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In this paper we present a method for building boundary refinement andregularization in satellite images using a fully convolutional neural networktrained with a combination of adversarial and regularized losses. Compared to apure Mask R-CNN model, the overall algorithm can achieve equivalent performancein terms of accuracy and completeness. However, unlike Mask R-CNN that producesirregular footprints, our framework generates regularized and visually pleasingbuilding boundaries which are beneficial in many applications.

Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses