Datasets:
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values | band_metadata stringclasses 2
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classes |
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End of preview. Expand in Data Studio
Simta Rice Mapping Classification
This dataset provides real SAR satellite imagery of rice fields collected across the Arkansas River Basin, Sacramento Valley, and Suihua regions using Sentinel-1 between 2017 and 2019. It captures multi-temporal rice cultivation patterns in agricultural field environments across the United States and China, offering valuable data for computer vision research in crop monitoring and mapping. The dataset contains 3,024 images across 4 classes: 2019Canifornia, EImage, ThImage, TImage.
Images per class:
- 2019Canifornia: 375
- EImage: 706
- ThImage: 884
- TImage: 1,059
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{ren2026simta,
title={SimTA: A Dual-Polarization SAR Time-Series Rice Field Mapping Model Based on Deep Feature-Level Fusion and Spatiotemporal Attention},
author={Ren, Dong and Liang, Jiaxuan and Liu, Li and Wei, Pengliang and Yang, Lingbo and Wang, Lu and Sun, Hang and Zhang, Kehan and Qiu, Bingwen and Liu, Weiwei and Huang, Jingfeng},
journal={Remote Sensing},
volume={18},
pages={1237},
year={2026},
publisher={MDPI}
}
The dataset itself can be cited as:
Liu, L., Deng, X., & Quan, T. (2025). Rice Mapping Based on SAR Imagery – Arkansas River Basin, Sacramento Region [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17997718
This dataset was reformatted from its original format to match HuggingFace standards.
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