RiverSnap - estimation of river hydraulic parameters using machine learning/AI models
RiverSnap is a citizen science-based project that employs machine learning/AI to detect/estimate hydraulic parameters such as water level, river course, and vegetation from images captured by smartphones or obtained from other sources.
The project pursues two interlinked objectives. On the one hand, it aims to collect smartphone and drone river scene images voluntarily shared by scientists and community members or with other citizen scientist platforms like crowdwater and CoastSnap, which are then combined with in-situ data (e.g., official gauge station data). On the other hand, it aims to develop, implement, and investigate robust, intelligent, cost-effective, and efficient artificial intelligence (AI)-based approaches for extracting hydrological parameters or features of rivers from these images or Videos.
Beteiligte ZDIN Einrichtungen:
Beteiligte Wissenschaftler*innen:
- Prof. Dr.-Ing. habil. Torsten Schlurmann (Leibniz Universität Hannover)
- Dr.-Ing. Mario Welzel (Leibniz Universität Hannover)
