We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic fully convolutional network (FCN). A generative adversarial network (GAN) is then involved to perform a regularization of building boundaries to make them more realistic, i.e., having more rectilinear outlines which construct right angles if required. This is achieved through the interplay between the discriminator which gives a probability of input image being true and generator that learns from discriminator's response to create more realistic images. Finally, we train the backbone convolutional neural network (CNN) which is adapted to predict sparse outcomes corresponding to building corners out of regularized building segmentation results. Experiments on three building segmentation datasets demonstrate that the proposed method is not only capable of obtaining accurate results, but also of producing visually pleasing building outlines parameterized as polygons.

本文提出了一种基于机器学习方法的建筑物分割掩码自动正则化和多边形化方法，通过泛化的完全卷积网络（FCN）进行建筑物分割映射的预测，采用生成对抗网络（GAN）对建筑物边界进行正则化以产生更加真实、更直接的轮廓，并利用骨干卷积神经网络（CNN） 在多次实验中展示了该方法不仅可以获得精确结果，而且可视化多边形化的建筑轮廓图像也很好。

建筑物分割掩膜的机器学习正则化和多边形化