Pre season crop type mapping using deep neural networks

Reliable crop type maps are needed early in the growing season to retrieve crop type information from satellite imagery in order to produce in-season crop condition and yield outlooks. However, the inability to have early crop maps due to limited earth observations available during the beginning of the season is a major challenge for developing reliable satellite-based early warning systems.This paper introduces a novel crop type prediction modeling system based on deep Neural Networks (NN) to produce preseason crop type maps at the field scale resolution using historical crop maps. The architecture of this modeling system comprises of two end-to-end NN based modules that form an autoencoder configuration: a spatio-temporal Encoder, built off of the Bidirectional ConvLSTM network, and a Decoder, which can learn both spatial and temporal patterns necessary to accurately predict the crop sequences.To …