Mapping and monitoring crops is a key step to-wards 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 is widely used for a number of agricultural applications, it has a number of limitations (e.g., pixelated errors, labels carried over from previous years and errors in classification of minor crops). In this work, we create a new semantic segmentation benchmark dataset, which we call CalCROP21, for the diverse crops in the Central Valley region of California at 10m spatial resolution using a Google Earth Engine based robust image processing pipeline and a novel attention based spatio-temporal semantic segmentation algorithm STATT. STATT uses re-sampled (interpolated) CDL labels for training, but is able to generate a better prediction than CDL by leveraging spatial and temporal patterns in Sentinel2 multi-spectral image series to effectively capture phenologic differences amongst crops and uses attention to reduce the impact of clouds and other atmospheric disturbances. We also present a comprehensive evaluation to show that STATT has significantly better results when compared to the resampled CDL labels. We have released the dataset and the processing pipeline code for generating the benchmark dataset.
|Original language||English (US)|
|Title of host publication||Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021|
|Editors||Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - 2021|
|Event||2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States|
Duration: Dec 15 2021 → Dec 18 2021
|Name||Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021|
|Conference||2021 IEEE International Conference on Big Data, Big Data 2021|
|Period||12/15/21 → 12/18/21|
Bibliographical noteFunding Information:
This work was funded by the NSF awards 1838159 and 1739191. Rahul Ghosh is supported by UMII MNDrive Graduate Fellowship. Access to computing facilities was provided by the Minnesota Supercomputing Institute.
© 2021 IEEE.
- Large Scale dataset
- Remote Sensing
- Semantic Segmentation
- Spatio-temporal data