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.
Copyright: © 2025 Chang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.