Toward Improved Real‐Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Abstract

Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real‐time urban flood forecasting. Using existing rainfall intensity measurement technologies, including ground rainfall gauges, ground‐based radar, and satellite remote sensing, it is challenging to obtain estimates of the desired quality and resolution. However, an approach based on processing distributed surveillance camera network imagery through machine learning algorithms to estimate rainfall intensities shows considerable promise. Here, we present a novel approach that first extracts raindrop information from the surveillance camera images (rather than using the raw imagery directly), followed by the use of convolutional neural networks (CNN) to estimate rainfall intensity from the resulting raindrop information. Evaluation of the approach on 12 rainfall events under both daytime and nighttime conditions shows that generalization ability, and especially nighttime predictive performance, is significantly improved. This represents an important step toward achieving real‐time, high spatiotemporal resolution, measurement of urban rainfall at relatively low cost.This article is protected by copyright. All rights reserved.

Classification of Rainfall Intensity and Cloud Type from Dash Cam Images Using Feature Removal by Masking

“…Although there is related research on rainfall estimation using images [16,17], there is no open-source dataset consisting of dash cam images annotated with rainfall amounts. A dataset was constructed using dash cam images and JMA observation records taken in Okinawa Prefecture in 2022.…”