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Architectural image segmentation refers to the extraction of architectural objects from remote sensing images. At present, most neural networks ignore the relationship between feature information, and there are problems such as model overfitting and gradient explosion. Thus, this article proposes an improved UNet based on ResNet34 and Attention Module (ResAt-UNet) to solve the related problems. The algorithm adds a two-layer residual structure (BasicBlock) and a regional enhancement attention mechanism (Space Enhancement Area Enhancement, SEAE) to the original framework of UNet, which enhances the network depth, improves the fitting performance, and extracts small objects more accurately. The experimental results show that the network has achieved MIOU of 78.81% in the Massachusetts dataset, and the newly developed model outperforms UNet in both quantitative and qualitative aspects.