Building extraction based on deep learning is an important research direction in the field of remote sensing. To effectively balance computational efficiency and extraction accuracy, a lightweight building extraction network integrating wavelet transform and global awareness is proposed. First, by integrating parameter sharing, star-shaped operations, and depthwise separable convolution, a star-shared depthwise convolution block is proposed to efficiently and accurately extract building features. Secondly, wavelet transform and frequency-domain spatial attention are introduced to propose an efficient boundary enhancement module that enhances the network’s ability to characterize building boundary features. Finally, employing a lightweight non-local attention mechanism and a hierarchical feature interaction strategy, a global context-aware module is proposed. This module significantly improves the fusion effectiveness of hierarchical features and enhances the overall perception capability during the network’s decoding stage. Experimental results demonstrate that the proposed network achieves Intersection over Union (IoU) scores of 90.08% and 79.16% on the publicly available WHU and Inria building extraction datasets, respectively. Concurrently, the model maintains a low parameter count (Params) of 3.09 million, FLOPs of 4.93 billion, and an inference speed of 30.24 frames per second (FPS). Compared to existing methods such as Swin Transformer, BuildFormer, SDSCUNet, EasyNet, HDNet, and CaSaFormerNet, the proposed method achieves higher extraction accuracy while maintaining low computational complexity, achieving a superior balance between computational efficiency and extraction accuracy.