  The production of thematic maps depicting land cover is one of the most
common applications of remote sensing. To this end, several semantic
segmentation approaches, based on deep learning, have been proposed in the
literature, but land cover segmentation is still considered an open problem due
to some specific problems related to remote sensing imaging. In this paper we
propose a novel approach to deal with the problem of modelling multiscale
contexts surrounding pixels of different land cover categories. The approach
leverages the computation of a heteroscedastic measure of uncertainty when
classifying individual pixels in an image. This classification uncertainty
measure is used to define a set of memory gates between layers that allow a
principled method to select the optimal decision for each pixel.
