ABSTRACT

With increase in urbanization and Earth Sciences research into urban areas, the need to quickly and accurately segment urban rooftop maps has never been greater. Current machine learning techniques struggle to produce high accuracy maps in dense urban zones where there is high image noise and foot print overlap. In this paper, we evaluate a training methodology for pixel-wise segmentation for high-resolution satellite imagery using progressive growing of generative adversarial networks as a solution. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We evaluate our approach using the SpaceNet version 2 and xView datasets. Our experiments show that for SpaceNet, progressive Generative Adversarial Network (GAN) training achieved a test accuracy of 93% compared to 89% for traditional GAN training and 87% for U-Net architecture, while for xView, we achieved 71% accuracy using progressive GAN training compared to 69% through traditional GAN training and 65% using U-Net.

摘要

随着城市化和地球科学研究领域的发展，对快速准确地分割城市屋顶地图的需求从未如此迫切。当前的机器学习技术努力在高图像噪声和足迹重叠的人口稠密的市区中生成高精度地图。在本文中，我们使用生成式对抗网络的逐步发展来评估高分辨率卫星图像像素分割的训练方法。我们将模型应用于建筑物屋顶的分割，并将这些结果与传统的屋顶分割方法进行比较。我们使用SpaceNet版本2和xView数据集评估我们的方法。我们的实验表明，对于SpaceNet，