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A review of ground camera-based computer vision techniques for flood management

  • Sanghoon Jun (Hyper-converged Forensic Research Center for Infrastructure, Korea University) ;
  • Hyewoon Jang (Department of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Seungjun Kim (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Jong-Sub Lee (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Donghwi Jung (School of Civil, Environmental and Architectural Engineering, Korea University)
  • Received : 2023.12.27
  • Accepted : 2024.02.16
  • Published : 2024.04.25
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Abstract

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2021R1A5A1032433), and Korea Environment Industry & Technology Institute(KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis, funded by Korea Ministry of Environment (MOE) (RS2023-00218873).

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