Abstract
Urban villages, as a typical phenomenon in the process of urbanization, play a significant role in urban planning and sustainable development. However, their high-density structures and complex boundaries pose significant challenges for extraction tasks based on remote sensing imagery. To address these challenges, this paper proposes a Multi-domain Enhancement and Boundary Awareness Network (MEBANet) for urban village extraction. MEBANet consists of three core blocks: 1) The spatial-frequency-channel feature extraction block (SFCB), which simultaneously enhances feature representation in the spatial, frequency, and channel domains; 2) The multi-scale boundary awareness block (MBAB), which leverages dense atrous spatial pyramid pooling (DenseASPP) and multi-directional sobel operator convolution to strengthen the perception of complex boundaries; and 3) The deep supervision block (DSB), which accelerates model convergence through multi-level supervision signals. Experiments were conducted on three publicly available datasets from Beijing, Xi'an, and Shenzhen. The results demonstrate that MEBANet outperforms existing methods in terms of precision, recall, F1-score, and IoU. Additionally, cross-dataset transfer experiments validate the robustness and generalization capability of MEBANet. Ablation studies further confirm the effectiveness of each block. This study provides a high-accuracy and automated solution for urban village extraction from high-resolution remote sensing imagery, offering valuable insights for urban planning and management.