@article{ref1,
title="Developing an aerial-image-based approach for creating digital sidewalk inventories",
journal="Transportation research record",
year="2019",
author="Luo, Ji and Wu, Guoyuan and Wei, Zhensong and Boriboonsomsin, Kanok and Barth, Matthew",
volume="2673",
number="8",
pages="499-507",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/0361198119842820",
url="http://dx.doi.org/10.1177/0361198119842820"
}