Improvement of automatic building region extraction based on deep neural network segmentation

Abstract

ABSTRACT This work seeks to improve the accuracy of building region extraction, in which each pixel in a scenery image is determined to be part of a building or part of the background. Specifically, UNet++ and MANet, which are state-of-the-art deep neural networks (DNNs) for segmentation, were applied to building extraction. Our experiment using 105 scenery images in the Zurich Buildings Database (ZuBuD) showed that these networks significantly improved the F-measure by at least 1.67% as compared with conventional building extraction. To address the shortcomings of segmentation networks, we also developed a method based on refinement of the building region extracted by a segmentation network. The proposed method demonstrated its effectiveness by significantly increasing the F-measure by at least 1.15%. Overall, the F-measure was improved by 3.58% as compared with conventional building extraction.