Corpus ID: 236447582

CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels

@article{Ghosh2021CalCROP21AG,
  title={CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels},
  author={Rahul Ghosh and Praveen Ravirathinam and Xiaowei Jia and Ankush Khandelwal and David J. Mulla and Vipin Kumar},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12499}
}
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security. A dataset like ImageNet that revolutionized computer vision applications can accelerate development of novel crop mapping techniques. Currently, the United States Department of Agriculture (USDA) annually releases the Cropland Data Layer (CDL) which contains crop labels at 30m resolution for the entire United States of America. While CDL is state of the art and isโ€ฆย Expand
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
TLDR
A novel architecture is introduced that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data and to better identify the unique temporal patterns of each land cover. Expand

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