Easy-Net: A Lightweight Building Extraction Network Based on Building Features

The efficient, accurate, and automatic extraction of buildings from remote sensing imagery is a key task in the intelligent extraction of remote sensing information owing to its importance in applications including urban planning, change detection, and unmanned aerial vehicle (UAV) navigation. However, the fast and accurate extraction of buildings from remote sensing images remains difficult owing to the complex, variable nature of geographic information and the variable external appearances of buildings. This is because many existing building extraction networks fail to incorporate building features into their design. Also, generally, simple lightweight networks do not accurately identify buildings, while large complex networks have high operational costs. Therefore, in this paper, we proposed a simple, effective feature fusion strategy based on the building features extracted by the lightweight backbone network; improved the feature fusion performance by combining the advantages of a convolutional neural network (CNN) and transformer; and presented the lightweight building extraction network Easy-Net. We conducted experiments comparing Easy-Net with existing high-performing networks on the public dataset WHU and self-made datasets; results showed the efficiency and accuracy of our method in the task of building extraction from remote sensing images. Thus, Easy-Net was found to be a promising alternative to existing building extraction networks. Code has been released at: github.com/teddy132/EasyNet_for_building_extraction.