Camera-based intelligent stream stage sensing for decentralized environmental monitoring

Abstract

Accurate, vast, and real-time coverage of water level monitoring is crucial for the advancement of environmental research, specifically in the areas of climate change, water distribution, and natural disaster preparedness and management. The current state of the monitoring network requires an immediate solution to produce low-cost and accurate water level measurement sensors. This research presents a novel methodology for intelligent stream stage measurement, creating a distinct opportunity for a low-cost, camera-based embedded system that will measure water levels and share surveys to support environmental monitoring and decision-making. It is implemented as a stand-alone device that utilizes a registry of structures and points of interest (POI) along with the core modules of the application logic: (1) deep-learning powered water segmentation; (2) visual servoing; and (3) POI geolocation computation. The implementation relies on a Raspberry-Pi with a motorized camera for automated measurements and is supported by a Proportional–Integral–Derivative controller and multiprocessing. For future work, the involvement of the camera supports further use cases such as recognizing objects (e.g., debris, trees, humans, and boats) on the water surface. Additionally, the method shown can be made into a Progressive Web Application (PWA) that can be used on smartphones to allow crowdsourced citizen science applications for environmental monitoring. HIGHLIGHTS A methodology is proposed for intelligent stream stage measurements to support widespread environmental monitoring.; It is implemented on a Raspberry-Pi for affordable and continuous water level monitoring.; The system is applicable worldwide with validated functionality for operational viability.;

Keywords

• citizen science
• crowdsourcing
• edge computing
• elevation estimation
• river monitoring
• water level measurement