RiverWatch: a citizen-science approach to river pollution monitoring

Under unprecedented pressure from urbanization and climate change, an ever increasing number of streams worldwide fails to meet good ecological status, thus threatening water quality and ecology, and severely impacting our territories. RiverWatch aims to develop a new disruptive monitoring infrastructure for river systems focused on the transport of buoyant plastics, woody material, and floating pollution. The infrastructure builds on current knowledge in image-based hydrological monitoring to explore novel advancements in unsupervised computer vision techniques for environmental analyses. RiverWatch exploits both camera systems on fixed stations as well as volunteer smartphones to build a dense network of monitoring stations potentially along any river system in the world. This may help to overcome the current limitations in the management and maintenance of high cost installations and at the same time allow us to expand our monitoring capabilities.

Towards establishing a robust infrastructure, RiverWatch focuses on the Sarno River as a case study to develop a dense monitoring system based on cutting-edge unsupervised computer vision. A custom-built mobile app as well as advanced image-based algorithms have been developed to process footage captured by citizens and fixed cameras and collected at a remote server. Image-based algorithms enable analysis of the river flow along with the estimation of surface pollutants and their characterization. Such data is published daily on the project web-Gis online platform featuring a storymap and a public database. High-frequency data at several locations in the drainage network will facilitate implementation of simple modeling tools to describe and forecast pollutant transport in the watershed.