metadata

dataset_info:
  features:
    - name: imageA
      dtype: image
    - name: imageB
      dtype: image
    - name: label
      dtype: image
  splits:
    - name: train
      num_bytes: 26938135
      num_examples: 827
    - name: test
      num_bytes: 12564707
      num_examples: 385
  download_size: 39371405
  dataset_size: 39502842
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*

Onera Satellite Change Detection Dataset - RGB - Cropped to 96x96 patches

The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.

It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. Locations are picked all over the world, in Brazil, USA, Europe, Middle-East and Asia. For each location, registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites are provided. Images vary in spatial resolution between 10m, 20m and 60m.

Pixel-level change ground truth is provided for all 14 training and 10 test image pairs. The annotated changes focus on urban changes, such as new buildings or new roads. These data can be used for training and setting parameters of change detection algorithms.

Dataset Details

Dataset Description

The images were cropped to 96x96 patches to enable direct usage with the standard protocol.

License: CC BY-NC-SA

Dataset Sources

Homepage: https://rcdaudt.github.io/oscd/
Paper: ArXiv

Uses

from datasets import load_dataset

oscd96 = load_dataset("blaz-r/OSCD_RGB_Cropped_96")

>>> oscd96["train"]
# single "sample" with 3 features: "imageA", "imageB", "label"

Recommended protocol

If you use this dataset, we encourage you to also consider following the evaluation protocol disscused in paper:

Be the Change You Want to See: Revisiting Remote Sensing Change Detection Practices Paper (arXiv)
Paper (TGRS)

This protocol (Sec III-B) standardizes evaluation across change detection benchmarks and helps ensure fair and reproducible comparisons.

Citation

If you use this work for your projects, please take the time to cite the original paper:

@inproceedings{daudt2018urban,
  title={Urban change detection for multispectral earth observation using convolutional neural networks},
  author={Daudt, Rodrigo Caye and Le Saux, Bertr and Boulch, Alexandre and Gousseau, Yann},
  booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
  pages={2115--2118},
  year={2018},
  organization={IEEE}
}