01901124

Component

https://doi.org/10.1177/03611981231210545

Pedestrian network creation and completion for various tasks in transportation planning engineering generally requires laborious manual labeling of sidewalks and crosswalks. In this study, we employed the deep learning-based object detection model YOLO v5 for detecting crosswalks using Google Maps satellite imagery data of four North American cities. We also tested for two additional scenarios involving two image-processing techniques to exploit the special properties of crosswalks. Observations showed that the original images performed better than with binary thresholding and nearly the same even when noisy regions outside the right-of-way were removed. We proposed an algorithm to assign the detected crosswalks to intersection legs. We demonstrated the effectiveness of this technique for Washington, D.C. and Los Angeles, CA, showing classification accuracy rates of 71% and 89%, respectively. We also showed the influence of increasing distance threshold, a tolerance radius of prediction accuracy, in degrading the classification performance. This method, when combined with existing methods for sidewalk detection from aerial and street view images, could be reasonably used to help complete urban pedestrian networks in cities where high-resolution satellite images are available.

Rajat Verma https://orcid.org/0000-0002-2273-8706© National Academy of Sciences: Transportation Research Board 2023.

English

0000-0002-2273-8706

0000-0001-8754-9925

pp 845-856

2024-7

Transportation Research Record: Journal of the Transportation Research Board

Web

References (40)

Crosswalks; Image analysis; Machine learning; Networks; Pedestrian areas

Google Maps

Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting

TRIS, TRB, ATRI

Nov 28 2023 3:02PM