Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions

Zhaoya Gong, Qiwei Ma, Changcheng Kan and Qianyun Qi
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Zhaoya Gong: School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
Qiwei Ma: School of Architecture, Tsinghua University, Beijing 100084, China
Changcheng Kan: Baidu.com Times Technology (Beijing) Co., Ltd., Beijing 100085, China
Qianyun Qi: China Academy of Urban Planning and Design, Beijing 100044, China

Sustainability, 2019, vol. 11, issue 22, 1-17

Abstract: Streets, as one type of land use, are generally treated as developed or impervious areas in most of the land-use/land-cover studies. This coarse classification substantially understates the value of streets as a type of public space with the most complexity. Street space, being an important arena for urban vitality, is valued by various dimensions, such as transportation, recreation, aesthetics, public health, and social interactions. Traditional remote sensing approaches taking a sky viewpoint cannot capture these dimensions not only due to the resolution issue but also the lack of a citizen viewpoint. The proliferation of street view images provides an unprecedented opportunity to characterize street spaces from a citizen perspective at the human scale for an entire city. This paper aims to characterize and classify street spaces based on features extracted from street view images by a deep learning model of computer vision. A rule-based clustering method is devised to support the empirically generated classification of street spaces. The proposed classification scheme of street spaces can serve as an indirect indicator of place-related functions if not a direct one, once its relationship with urban functions is empirically tested and established. This approach is empirically applied to Beijing city to demonstrate its validity.

Keywords: street view images; streetscape classification; spatial indicator of urban functions; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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