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Smart city article details

Title	Automatic Detection And Assessment Of Pavement Marking Defects With Street View Imagery At The City Scale
ID_Doc	11301
Authors	Kong W.; Zhong T.; Mai X.; Zhang S.; Chen M.; Lv G.
Year	2022
Published	Remote Sensing, 14, 16
DOI	http://dx.doi.org/10.3390/rs14164037
Abstract	Pavement markings could wear out before their expected service life expires, causing traffic safety hazards. However, assessing pavement-marking conditions at the city scale was a great challenge in previous studies. In this article, we advance the method of detecting and evaluating pavement-marking defects at the city scale with Baidu Street View (BSV) images, using a case study in Nanjing. Specifically, we employ inverse perspective mapping (IPM) and a deep learning-based approach to pavement-marking extraction to make efficient use of street-view imageries. In addition, we propose an evaluation system to assess three types of pavement-marking defects, with quantitative and qualitative results provided for each image. Factors causing pavement-marking defects are discussed by mapping the spatial distribution of pavement-marking defects at the city scale. Our proposed methods are conducive to pavement-marking repair operations. Beyond this, this article can contribute to smart urbanism development by creating a new road maintenance solution and ensuring the large-scale realization of intelligent decision-making in urban infrastructure management. © 2022 by the authors.
Author Keywords	pavement-marking defects; smart cities; street-view images; urban management

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