Classifying street spaces with Street View images for a spatial indicator of urban functions

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • Tsinghua University
  • Baidu.com Times Technology (Beijing) Co., Ltd
  • China Academy of Urban Planning and Design

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.

Details

Original languageEnglish
Article number6424
Number of pages17
JournalSustainability
Volume11
Issue number22
Publication statusPublished - 15 Nov 2019

Keywords

  • Street view images, Streetscape classification, Spatial indicator of urban functions, Deep learning