---
license: cc-by-4.0
task_categories:
- image-segmentation
- image-classification
language:
- en
tags:
- semantic segmentation
- remote sensing
- sentinel
- wildfire
pretty_name: Wildfires - CEMS
size_categories:
- 1K<n<10K
---
# 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.

---------
- **Repository:** https://github.com/links-ads/burned-area-seg
- **Paper:** https://paperswithcode.com/paper/robust-burned-area-delineation-through
---------
![Dataset sample](assets/sample.png)

## 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:

```console
$ 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](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/](https://creativecommons.org/licenses/by/4.0/)

### Citation Information

```bibtex
@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)