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.


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    Titel :

    Developing an Aerial-Image-Based Approach for Creating Digital Sidewalk Inventories


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Luo, Ji (Autor:in) / Wu, Guoyuan (Autor:in) / Wei, Zhensong (Autor:in) / Boriboonsomsin, Kanok (Autor:in) / Barth, Matthew (Autor:in)


    Erscheinungsdatum :

    04.04.2019




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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