metadata

language: en
license: unknown
task_categories:
  - change-detection
pretty_name: ChaBuD
tags:
  - remote-sensing
  - earth-observation
  - geospatial
  - satellite-imagery
  - change-detection
  - sentinel-2
dataset_info:
  features:
    - name: image1
      dtype: image
    - name: image2
      dtype: image
    - name: mask
      dtype: image
  splits:
    - name: train
      num_bytes: 577995423
      num_examples: 278
    - name: validation
      num_bytes: 158102432
      num_examples: 78
  download_size: 380547073
  dataset_size: 736097855
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*

ChaBuD

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 RGB version with 3 bands.

Paper: https://doi.org/10.1016/j.rse.2021.112603
Homepage: https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023

Description

Total Number of Images: 356
Bands: 3 (RGB)
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").

from datasets import load_dataset
ChaBuD = load_dataset("blanchon/ChaBuD")

Citation

If you use the ChaBuD dataset in your research, please consider citing the following publication:

@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},
}