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--- |
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license: cc-by-4.0 |
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task_categories: |
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- image-segmentation |
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- image-classification |
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language: |
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- en |
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tags: |
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- semantic segmentation |
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- remote sensing |
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- sentinel |
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- wildfire |
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pretty_name: Wildfires - CEMS |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Wildfires - CEMS |
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The dataset includes annotations for burned area delineation and land cover segmentation, with a focus on European soil. |
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The dataset is curated from various sources, including the Copernicus European Monitoring System (EMS) and Sentinel-2 feeds. |
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--------- |
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- **Repository:** https://github.com/links-ads/burned-area-seg |
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- **Paper:** Coming soon |
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--------- |
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## Dataset Preparation |
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The dataset has been compressed into segmentented tarballs for ease of use within Git LFS (that is, tar > gzip > split). |
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To revert the process into files and directories follow these steps: |
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```console |
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$ git clone https://huggingface.co/datasets/links-ads/wildfires-cems |
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$ cd wildfires-ems |
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# revert the multipart compression: merge first, then untar |
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$ cat data/train/train.tar.* | tar -xvf - -i |
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$ cat data/test/test.tar.* | tar -xvf - -i |
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$ cat data/val/val.tar.* | tar -xvf - -i |
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``` |
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It is very likely that the extracted files will retain the internal directory structure, making the `train/val/test` directories useless. |
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Adapt the output structure as you see fit, the original structure is shown below. |
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## Dataset Structure |
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The main dataset used in the paper comprises the following inputs: |
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| Suffix | Data Type | Description | Format | |
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|---------|--------------------|-------------------------------------------------------------------------------------------|--------------------------| |
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| S2L2A | Sentinel-2 Image | L2A data with 12 channels in reflectance/10k format | GeoTIFF (.tif) | |
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| DEL | Delineation Map | Binary map indicating burned areas as uint8 values (0 or 1) | GeoTIFF (.tif) | |
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| GRA | Grading Map | Grading information (if available) with uint8 values ranging from 0 to 4 | GeoTIFF (.tif) | |
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| ESA_LC | Land Cover Map | ESA WorldCover 2020 land cover classes as uint8 values | GeoTIFF (.tif) | |
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| CM | Cloud Cover Map | Cloud cover mask, uint8 values generated using CloudSen12 (0 or 1) | GeoTIFF (.tif) | |
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Additionally, the dataset also contains two land cover variants, the ESRI Annual Land Cover (9 categories) and the static variant (10 categories), not used in this study. |
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The dataset already provides a `train` / `val` / `test` split for convenience, however the inner structure of each group is the same. |
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The folders are structured as follows: |
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``` |
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train/val/test/ |
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├── EMSR230/ |
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│ ├── AOI01/ |
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│ │ ├── EMSR230_AOI01_01/ |
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│ │ │ ├── EMSR230_AOI01_01_CM.png |
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│ │ │ ├── EMSR230_AOI01_01_CM.tif |
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│ │ │ ├── EMSR230_AOI01_01_DEL.png |
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│ │ │ ├── EMSR230_AOI01_01_DEL.tif |
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│ │ │ ├── EMSR230_AOI01_01_ESA_LC.png |
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│ │ │ ├── EMSR230_AOI01_01_ESA_LC.tif |
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│ │ │ ├── EMSR230_AOI01_01_GRA.png |
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│ │ │ ├── EMSR230_AOI01_01_GRA.tif |
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│ │ │ ├── EMSR230_AOI01_01_S2L2A.json -> metadata information |
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│ │ │ ├── EMSR230_AOI01_01_S2L2A.png -> RGB visualization |
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│ │ │ └── EMSR230_AOI01_01_S2L2A.tif |
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│ │ │ └── ... |
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│ │ ├── EMSR230_AOI01_02/ |
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│ │ │ └── ... |
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│ │ ├── ... |
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│ ├── AOI02/ |
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│ │ └── ... |
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│ ├── ... |
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├── EMSR231/ |
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│ ├── ... |
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├── ... |
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``` |
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### Source Data |
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- Activations are directly derived from Copernicus EMS (CEMS): [https://emergency.copernicus.eu/mapping/list-of-activations-rapid](https://emergency.copernicus.eu/mapping/list-of-activations-rapid) |
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- Sentinel-2 and LC images are downloaded from Microsoft Planetary Computer, using the AoI provided by CEMS. |
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- DEL and GRA maps represent the rasterized version of the delineation/grading products provided by the Copernicus service. |
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### Licensing Information |
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CC-BY-4.0 [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/) |
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### Citation Information |
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```bibtex |
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@inproceedings{arnaudo2023burned, |
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title={Robust Burned Area Delineation through Multitask Learning}, |
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author={Arnaudo, Edoardo and Barco, Luca and Merlo, Matteo and Rossi, Claudio}, |
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booktitle={Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases}, |
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year={2023} |
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} |
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``` |
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### Contributions |
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- Luca Barco (luca.barco@linksfoundation.com) |
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- Edoardo Arnaudo (edoardo.arnaudo@polito.it | linksfoundation.com) |
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