01703725

Component

https://doi.org/10.1177/0361198119842820

To support active mobility, extensive work has been focused on planning, maintaining, and enhancing infrastructure, such as sidewalks. A significant amount of these efforts has to go on the setup and maintenance of sidewalk inventory on a certain geographic scale (e.g., citywide, statewide). To address the stated problem, this paper proposes the development of an aerial-image-based approach that can 1) extract the features of sidewalks based on digital vehicle road network; 2) overlay the initial sidewalk features with aerial imagery and extract aerial images around the sidewalk area; 3) apply a machine learning algorithm to classify sidewalk images into two major categories, that is, concrete surface present or sidewalks missing; and 4) construct a connected sidewalk network in a time-efficient and cost-effective manner. A deep convolutional neural network is applied to classify the extracted sidewalk images. The learning algorithm gives 97.22% total predication rate for the test set and 92.6% total predication rate in the blind test. The proposed method takes full advantage of available data sources and builds on top of the existing roadway network to digitize sidewalks.

19-02287

English

pp 499-507

2019-8

Transportation Research Record: Journal of the Transportation Research Board

Digital/other

Figures (9) ; References (21) ; Tables (2)

Aerial photogrammetry; Algorithms; Digital mapping; Inventory; Machine learning; Neural networks; Sidewalks

Data and Information Technology; Highways; Planning and Forecasting

TRIS, TRB, ATRI

Mar 20 2019 3:21PM