Presentation + Paper
18 April 2023 Imagery-based post-flooding infrastructure damages level assessment
Shanyue Guan, Chengcheng Tao, Shufan Liu
Author Affiliations +
Abstract
Coastal flooding events have caused many issues to infrastructure including bridges and highways. How to assess the flooding level and infrastructure damages in a low-cost, rapid, and accurate approach is critical to the infrastructure performance recovery. Due to the limited access to infrastructure during the post-flooding events, it is very challenging to evaluate infrastructure conditions closely. With the help of small unmanned aerial vehicles and onboard cameras, it provides the possibility to inspect the infrastructure conditions from images captured by drones remotely. With the additional help of image processing algorithms, it can help capture the infrastructure conditions and flooding levels from the imageries automatically with post-processing analysis. In this paper, we apply several different image processing algorithms to assess the infrastructure conditions by segmenting the flooding zone from the infrastructure. The performance of these algorithms in assessing infrastructure conditions is compared based on different factors with previously taken airborne imageries of infrastructure and flooding events. The performance of image processing is summarized and future work of assessing the infrastructure post-flooding damages is discussed.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shanyue Guan, Chengcheng Tao, and Shufan Liu "Imagery-based post-flooding infrastructure damages level assessment", Proc. SPIE 12486, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023, 124860J (18 April 2023); https://doi.org/10.1117/12.2658632
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KEYWORDS
Image segmentation

Image processing

Image processing algorithms and systems

Image analysis

Unmanned aerial vehicles

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