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 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 support active mobility, extensive work has been focused on planning, maintaining, and enhancing infrastructure, such as sidewalks.
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