AI turns street cams into rainfall sensors

A team of researchers from Tianjin University in China has unveiled a breakthrough method that transforms everyday surveillance cameras into high-precision rainfall sensors. The study features a hybrid AI framework that merges image-quality analysis with an enhanced random forest classifier and a deep learning model built on depthwise separable convolution and gated recurrent units.

Tested in Tianjin and Fuzhou, the system delivered high accuracy in detecting rainfall – even in low-light and poor-visibility conditions. The findings are published in Environmental Science and Ecotechnology.

The innovation marks a dramatic shift from conventional rainfall measurement tools like rain gauges, radar, and satellite imaging, which struggle with the spatial and temporal granularity demanded by dynamic urban environments. These traditional methods are costly, infrastructure-heavy, and often fall short during intense storms. Global reductions in ground-based monitoring networks have only deepened the data gaps.

While researchers have previously explored alternatives – from acoustic sensors to signal fluctuations in cellular networks – most solutions require complex infrastructure or calibration. Surveillance cameras, by contrast, are already ubiquitous in cities. Yet issues such as low resolution, background clutter, and variable lighting have held back their utility for weather sensing. The new AI-powered approach opens the door for a smarter, more scalable future in urban rainfall detection.

The proposed system operates through two key modules: a feature extraction module (FeM) and a rainfall estimation module (RiM). The FeM analyses video frames using a novel image quality signature (IQS) method that extracts brightness, contrast, and texture features to detect rain streaks, even from noisy or low-light footage.

Photo supplied.

It then uses an enhanced random forest classifier (eRFC) to classify video frames and apply optimal filters, accurately isolating rain features while discarding irrelevant visual information. The RiM employs a hybrid deep learning model combining depthwise separable convolution (DSC) and gated recurrent units (GRU), enabling it to capture both spatial and temporal patterns in rain events.

This architecture proved highly effective in estimating rainfall intensity (RI) at minute-level intervals. The model was trained on over 60 hours of video data and validated against rain gauge measurements, achieving an R² value of up to 0.95 and a Kling–Gupta efficiency (KGE) of 0.97.

Importantly, the system demonstrated robustness across varying conditions, including daytime and nighttime, and across multiple surveillance cameras. This adaptability marks a significant advancement in cost-effective, scalable rainfall monitoring technologies.

“Our system leverages widely available surveillance infrastructure and advanced AI to fill gaps left by traditional rainfall monitoring techniques,” says Dr Mingna Wang, senior author of the study. “What’s most exciting is that we can now provide highly accurate, real-time rainfall estimates using existing urban technology, even under challenging conditions like night-time or high-density rainfall. This opens the door to smarter flood management systems and more resilient cities in the face of climate change.”

The research offers a scalable and low-cost solution for urban rainfall monitoring, particularly valuable for cities facing infrastructure and budget constraints. By repurposing existing surveillance camera networks, municipalities can implement real-time rainfall monitoring systems without significant additional investment. The model’s ability to function across diverse lighting and environmental conditions makes it useful for deployment in complex urban settings.

The framework can enhance predictive flood modelling, support emergency response strategies, and inform infrastructure planning. Future improvements, such as integrating additional data sources or optimising performance during high-intensity rainfall, could further elevate its utility in climate adaptation and smart city initiatives.