Sidewalk extraction using aerial and street view images
Document Type
Article
Publication Date
2-19-2021
Department
College of Forest Resources and Environmental Science
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.
Publication Title
Environment and Planning B: Urban Analytics and City Science
Recommended Citation
Ning, H.,
Ye, X.,
Chen, Z.,
Liu, T.,
&
Cao, T.
(2021).
Sidewalk extraction using aerial and street view images.
Environment and Planning B: Urban Analytics and City Science.
http://doi.org/10.1177/2399808321995817
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/14686
Publisher's Statement
© The Author(s) 2021. Publisher’s version of record: https://doi.org/10.1177/2399808321995817