Abstract
In this paper we present a method for building boundary refinement and
regularization in satellite images using a fully convolutional neural network
trained with a combination of adversarial and regularized losses. Compared to a
pure Mask R-CNN model, the overall algorithm can achieve equivalent performance
in terms of accuracy and completeness. However, unlike Mask R-CNN that produces
irregular footprints, our framework generates regularized and visually pleasing
building boundaries which are beneficial in many applications.
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