A Method for Fully Automatic Building Footprint Extraction From Remote Sensing Images

Abstract

Abstract Automatically mapping building footprints has a wide range of applications in many fields. In recent years, the automatic building extraction methods based on deep learning show an absolute advantage over the traditional image segmentation methods due to its high speed and high precision. However, the building footprint extracted by deep learning is just an irregular building mask. There is still much work to be done to transform the building mask into a vector building footprint in the usual sense. One of the most important tasks is to determine the orientation of each side of the building. Most of the current methods are based on the building mask to determine the orientation of each side of the building. The biggest disadvantage of this method is that it completely relies on the building mask which is often unsatisfactory. In this case, the article proposes a method to determine the orientation of each side of the building based on the building mask and line segments, thereby effectively avoiding the danger of relying on the building mask. Experiments show that the proposed method can achieve high-speed and high-precision automatic extraction of building footprints from remote sensing images, saving costs.