Sidewalk extraction using aerial and street view images

Document Type

Article

Publication Date

1-1-2022

Abstract

A reliable, punctual, and spatially accurate dataset of sidewalks is vital for identifying where improvements can be made upon urban environment to enhance multi-modal accessibility, social cohesion, and residents' physical activity. This paper develops a synthetically new spatial procedure to extract the sidewalk by integrating the detected results from aerial and street view imagery. We first train neural networks to extract sidewalks from aerial images, and then use pre-trained models to restore occluded and missing sidewalks from street view images. By combining the results from both data sources, a complete network of sidewalks can be produced. Our case study includes four counties in the U.S., and both precision and recall reach about 0.9. The street view imagery helps restore the occluded sidewalks and largely enhances the sidewalk network's connectivity by linking 20% of dangles.

Identifier

85101174819 (Scopus)

Publication Title

Environment and Planning B Urban Analytics and City Science

External Full Text Location

https://doi.org/10.1177/2399808321995817

e-ISSN

23998091

ISSN

23998083

First Page

7

Last Page

22

Issue

1

Volume

49

Grant

1937908

Fund Ref

Center for Selective C-H Functionalization, National Science Foundation

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