Update README.md

README.md

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   - split: test_hard
     path: Yukon/test_hard-*
 ---
+
+# 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](TBC) <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.
+
+### 🔥📍 Fire Polygons Source
+
+- 💻 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:
+
+| **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">
+  <img src="images/pos_samples.png" alt="Positive Samples" width="45%"/>
+  <img src="images/neg_samples.png" alt="Negative Samples" width="45%"/>
+</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.
+
+```
+TBC
+```
+
+## 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