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Automatic building extraction has significant socio-economic applications such as population estimation, urban planning, rapid disaster response, monitoring illegal land acquisition, and change detection. The traditional methods fail to deal with the challenges entirely associated with building extraction,  i.e.  different shape, size, texture of buildings, missing and incomplete buildings due to the occlusion, and high intra-class variation. The existing CNN-based approaches are incapable of recovering the boundary information, especially when the building structures are small and complex. To alleviate the issues faced by current methods, we propose a lightweight attention mechanism-based model – refined cross attention neural network (RCA-Net) for precisely extracting the coarse-to-fine building features. Unlike recent attention mechanism-based approaches, the RCA-Net utilizes spatial and channel attention to capture the long-range multi-scale context. Then, we introduce an efficient attention module, the Global Attention Fuse (GAF) module, that fuses the local and global cross-channel relationships to capture the essential features without enhancing the computational complexity. A loss function, unified loss, is also presented that combines BCE loss and dice loss to alleviate the imbalanced class distribution problem. Experimental results show that our proposed method outperforms the latest method DSNet by 2.06% and 1.47% in IoU and 2.11% and 1.27% in F1-score on the publicly available datasets: Massachusetts building dataset and Inria Aerial Image Labeling dataset, respectively.

Extracting Building Footprints from High-resolution Aerial Imagery Using Refined Cross AttentionNet