V-FloodNet: A video segmentation system for urban flood detection and quantification. (February 2023)

• Record Type: Journal Article
• Authors: Liang, Yongqing; Li, Xin; Tsai, Brian; Chen, Qin; Jafari, Navid
• Is Part Of: Environmental modelling & software. Volume 160(2023)
• Publication Date: 2023-02
• Subjects: Water level estimation -- Water image/video segmentation -- Object dimension estimation -- Flood monitoring -- Deep learning; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105586
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Effective monitoring and forecasting of urban flooding are crucial for climate change adaptation and resilience around the world. We proposed a novel and automatic system for urban flood detection and quantification. Our software takes image/video data of flooding as inputs because the such data source is easy to obtain and widely available compared with conventional water level sensors or flood gauges. First, the kernel of our system is a robust water region segmentation module that detects flooded regions together with surrounding reference objects from the scene. We combine image and video segmentation technologies to make the system reliable under varying weather and illumination conditions. Second, our system uses the detected situated objects to determine the inundation depth. Field experiments demonstrate that our segmentation results are accurate and reliable; and our system can detect flooding and estimate inundation depths from images and time-lapse videos. Our code is available at https://github.com/xmlyqing00/V-FloodNet . Highlights: An automatic system for water level or inundation depth estimation from images and videos. A reliable deep learning based flood segmentation framework for images and videos. A new water depth estimation model based on reference object template matching. Releasing a new dataset containing water-related images and videos, with segmentation labels/contours, and water depth information.