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arXiv · 2007.11840

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

Stefano Zorzi·Friedrich Fraundorfer
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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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Stefano Zorzi, Friedrich Fraundorfer. 2020-07-23. Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses. https://arxiv.org/abs/2007.11840

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