Semi-Supervised Learning for Spatio-Temporal Landcover Monitoring

Abstract

We consider the task of spatio-temporal landcover monitoring and formulate it as follows. Given a time sequence of multispectral satellite images of an area of interest (AOI), we want to predict a corresponding sequence of semantic segmentations with segment labels representing landcover types. We propose to combine asymmetric UNets (achieving super-resolution segmentation) with Markov chain models to account for both spatial and temporal dependencies. Such models cannot be trained in a supervised manner, as obtaining dense spatio-temporal annotations for satellite image time sequences is infeasible. We therefore focus on the challenge of their semi-supervised training. The proposed approach is evaluated on the task of forest monitoring in a national park in the Czech Republic, which suffers from a severe forest dieback due to droughts and bark beetle outbreaks. We achieve 83 % (90 %) prediction accuracy in this challenging task.