Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping

Abstract

Successful flood recovery and evacuation require access to reliable flood depth information. Most existing flood mapping tools do not provide real-time flood maps of inundated streets in and around residential areas. In this paper, a deep convolutional network is used to determine flood depth with high spatial resolution by analyzing crowdsourced images of submerged traffic signs. Testing the model on photos from a recent flood in the U.S. and Canada yields a mean absolute error of 6.978 in., which is on par with previous studies, thus demonstrating the applicability of this approach to low-cost, accurate, and real-time flood risk mapping.

arXiv comment2022 European Conference on Computing in Construction

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• YOLOv4: Optimal Speed and Accuracy of Object Detection
• An overview of gradient descent optimization algorithms
• DSSD : Deconvolutional Single Shot Detector
• A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
• Feasibility study of urban flood mapping using traffic signs for route optimization
• Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition