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/Author (Zhaoya Gong, Qiwei Ma, Changcheng Kan and Qianyun Qi)
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/Keywords (street view images; streetscape classification; spatial indicator of urban functions; deep learning)
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/Subject (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.)
/Title (Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions)
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Zhaoya Gong, Qiwei Ma, Changcheng Kan and Qianyun Qi
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.
Classifying Street Spaces with Street View Images for a Spatial Indicator of Urban Functions
2019-11-15T16:10:54+08:00
LaTeX with hyperref package
2020-01-29T12:41:49Z
2020-01-29T12:41:49Z
street view images; streetscape classification; spatial indicator of urban functions; deep learning
pdfTeX-1.40.18
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This is pdfTeX, Version 3.14159265-2.6-1.40.18 (TeX Live 2017/W32TeX) kpathsea version 6.2.3
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