%0 Generic %1 zorzi2020regularization %A Zorzi, Stefano %A Fraundorfer, Friedrich %D 2020 %K segmentation %T Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses %U http://arxiv.org/abs/2007.11840 %X 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.

@misc{zorzi2020regularization, 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.}, added-at = {2022-07-19T17:16:21.000+0200}, author = {Zorzi, Stefano and Fraundorfer, Friedrich}, biburl = {https://www.bibsonomy.org/bibtex/2812662138ab224e291c55cc3868176ba/redtedtezza}, description = {Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses}, interhash = {13af9738488f29a939f9a3567aa84aa8}, intrahash = {812662138ab224e291c55cc3868176ba}, keywords = {segmentation}, note = {cite arxiv:2007.11840}, timestamp = {2022-07-19T17:16:21.000+0200}, title = {Regularization of Building Boundaries in Satellite Images using Adversarial and Regularized Losses}, url = {http://arxiv.org/abs/2007.11840}, year = 2020 }