Rainfall intensity estimation at night using deep learning and urban surveillance cameras in Jiangsu Province, China

Study region This study was conducted in the Yangtze River Delta of eastern China, focusing on the highly urbanized corridor of Nanjing, Yangzhou, and Wuxi (Jiangsu Province), where the nighttime surveillance videos were collected during 2022–2025. Study focus Nighttime rainfall measurement from surveillance video remains challenging due to low visibility, uneven illumination, and complex background noise. To address these issues, this study proposes NightRAIN-Net (Nighttime Rainfall Adaptive and Integrated Network), a novel deep learning (DL) framework tailored for nighttime rainfall estimation. The framework integrates two key modules: Rain-Adaptive Channel Enhancement, which adapts to nighttime lighting variations to enhance raindrop visibility, and Selective Raindrop Localization, which captures raindrop’s shape and structure, mitigating interference from complex backgrounds. Furthermore, NightRAIN-Net integrates LSTM for modeling short-term fluctuations in rainfall intensity and Transformer for learning long-range dependencies, enabling robust performance across diverse precipitation types, from light drizzle to extreme rainfall. New hydrological insights Real-world experimental results demonstrate that NightRAIN-Net achieves a Mean Absolute Error (MAE) of 3.22 mm/h and a Root Mean Squared Error (RMSE) of 3.88 mm/h, while remaining stable across different scenarios and varying camera parameters. It exhibits stable performance across different rainfall scenarios, outperforming state-of-the-art methods. These findings indicate that camera networks can provide scalable, near-continuous (24-hour) high-frequency rainfall information in Yangtze River Delta, supporting urban hydrological monitoring, rapid flood/urban waterlogging early warning, and disaster risk mitigation.