Urban road extraction based on multi-source data

doi:10.13474/j.cnki.11-2246.2021.306

Abstract

Abstract: The high-accuracy extraction of urban roads can provide data basis and support for many fields, for instance, three-dimensional urban expression, urban terrain analysis, urban construction planning and traffic navigation. Comprehensively combining the advantages of open source road network, street-view images and remote sensing images, and taking the part of Hefei city as the experimental area, an extraction approach for urban road is proposed. Firstly, the maximum likelihood method is used to extract the urban road. Secondly, the void is filled and the connecting areas that does not belong to the urban road are divided by using the open source road network and street view data. Thirdly, the missing parts caused by cover, like vegetation, are disposed of via the width of each road which measured by street view. Finally, the intersections of urban road are improved. Spatial analysis, statistical analysis, geometric measurement, least square fitting methods are involved in the process. To assess the performance of the proposed method, the precision of urban road extraction is evaluated and analyzed. The experimental results indicate that the urban road extraction method proposed in this paper can extract urban road with high precision and the extraction precision is 96.65%, Kappa coefficient is 93.71% and the standard deviation of road width is 0.03 m. In particular, it can improve the incomplete information extraction caused by different spectrum of the same object, different object with the same spectrum and the problem of cover.

Key words: urban road, remote sensing image, open-access road network, street view image, geometric measurement, spatial analysis

CLC Number:

P237

DENG Kai, YANG Cancan, YIN Li, ZHAO Mingwei, JIANG Ling, PENG Daoli. Urban road extraction based on multi-source data[J]. Bulletin of Surveying and Mapping, 2021, 0(10): 60-66,82.

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