Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities. Traditionally, governments heavily depend on field survey methods to monitor the urban villages, which however are time-consuming, labor-intensive, and possibly delayed. Thanks to widely available and timely updated satellite images, recent studies develop computer vision techniques to detect urban villages efficiently. However, existing studies either focus on simple urban village image classification or fail to provide accurate boundary information. To accurately identify urban village boundaries from satellite images, we harness the power of the vision foundation model and adapt the Segment Anything Model (SAM) to urban village segmentation, named UV-SAM. Specifically, UV-SAM first leverages a small-sized semantic segmentation model to produce mixed prompts for urban villages, including mask, bounding box, and image representations, which are then fed into SAM for fine-grained boundary identification. Extensive experimental results on two datasets in China demonstrate that UV-SAM outperforms existing baselines, and identification results over multiple years show that both the number and area of urban villages are decreasing over time, providing deeper insights into the development trends of urban villages and sheds light on the vision foundation models for sustainable cities. The dataset and codes of this study are available at https://github.com/tsinghua-fib-lab/UV-SAM.

翻译：城中村指城市中心或周边的非正规居住区，其基础设施匮乏、居住条件恶劣，与可持续发展目标（SDGs）中关于减贫、适足住房及可持续城市的议题密切相关。传统上，政府主要依赖实地调查方法监测城中村，但该方法耗时费力且存在滞后性。得益于广泛可用且及时更新的卫星影像，近期研究开发了计算机视觉技术以实现城中村高效检测。然而，现有研究或聚焦于简单的城中村图像分类，或未能提供准确的边界信息。为从卫星影像中精确识别城中村边界，本研究利用视觉基础模型的能力，将分割一切模型（SAM）适配于城中村分割任务，提出UV-SAM方法。具体而言，UV-SAM首先利用小规模语义分割模型为城中村生成混合提示（包括掩码、边界框与图像表征），随后将其输入SAM进行细粒度边界识别。基于中国两个数据集的广泛实验结果表明，UV-SAM优于现有基线方法；多年份识别结果显示，城中村的数量与面积均呈递减趋势，这为理解城中村发展动态提供了深层洞见，也为可持续发展城市的视觉基础模型研究提供了启示。本研究的数据集与代码已公开于https://github.com/tsinghua-fib-lab/UV-SAM。