(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.
|Publication status||Published - 2021 May 1|
Bibliographical noteFunding Information:
Funding: This research was supported by National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1C1C1013021).
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
All Science Journal Classification (ASJC) codes
- Analytical Chemistry
- Information Systems
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering