CNNs-Based Snowfall Intensity Estimation Model Utilizing CCTV Data

Abstract

Accurate estimation of snowfall intensity is critical for effective winter weather management, transportation safety, and hydrological forecasting. Traditional approaches predominantly rely on ground-based sensors and radar systems, which are often spatially sparse and costly to install and maintain. In this study, we propose a novel convolutional neural networks (CNNs)-based framework for estimating snowfall intensity using images captured by closed-circuit television (CCTV) cameras, which are gaining attention as prominent IoT sensing devices. This approach capitalizes on the extensive availability of CCTV infrastructure, enabling high-frequency and localized monitoring of snowfall patterns. The proposed model is trained using matched datasets comprising snowfall intensity values obtained from PARSIVEL, a type of disdrometer capable of measuring particle information at ground observation stations, and CCTV data captured simultaneously. The study area, Daegwallyeong in Gangwon Province, South Korea, is highly suitable for snowfall observations, with an average of more than 10 snowy days per month during the winter season from December to February. A notable feature of this framework is its ability to estimate snowfall intensity values from CCTV data by leveraging convolutional neural networks. Furthermore, a dedicated preprocessing step was implemented to extract snowfall particles from the original images, thereby enhancing the accuracy of snowfall intensity estimation. Experimental results demonstrate that the CNNs-based framework developed in this study is highly effective for estimating snowfall intensity using CCTV data. Moreover, the incorporation of snowfall particle extraction during preprocessing significantly improved estimation accuracy compared to scenarios where particle extraction was not applied.

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00334564), Korea Meteorological Administration Research and Development Program under Grant RS-2023-00243008, and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00272105).