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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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tags: |
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- climate |
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pretty_name: BioMassters |
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size_categories: |
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- 100K<n<1M |
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--- |
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# BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series https://nascetti-a.github.io/BioMasster/ |
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The objective of this repository is to provide a deep learning ready dataset to predict yearly Above Ground Biomass (AGB) for Finnish forests using multi-temporal satellite imagery from |
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the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions, designed to collect a rich array of Earth observation data |
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### Reference data: |
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* pixel-wise above-ground biomass maps |
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* Measurements were collected using LiDAR calibrated with in-situ measurements. |
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* Total 13000 patches, each patch covering 2,560 X 2,560 meter area. |
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### Feature data: |
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* Sentinel-1 SAR (band order: ASC VV, ASC VH, DSC VV, DSC VH) |
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* Sentinel-2 MSI (band order: B2, B3, B4, B5, B6, B7, B8, B8A, B11, B12, CLP) |
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* 12 months of data (1 image per month) |
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* Total 310,000 patches |
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### Data Specifications: |
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 |
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### Data Size: |
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``` |
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dataset | # files | size |
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-------------------------------------- |
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train_features | 189078 | 215.9GB |
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test_features | 63348 | 73.0GB |
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train_agbm | 8689 | 2.1GB |
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``` |
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## Citation: |
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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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``` |
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