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blanchon
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ChaBuD

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Tags:
remote-sensing
earth-observation
geospatial
satellite-imagery
change-detection
sentinel-2
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YAML Metadata Warning: The task_categories "change-detection" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

ChaBuD

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.

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},
}
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