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Integrating high-resolution remote sensing and street view images to identify urban villages： A case study in Yuexiu District， Guangzhou City

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

  • Email：cuic@lreis.ac.cn
  • Introduction：崔成，研究方向为空间数据挖掘。E-mail： cuic@lreis.ac.cn
CUI Cheng12,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

ZHAO Lu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

  • Email：renhy@lreis.ac.cn
  • Introduction：任红艳，研究方向为环境遥感与公共健康。E-mail： renhy@lreis.ac.cn
REN Hongyan1*,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

LU Weili12,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

HUANG Yaohuan12

Resümee

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.

Schlüsselwort

high-resolution remote sensing image;street view image;urban village;street space quality;Random Forest;image fusion;multi-scale segmentation

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