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Land use classification of low resolution spatial imagery is one of the mostextensively researched fields in remote sensing. Despite significantadvancements in satellite technology, high resolution imagery lacks globalcoverage and can be prohibitively expensive to procure for extended timeperiods. Accurately classifying land use change without high resolution imageryoffers the potential to monitor vital aspects of global development agendaincluding climate smart agriculture, drought resistant crops, and sustainableland management. Utilizing a combination of capsule layers and long-short termmemory layers with distributed attention, the present paper achievesstate-of-the-art accuracy on temporal crop type classification at a 30x30mresolution with Sentinel 2 imagery.

Spatio-temporal crop classification of low-resolution satellite imagery with capsule layers and distributed attention