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

Sanghoon Jun, Hyewoon Jang, Seungjun Kim, Jong Sub Lee, Donghwi Jung

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)425-443
Number of pages19
JournalComputers and Concrete
Volume33
Issue number4
DOIs
Publication statusPublished - 2024 Apr

Bibliographical note

Publisher Copyright:
Copyright © 2024 Techno-Press, Ltd.

Keywords

  • CCTV
  • computer vision
  • flooding
  • image processing
  • machine learning
  • segmentation

ASJC Scopus subject areas

  • Computational Mechanics

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