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--- |
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license: other |
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license_name: cc-by-4.0 |
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license_link: https://creativecommons.org/licenses/by/4.0/deed.en |
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size_categories: |
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- 100K<n<1M |
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--- |
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Rehosted dataset from [official repo](https://huggingface.co/datasets/nascetti-a/BioMassters). However, we have created tar balls instead of split zip archives, since they are easier to extract. |
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We also changed the nested directory structure of the `train_features` directory and renamed the .csv files. |
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The tar balls can be extracted with theses commands: |
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```cli |
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tar -xvf train_agbm.tar.gz |
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tar -xvf test_agbm.tar.gz |
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cat train_features.tar.gz* | tar xvfz - |
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cat test_features.tar.gz* | tar xvfz - |
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``` |
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You can also check the accompanying dataloader in [torchgeo](https://torchgeo.readthedocs.io/en/latest/api/datasets.html#biomassters). |
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If you use this dataset, please cite: |
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``` |
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@inproceedings{nascetti2023biomassters, |
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title={BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series}, |
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author={Nascetti, Andrea and Yadav, Ritu and Brodt, Kirill and Qu, Qixun and Fan, Hongwei and Shendryk, Yuri and Shah, Isha and Chung, Christine}, |
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booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, |
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year={2023} |
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} |
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``` |
