Sidewalk extraction using aerial and street view images

Huan Ning, Xinyue Ye, Zhihui Chen, Tao Liu and Tianzhi Cao
Additional contact information
Huan Ning: New Jersey Institute of Technology, USA
Xinyue Ye: Texas A&M University, USA
Zhihui Chen: Suzhou University of Science and Technology, China
Tao Liu: Michigan Technological University, USA

Environment and Planning B, 2022, vol. 49, issue 1, 7-22

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.

Keywords: Sidewalk; aerial imagery; street view imagery; deep learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:49:y:2022:i:1:p:7-22

DOI: 10.1177/2399808321995817

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