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|
- 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 |
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|
path: Alberta/validation-* |
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- split: test |
|
|
path: Alberta/test-* |
|
|
- split: test_hard |
|
|
path: Alberta/test_hard-* |
|
|
- config_name: British Columbia |
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|
data_files: |
|
|
- split: train |
|
|
path: British Columbia/train-* |
|
|
- split: validation |
|
|
path: British Columbia/validation-* |
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|
- split: test |
|
|
path: British Columbia/test-* |
|
|
- split: test_hard |
|
|
path: British Columbia/test_hard-* |
|
|
- config_name: Manitoba |
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|
data_files: |
|
|
- split: train |
|
|
path: Manitoba/train-* |
|
|
- split: validation |
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|
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 |
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|
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 |
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|
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<n<1M |
|
|
--- |
|
|
|
|
|
# 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](https://arxiv.org/abs/2506.08690) <br> |
|
|
- 💿 Dataset repository on [GitHub](https://github.com/eceo-epfl/CanadaFireSat-Data) <br> |
|
|
- 🤖 Model repository on [GitHub](https://github.com/eceo-epfl/CanadaFireSat-Model) & Weights on [Hugging Face](TBC) |
|
|
|
|
|
## 📝 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. |
|
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|
|
|
### 🔥📍 Fire Polygons Source |
|
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|
|
|
- 💻 National Burned Area Composite (NBAC 🇨🇦): Polygons Shapefile downloaded from [CWFIS Datamart](https://cwfis.cfs.nrcan.gc.ca/home) <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](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_HARMONIZED) <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](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. |
|
|
- 🔥 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 (0.25° 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](https://developers.google.com/earth-engine/datasets/catalog/USGS_NLCD_RELEASES_2020_REL_NALCMS)) |
|
|
|
|
|
## 📷 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: |
|
|
|
|
|
<p align="center"> |
|
|
<div style="display: flex; justify-content: center; gap: 10px;"> |
|
|
<img src="images/pos_samples.png" alt="Positive Samples" width="45%"/> |
|
|
<img src="images/neg_samples.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 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](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 |
