File size: 4,936 Bytes
ed9ccc0 bb78965 ed9ccc0 bb78965 7896878 bb78965 7896878 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
---
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:** Coming soon
---------
## 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 -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](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)
|