Assessment of Perceived and Physical Walkability Using Street View Images and Deep Learning Technology

Youngok Kang, Jiyeon Kim, Jiyoung Park, Jiyoon Lee

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

As neighborhood walkability has gradually become an important topic in various fields, many cities around the world are promoting an eco-friendly and people-centered walking environment as a top priority in urban planning. The purpose of this study is to visualize physical and perceived walkability in detail and analyze the differences to prepare alternatives for improving the neighborhood’s walking environment. The study area is Jeonju City, one of the medium-sized cities in Korea. For the evaluation of perceived walkability, 196,624 street view images were crawled and 127,317 pairs of training datasets were constructed. After developing a convolutional neural network model, the scores of perceived walkability are predicted. For the evaluation of physical walkability, eight indicators are selected, and the score of overall physical walkability is calculated by combining the scores of the eight indicators. After that, the scores of perceived and physical walkability are visualized, and the difference between them is analyzed. This study is novel in three aspects. First, we develop a deep learning model that can improve the accuracy of perceived walkability using street view images, even in small and medium-sized cities. Second, in analyzing the characteristics of street view images, the possibilities and limitations of the semantic segmentation technique are confirmed. Third, the differences between perceived and physical walkability are analyzed in detail, and how the results of our study can be used to prepare alternatives for improving the walking environment is presented.

Original languageEnglish
Article number186
JournalISPRS International Journal of Geo-Information
Volume12
Issue number5
DOIs
StatePublished - May 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • crowdsourced data
  • deep learning technology
  • difference between perceived and physical walkability
  • perceived walkability
  • physical walkability
  • semantic segmentation
  • street view images

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