links-ads/wildfires-cems

Wildfires - CEMS

The dataset includes annotations for burned area delineation and land cover segmentation, with a focus on European soil. The dataset is curated from various sources, including the Copernicus European Monitoring System (EMS) and Sentinel-2 feeds.

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• Repository: https://github.com/links-ads/burned-area-seg
• Paper: https://paperswithcode.com/paper/robust-burned-area-delineation-through

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Dataset Preparation

The dataset has been compressed into segmentented tarballs for ease of use within Git LFS (that is, tar > gzip > split). To revert the process into files and directories follow these steps:

$ git clone https://huggingface.co/datasets/links-ads/wildfires-cems
$ cd wildfires-ems
# revert the multipart compression: merge first, then untar
$ cat data/train/train.tar.* | tar -xzvf - -i
$ cat data/test/test.tar.* | tar -xzvf - -i
$ cat data/val/val.tar.* | tar -xzvf - -i

It is very likely that the extracted files will retain the internal directory structure, making the train/val/test directories useless. Adapt the output structure as you see fit, the original structure is shown below.

Dataset Structure

The main dataset used in the paper comprises the following inputs:

Suffix	Data Type	Description	Format
S2L2A	Sentinel-2 Image	L2A data with 12 channels in reflectance/10k format	GeoTIFF (.tif)
DEL	Delineation Map	Binary map indicating burned areas as uint8 values (0 or 1)	GeoTIFF (.tif)
GRA	Grading Map	Grading information (if available) with uint8 values ranging from 0 to 4	GeoTIFF (.tif)
ESA_LC	Land Cover Map	ESA WorldCover 2020 land cover classes as uint8 values	GeoTIFF (.tif)
CM	Cloud Cover Map	Cloud cover mask, uint8 values generated using CloudSen12 (0 or 1)	GeoTIFF (.tif)

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. The dataset already provides a train / val / test split for convenience, however the inner structure of each group is the same. The folders are structured as follows:

train/val/test/
├── EMSR230/
│   ├── AOI01/
│   │   ├── EMSR230_AOI01_01/
│   │   │   ├── EMSR230_AOI01_01_CM.png
│   │   │   ├── EMSR230_AOI01_01_CM.tif
│   │   │   ├── EMSR230_AOI01_01_DEL.png
│   │   │   ├── EMSR230_AOI01_01_DEL.tif
│   │   │   ├── EMSR230_AOI01_01_ESA_LC.png
│   │   │   ├── EMSR230_AOI01_01_ESA_LC.tif
│   │   │   ├── EMSR230_AOI01_01_GRA.png
│   │   │   ├── EMSR230_AOI01_01_GRA.tif
│   │   │   ├── EMSR230_AOI01_01_S2L2A.json -> metadata information
│   │   │   ├── EMSR230_AOI01_01_S2L2A.png -> RGB visualization
│   │   │   └── EMSR230_AOI01_01_S2L2A.tif
│   │   │   └── ...
│   │   ├── EMSR230_AOI01_02/
│   │   │   └── ...
│   │   ├── ...
│   ├── AOI02/
│   │   └── ...
│   ├── ...
├── EMSR231/
│   ├── ...
├── ...

Source Data

• Activations are directly derived from Copernicus EMS (CEMS): https://emergency.copernicus.eu/mapping/list-of-activations-rapid
• Sentinel-2 and LC images are downloaded from Microsoft Planetary Computer, using the AoI provided by CEMS.
• DEL and GRA maps represent the rasterized version of the delineation/grading products provided by the Copernicus service.

Licensing Information

CC-BY-4.0 https://creativecommons.org/licenses/by/4.0/

Citation Information

@inproceedings{arnaudo2023burned,
  title={Robust Burned Area Delineation through Multitask Learning},
  author={Arnaudo, Edoardo and Barco, Luca and Merlo, Matteo and Rossi, Claudio},
  booktitle={Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year={2023}
}

Contributions

• Luca Barco (luca.barco@linksfoundation.com)
• Edoardo Arnaudo (edoardo.arnaudo@polito.it | linksfoundation.com)