Security cameras can keep us safe and teach us about rain
The quantity and quality of precipitation data are crucial to hydrological research, water resource management, and analysis of global change. Rain gauges collect raindrops at ground level and are a classic approach to measuring rainfall. However, rain gauges are usually spatially sparse; thus, they cannot adequately capture the spatial variability of precipitation, especially in topographically challenging areas, such as mountainous or urban areas. Cities like Chongqing are both urban and mountainous regions, which make the collection of accurate precipitation data especially taxing.
Recently, esteemed environmental science journal Water Resources Research published a paper by Professor Zheng Yi’s research team from the School of Environmental Science and Engineering (SESE) at Southern University of Science and Engineering (SUSTech). The paper, entitled “Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall with Ordinary Surveillance Cameras,” sought to examine the use of the Internet of Things for the hydrological sensing. A summary of the paper was also published on Earth and Space Science News Research Spotlight that reports on a small number of important research results.
“Opportunistic sensing” represents an appealing idea for collecting unconventional data with broad spatial coverage and high resolution, but few studies have explored its feasibility in hydrology. As we enter the era of the Internet of Things, “anything may become data” has become a common meme. The density of CCTV cameras has led to researchers considering their use for a wider range of purposes. Visual data from surveillance cameras is more informative, intuitive, and achievable, with commercial applications in areas such as traffic management. CCTV cameras can quantify rainfall intensity through rain streaks, and researchers have developed algorithms for attempting to better understand rainfall intensity. However, that research had not yet provided accurate measurements in real-world environments that are visually complex.
Professor Zheng Yi’s research group proposed a novel approach for using CCTV cameras to measure environmental data. They developed a rain print extraction and segmentation algorithm for videos of rainfall. The algorithm combined geometric optics and raindrop spectrum analysis, thereby providing high-frequency, fixed-point rainfall estimations. This algorithm was proven to provide low cost, high resolution and real-time results, thereby allowing for the dense collection of closed circuit television cameras in urban areas to be used as a rainfall monitoring network.
Tracking of real-time rainfall data will assist scientists, researchers and institutions in simulating climate change, flood forecasting, water resources management and other water environment monitoring purposes.
The research team is currently working with local meteorological authorities to apply the new method across Shenzhen.
Ph.D. candidate Jiang Shijie of the School of Environmental Science and Engineering at SUSTech (co-cultivated with National University of Singapore) is the first author and Professor Zheng Yi is the correspondent author.
The research was funded by the National Natural Science Foundation of China, the State Key Laboratory for Comprehensive Prevention and Control of Surface Water and Groundwater Pollution in Environmental Protection Watershed, and the Guangdong Key Laboratory for Prevention, Control and Rehabilitation of Soil and Groundwater Pollution.