Extraction of Building Roofs and Facades Based on Axial Feature Enhancement

Affected by the vertical expansion of the city and non-nadir imaging, the roof of a building cannot completely overlap with its bottom footprint. Therefore, it is difficult to obtain the actual precise location of the building only by extracting the roof of the building. This paper proposes a new model framework that embeds the Axial Feature Enhancement Module in the network to represent the spatial dependence of the building roof and facade, and relies on the axial feature enhancement loss to constrain the network to learn the category distribution of the horizontal and vertical axes. As far as we know, this is the first time that the importance of building facade information is considered to carry out research on building roof and facade extraction. The experimental results also demonstrate the effectiveness of the method proposed in this article.