  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.
