LAUSR

Rapidly developing remote sensing technology provides massive data for urban planning, mapping, and disaster management. As a carrier of human productive activities, buildings are essential to both urban dynamic monitoring and suburban construction inspection. Fully-convolutional-network-based methods have provided a paradigm for automatically extracting buildings from high-resolution imagery. However, high intraclass variance and complexity are two problems in building extraction. It is hard to identify different scales of buildings by using a single receptive field. For this purpose, in this article, we use the stable encoder– decoder architecture, combined with a grid-based attention gate and atrous spatial pyramid pooling module, to capture and restore features progressively and effectively. A modified ResNet50 encoder is also applied to extract features. The proposed method could learn gated features and distinguish buildings from complex surroundings such as trees. We evaluate our model on two building datasets, WHU aerial building dataset and our DB UAV rural building dataset. Experiments show that our model outperforms other five most recent models. The results also exhibit great potential for extracting buildings with different scales and validate the effectiveness of deep learning in practical scenarios.