Developing sidewalk inventory data using street view images

Bumjoon Kang, Sangwon Lee, Shengyuan Zou

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

(1) Background: Public sidewalk GIS data are essential for smart city development. We developed an automated street‐level sidewalk detection method with image‐processing Google Street View data. (2) Methods: Street view images were processed to produce graph‐based segmentations. Image segment regions were manually labeled and a random forest classifier was established. We used multiple aggregation steps to determine street‐level sidewalk presence. (3) Results: In total, 2438 GSV street images and 78,255 segmented image regions were examined. The image‐level sidewalk classifier had an 87% accuracy rate. The street‐level sidewalk classifier performed with nearly 95% accuracy in most streets in the study area. (4) Conclusions: Highly accurate street‐level sidewalk GIS data can be successfully developed using street view images.

Original languageEnglish
Article number3300
JournalSensors
Volume21
Issue number9
DOIs
Publication statusPublished - 2021 May 1

Bibliographical note

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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