Deep-Learning Based Multiple Land-Cover Map Translation

Abstract

This paper presents a framework for simultaneously translating multiple land-cover maps into a given one in a supervised way. Conversely to existing approaches working on 1-1 translation, we propose a multi-translation setup that increases the generalizability and translation performance, especially on land-cover maps covering restricted spatial extents. The proposed method mainly assumes that the map of interest spatially overlaps at least with one of the other maps. High performance translation is achieved with a Convolutional Neural Network (CNN) based encoder-decoder framework trained with three goals: (i) high-quality translation; (ii) self-reconstruction ability; (iii) mapping of all datasets into a common representation space. Country-scale experimental results show the method effectiveness in translating six highly heterogeneous land-cover maps, achieving significantly better results than the traditional semantic-based method and better results than CNN trained for a 1-1 translation task (+ 9.7% in Overall Accuracy (OA) and +12% in macro F1-score (mF1)).