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---
language: en
license: unknown
task_categories:
- change-detection
pretty_name: ChaBuD MSI
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- change-detection
- sentinel-2
dataset_info:
features:
- name: image1
dtype:
array3_d:
dtype: uint8
shape:
- 512
- 512
- 13
- name: image2
dtype:
array3_d:
dtype: uint8
shape:
- 512
- 512
- 13
- name: mask
dtype: image
splits:
- name: train
num_bytes: 2624716428.0
num_examples: 278
- name: validation
num_bytes: 736431228.0
num_examples: 78
download_size: 2232652835
dataset_size: 3361147656.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# ChaBuD MSI
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
ChaBuD is a dataset for Change detection for Burned area Delineation and is used for the ChaBuD ECML-PKDD 2023 Discovery Challenge. This is the MSI version with 13 bands.
- **Paper:** https://doi.org/10.1016/j.rse.2021.112603
- **Homepage:** https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023
## Description
<!-- Provide a longer summary of what this dataset is. -->
- **Total Number of Images**: 356
- **Bands**: 13 (MSI)
- **Image Size**: 512x512
- **Image Resolution**: 10m
- **Land Cover Classes**: 2
- **Classes**: no change, burned area
- **Source**: Sentinel-2
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/ChaBuD_MSI")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
ChaBuD_MSI = load_dataset("blanchon/ChaBuD_MSI")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the ChaBuD_MSI dataset in your research, please consider citing the following publication:
```bibtex
@article{TURKOGLU2021112603,
title = {Crop mapping from image time series: Deep learning with multi-scale label hierarchies},
journal = {Remote Sensing of Environment},
volume = {264},
pages = {112603},
year = {2021},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2021.112603},
url = {https://www.sciencedirect.com/science/article/pii/S0034425721003230},
author = {Mehmet Ozgur Turkoglu and Stefano D'Aronco and Gregor Perich and Frank Liebisch and Constantin Streit and Konrad Schindler and Jan Dirk Wegner},
keywords = {Deep learning, Recurrent neural network (RNN), Convolutional RNN, Hierarchical classification, Multi-stage, Crop classification, Multi-temporal, Time series},
}
```
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