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--- |
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language: en |
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license: unknown |
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task_categories: |
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- change-detection |
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pretty_name: ChaBuD |
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tags: |
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- remote-sensing |
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- earth-observation |
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- geospatial |
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- satellite-imagery |
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- change-detection |
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- sentinel-2 |
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dataset_info: |
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features: |
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- name: image1 |
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dtype: image |
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- name: image2 |
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dtype: image |
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- name: mask |
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dtype: image |
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splits: |
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- name: train |
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num_bytes: 577995423.0 |
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num_examples: 278 |
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- name: validation |
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num_bytes: 158102432.0 |
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num_examples: 78 |
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download_size: 380547073 |
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dataset_size: 736097855.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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--- |
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# ChaBuD |
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<!-- Dataset thumbnail --> |
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![ChaBuD](./thumbnail.png) |
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<!-- Provide a quick summary of the dataset. --> |
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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. |
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- **Paper:** https://doi.org/10.1016/j.rse.2021.112603 |
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- **Homepage:** https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023 |
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## Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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- **Total Number of Images**: 356 |
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- **Bands**: 3 (RGB) |
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- **Image Size**: 512x512 |
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- **Image Resolution**: 10m |
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- **Land Cover Classes**: 2 |
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- **Classes**: no change, burned area |
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- **Source**: Sentinel-2 |
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## Usage |
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To use this dataset, simply use `datasets.load_dataset("blanchon/ChaBuD")`. |
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<!-- Provide any additional information on how to use this dataset. --> |
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```python |
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from datasets import load_dataset |
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ChaBuD = load_dataset("blanchon/ChaBuD") |
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``` |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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If you use the ChaBuD dataset in your research, please consider citing the following publication: |
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```bibtex |
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@article{TURKOGLU2021112603, |
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title = {Crop mapping from image time series: Deep learning with multi-scale label hierarchies}, |
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journal = {Remote Sensing of Environment}, |
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volume = {264}, |
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pages = {112603}, |
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year = {2021}, |
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issn = {0034-4257}, |
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doi = {https://doi.org/10.1016/j.rse.2021.112603}, |
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url = {https://www.sciencedirect.com/science/article/pii/S0034425721003230}, |
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author = {Mehmet Ozgur Turkoglu and Stefano D'Aronco and Gregor Perich and Frank Liebisch and Constantin Streit and Konrad Schindler and Jan Dirk Wegner}, |
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keywords = {Deep learning, Recurrent neural network (RNN), Convolutional RNN, Hierarchical classification, Multi-stage, Crop classification, Multi-temporal, Time series}, |
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} |
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``` |
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