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Datasets:
links-ads
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wildfires-cems

Tasks:
Image Segmentation
Image Classification
Languages:
English
Size:
1K<n<10K
Tags:
semantic segmentation
remote sensing
sentinel
wildfire
DOI:
License:
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metadata
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.

Dataset sample

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 -xvf - -i
$ cat data/test/test.tar.* | tar -xvf - -i
$ cat data/val/val.tar.* | tar -xvf - -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