Detection of urban flood inundation from traffic images using deep learning methods

Abstract

Abstract Urban hydrological monitoring is the basis for urban hydrological analysis and storm flood control. However, current monitoring of urban hydrological data is insufficient, including flood inundation depth. This limits calibration and flood early warning ability of the hydrological model. In response to this limitation, a method for evaluating the depth of urban floods based on image recognition using deep learning was established in this study. This method can identify the submerged positions of pedestrians or vehicles in the image, such as pedestrian legs and car exhaust pipes, using the object recognition model YOLOv4. The mean average precision of water depth recognition in a dataset of 1177 flood images reached 89.29%. The established method extracted on-site, real-time, and continuous water depth data from images or video data provided by existing traffic cameras. This system does not require installation of additional water gauges and thus has a low cost and immediate usability.