  Machine learning has proven to be useful in classification and segmentation
of images. In this paper, we evaluate a training methodology for pixel-wise
segmentation on high resolution satellite images using progressive growing of
generative adversarial networks. We apply our model to segmenting building
rooftops and compare these results to conventional methods for rooftop
segmentation. We present our findings using the SpaceNet version 2 dataset.
Progressive GAN training achieved a test accuracy of 93% compared to 89% for
traditional GAN training.
