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Sidewalk extraction using aerial and street view images

Author

Listed:
  • 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)

  • Tianzhi Cao

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.

Suggested Citation

  • Huan Ning & Xinyue Ye & Zhihui Chen & Tao Liu & Tianzhi Cao, 2022. "Sidewalk extraction using aerial and street view images," Environment and Planning B, , vol. 49(1), pages 7-22, January.
    • Handle: RePEc:sae:envirb:v:49:y:2022:i:1:p:7-22
    DOI: 10.1177/2399808321995817
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    Cited by:

    1. Hennessy, Emily Rose & Ai, Chengbo, 2023. "A comparative analysis of pedestrian network connectivity and accessibility using network approximation," Journal of Transport Geography, Elsevier, vol. 111(C).

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