利用多源空间数据的城中村空间 层次化识别方法.

Objectives: The fine spatial distribution of urban villages is important for urban planning and urban renewal. However, since urban villages are high-level semantic geo-objects and have obscure remote sensing characteristics, it is difficult to obtain fine spatial distribution with good precision from high-density cities using traditional methods. Methods: We propose a novel hierarchical recognition method for urban villages that fuses remote sensing images and social sensing data to finely recognize the urban villages. The method combines the advantages of remote sensing images and social perception data in features. Large and small-scale information are both considered into the process by using the hierarchical framework. Re⁃ sults: The method provides a new idea for a comprehensive understanding of urban villages at a fine scale. A case study has been implemented in Shenzhen. An urban village distribution with a spatial resolution of 2.5 m is obtained. The accuracy assessment shows that the overall accuracy and Kappa coefficient reach 98.68% and 0.807， respectively, indicating the excellent performance of the method. In addition, the gain effects of the hierarchical framework and the fusion of remote sensing images and social perception data are demonstrated, respectively. Conclusions: The results show that both the hierarchical framework and the multi-source spatial data are effective in improving the accuracy of the urban village recognition method. [ABSTRACT FROM AUTHOR]