An Improved UNet3+ for Efficient Building Extraction in Typical Villages of the Hehuang Valley, Qinghai

In China, the advancement of the rural revitalisation strategy has increased the demand for precise spatial information on village buildings. However, the irregular architectural forms in the Hehuang Valley of Qinghai complicate this task, making traditional extraction methods inadequate. This study proposes an efficient building extraction method using an improved UNet3+ model. The approach includes a comprehensive optimisation scheme addressing the model's limitations in complex environments, consisting of “structural optimisation, mechanism enhancement, and training strategy improvement.” Structurally, a pruning strategy enhances computational efficiency while maintaining accuracy. Mechanically, the Convolutional Block Attention Module (CBAM) is introduced to improve feature identification through a dual attention mechanism. The training strategy integrates Focal Loss, MS‐SSIM Loss, and IoU Loss functions, along with early stopping and learning schedulers to boost efficiency and generalisation. On an aerial imagery dataset, this method reduces parameters from 30.18M to 7.71M and increases precision from 92.49% to 95.33%. The method was validated using a drone‐captured dataset of typical villages in the Hehuang Valley.