Automated building extraction using satellite remote sensing imagery

Automatic extraction of buildings from remote sensing images plays a critical role in urban planning and digital city construction applications. In real-world applications, however, real scenes can be highly complex (e.g., various building structures and shapes, presence of obstacles, and low contrast between buildings and surrounding regions), making automatic building extraction extremely challenging. To conquer this challenge, we propose a novel method called Deep Automatic Building Extraction Network (DABE-Net). It adopts squeeze-and excitation (SE) operations and the residual recurrent convolutional neural network (RRCNN) to construct building-blocks. Furthermore, an attention mechanism is introduced into the network to improve segmentation accuracy. Specifically, to handle small buildings, we highlight small buildings and develop a multi-scale segmentation loss function. The theoretical analysis and experimental results show that the proposed method is effective in building extraction and outperforms several peer methods on the dataset of Mapping challenge competition.