Abstract: In response to the challenges posed by substantial parameters and the loss of building details during downsampling，this study，inspired by lightweight networks，designed a building extraction network （SD-BASNet） incorporating depthwise separable residual blocks and dilated convolution. First，a depthwise separable residual block was designed in the prediction module of the deep supervision encoder-decoder. Depthwise separable convolution was incorporated into the backbone ResNet to prevent oversized convolutional kernels and reduce the number of network parameters. Second，to mitigate the potential decline in accuracy due to network lightweighting，dilated convolution was integrated into the encoder layer of the post-processing optimization module. This strategy effectively expands the receptive field of feature maps，thereby capturing broader contextual information and enhancing the accuracy of building feature extraction. Experiments on the WHU building dataset showed that the proposed network achieved an mIoU of 92.25%，an mPA of 96.59%，a Recall of 96.50%，a Precision of 93.79%，and a F1-score of 92.61%. Compared with current semantic segmentation networks，including PSPNet，SegNet，DeepLabV3，SE-UNet，and UNet++，the SD-BASNet demonstrated significantly improved accuracy and better completeness of building extraction. Compared with the baseline BASNet，the SD-BASNet also exhibited reductions in both parameter count and runtime，demonstrating its effectiveness.