (Contains mean and std JSON files)
ENV	neg_tabular.*	[TILE ID]/[source].csv	neg_env_tab	Tabular Environmental Predictors for Negative Samples
ENV	neg_tabular_hard.*	[TILE ID]/[source].csv	neg_env_tab_hard	Tabular Environmental Predictors for Negative Samples (Test Hard)
S2_POS	[REGION].zip	[TILE ID]/images_ts_[scale]_one.npy, [TILE ID]/doy_ts_one.npy, [TILE ID]/loc_one.npy,	pos_sits	Satellite Image Time Series for Positive Samples
S2_NEG	[REGION].zip	[TILE ID]/images_ts_[scale]_one.npy, [TILE ID]/doy_ts_one.npy, [TILE ID]/loc_one.npy,	neg_sits	Satellite Image Time Series for Negative Samples
S2_NEG_HARD	[REGION].zip	[TILE ID]/images_ts_[scale]_one.npy, [TILE ID]/doy_ts_one.npy, [TILE ID]/loc_one.npy,	neg_sits_hard	Satellite Image Time Series for Negative Samples (Test Hard)
LABELS	labels.zip	[TILE ID]/label.npy	label	Binary Labels Maps
metadata	*.json	-	-	Metadata Files for Model Trainig

Dataset Structure

Name	Type	Shape (T: Sample Temporal Dimension)	Description
doy_ts_one.npy	numpy.NDArray<int64>	(T)	Sentinel-2 Tiles Day of the Year
images_ts_10_one.npy	numpy.NDArray<uint8>	(T, 4, 264, 264)	Sentinel-2 10m bands, Order: ["B4", "B3", "B2", "B8"]
images_ts_20_one.npy	numpy.NDArray<uint8>	(T, 6, 132, 132)	Sentinel-2 20m bands, Order: ["B5", "B6", "B7", "B8A", "B11", "B12"]
images_ts_60_one.npy	numpy.NDArray<uint8>	(T, 3, 44, 44)	Sentinel-2 60m bands, Order: ["B1", "B9", "B10"]
loc_one.npy	numpy.NDArray<int64>	(2, 264, 264)	Latitude and Longitude grid
label.npy	numpy.NDArray<uint8>	(264, 264)	Fire binary label mask
cds.csv	CSV	(8, 6)	Tabular CDS variables
era5.csv	CSV	(8, 45)	Tabular ERA5 variables
modis.csv	CSV	(8, 7)	Tabular MODIS products
cds.npy	numpy.NDArray<float32>	(8, 6, 13, 13)	Spatial CDS variables
cds_loc.npy	numpy.NDArray<float32>	(13, 13, 2)	Grid coordinates for CDS
era5.npy	numpy.NDArray<float32>	(8, 45, 32, 32)	Spatial ERA5 variables
era5_loc.npy	numpy.NDArray<float32>	(32, 32, 2)	Grid coordinates for ERA5
modis11.npy	numpy.NDArray<float32>	(8, 3, 16, 16)	Spatial MODIS11 variables
modis11_loc.npy	numpy.NDArray<float32>	(16, 16, 2)	Grid coordinates for MODIS11
modis13_15.npy	numpy.NDArray<float32>	(8, 4, 32, 32)	Spatial MODIS13/15 variables
modis13_15_loc.npy	numpy.NDArray<float32>	(32, 32, 2)	Grid coordinates for MODIS13/15)
dates.npy	numpy.NDArray<int64>	(8)	Environment Variables Day of the Year

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

Encoder	Modality	Params (M)	Val PRAUC	Val F1	Test PRAUC	Test F1	Test Hard PRAUC	Test Hard F1	Avg PRAUC	Avg F1
🔷 ResNet-50
	SITS Only	52.2	45.2	49.3	53.3	58.9	26.3	36.7	41.6	48.3
	ENV Only	97.5	41.6	46.7	49.9	53.5	24.5	33.1	38.7	44.4
	Multi-Modal	52.2	46.1	51.1	57.0	60.3	27.1	37.4	43.4	49.6
🔶 ViT_S
	SITS Only	36.5	45.2	50.6	51.2	51.9	25.7	33.8	40.7	45.2
	ENV Only	54.8	34.8	45.7	49.2	59.9	21.2	35.1	35.1	46.9
	Multi-Modal	37.7	43.9	50.0	56.3	59.2	25.1	36.6	41.8	48.6
⚫ Baselines
Baseline (FWI)	ENV Only	-	20.0	32.7	43.1	50.3	21.1	32.7	28.1	38.6
Baseline (UNet) ¹	ENV Only	9.1	33.6	43.2	51.4	58.4	25.1	34.2	36.7	45.3
Baseline (UTAE) ²	ENV Only	1.1	32.9	43.8	47.2	52.5	22.0	31.7	34.0	42.7
Baseline (ConvLSTM) ³	SITS Only	1.2	41.4	46.0	50.2	58.9	23.1	35.0	38.2	46.6

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