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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 MSI
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
- 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").
from datasets import load_dataset
ChaBuD_MSI = load_dataset("blanchon/ChaBuD_MSI")
Citation
If you use the ChaBuD_MSI 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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