<scp>CRU</scp> ‐Net: An Innovative Network for Building Extraction From Remote Sensing Images Based on Channel Enhancement and Multiscale Spatial Attention With <scp>ResNet</scp>

ABSTRACT Building extraction from high‐resolution remote sensing images enables systematic quantification of urban form evolution, supports critical decision‐making in infrastructure development and land‐use optimization, and facilitates disaster resilience and risk assessment. However, the characteristics of urban landscapes, such as high‐density building distribution and heterogeneous building geometries, pose significant challenges in achieving pixel‐level accuracy. To address these challenges, we proposed an innovative network for building extraction based on channel enhancement and multiscale spatial attention with ResNet (CRU‐Net). First, CRU‐Net employed U‐Net as the core architecture, with ResNet34 as the encoder component. Second, to fully exploit the ability of convolutional neural networks to extract features at multiple scales, a new dilated residual block (DRB) was designed by combining a residual block with dilated convolution. Replacing the residual blocks in ResNet34 with DRB enhances the ability of CRU‐Net to extract semantic information at different scales for building extraction. Next, the channel enhancement and multiscale spatial attention (CEMS) module was proposed and added to the skip connection of the network. CEMS is capable of learning more important features both spatially and channel‐wise, enhancing the network's feature representation ability. Finally, a joint loss function combining normalized cross‐correlation loss and binary cross‐entropy loss was introduced to train the network, enabling it to focus on learning both global and local features of the building. The experiments show that CRU‐Net achieves high accuracy and intersection over union (IoU) values on the Massachusetts building dataset, Inria aerial image labeling dataset, and WHU building dataset.