及时准确地获取城中村的空间分布及其环境质量信息对于优化城市空间、改善人居环境具有重要意义。本文以广州市越秀区为例，提出了耦合GF-2高分遥感影像和百度街景影像的城中村识别方法。首先，从街景影像中提取越秀区的街道空间品质特征；其次，在对高分遥感影像预处理并进行多尺度分割的基础上计算光谱、形状、纹理、场景特征和建筑结构5类共计23个特征；最后，融合两种影像的特征用于构建随机森林分类器进行城中村识别。结果表明，基于高分影像和基于街景影像的城中村识别整体精度分别为94.5%和85.7%，Kappa系数分别为0.58和0.31，而两者融合后的分类精度和Kappa系数为96.1%和0.67；其中基于街景影像获取的度量街道空间品质的5个指标贡献了31.6%的特征重要性。鸟瞰视野高分影像和人本视角街景影像提供的信息综合互补，构建了更有区分度的特征空间，减少了城中村的错分现象。本文证实了高分影像和街景影像在特征尺度的融合提升了城中村识别精度。街景影像中的信息可以融入到高分遥感影像等数据源中，辅助进行城中村等非正规居住空间的识别。

China has been experiencing rapid urbanization at an unprecedented rate with the significantly changing urban internal spatial structure. As an inevitable byproduct, Urban Villages (UVs), which refer to informal living spaces with substandard living conditions, have emerged in many newly and quickly industrialized regions and cities. Although UVs provide plenty of living spaces for floating populations, their poor living environment has a negative impact on the urban landscape and public health. Thus, obtaining the spatial distribution and environmental quality information of UVs in a timely manner and accurately for optimizing urban spaces and improving human settlements has practical significance. High-resolution Remote Sensing Images (RSIs) and Street View Images (SVIs) have been employed to quickly extract UV information. However, the combination of RSIs and SVIs for retrieving UV information has received little attention. In this study, we took Yuexiu District in Guangzhou City as the study area and then propose a UV identification method based on the GF-2 high-resolution RSIs and SVIs released by Baidu Company. First, street space quality information was derived from the SVIs using support vector machine and random forest. Then, on the basis of the pre-extraction results on the GF-2 images, multi-scale segmentation was performed based on object-based image analysis, including the building instance and block levels. Twenty-three features were obtained, including the spectrum, shape, texture, building structure, and scene from the RSIs, and five indicators were obtained to measure the street space quality on the basis of the SVIs. Finally, the random forest algorithm was applied to combine the features of the two kinds of images to identify the UVs. Experimental results demonstrate that the UV recognition based on RSIs has an overall accuracy of 94.5% and a Kappa coefficient of 0.58, and the overall accuracy and Kappa coefficient of the UV identification based on SVIs are 85.7% and 0.31, respectively. An overall accuracy of 96.1% and a Kappa coefficient of 0.67 were achieved by the fusion model of the two kinds of images, exhibiting the best performance in UV recognition. Street space quality, textural, structural, and shape features play an import role in the UV recognition based on the fusion model of RSIs and SVIs. The five indicators that measure street space quality on the basis of SVIs contributed 31.6% in feature importance to the fusion model. The information provided by RSIs from the bird view and the SVIs from the human perspective could complement each other, creating an outstanding feature space and reducing the misclassification phenomenon of UVs. The key to this method is integrating the information provided by SVIs into the UV extraction process based on high-resolution RSIs to obtain a highly stable and reliable UV classification result in Yuexiu District. Multi-source data fusion is an important method in improving the ability of RSIs, and other data should be collected to enrich the existing coupling methods and the technical system. This paper reveals that the fusion of high-resolution RSIs and SVIs in the feature level could improve the recognition accuracy of UVs, and the extracted UV distribution data can be used in urban planning and other studies related to urban development. The information in SVIs could be integrated into high-resolution RSIs and other data sources to assist in identifying informal living spaces, such as UVs. Therefore, retrieving highly accurate UV information is feasible through the combination of RSIs and SVIs.