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DeepAqua: Revolutionizing the quantification of Swedish surface water changes with deep learning

The project aims to quantify the changes in surface water over time. We want to create a real-time monitoring system of changes in water bodies by combining remote sensing technologies, including optical and radar imagery, with deep learning techniques to perform computer vision and transfer learning.

Employing this innovative strategy will allow us to calculate the water extent and level dynamics with unprecedented accuracy and response time speed. This approach offers a practical solution for monitoring water extent and level dynamics, making it highly adaptable and scalable for water conservation efforts.

Project period

2024–2026

Funding

Digital Futures

Contact persons

Zahra Kalantari
Zahra Kalantari associate professor
Amir Hossein Payberah
Amir Hossein Payberah associate professor

Other SATORI participants

Francisco Pena Escobar, Karolinska Insitutet

More information on Digital Futures' website

DeepAqua: Revolutionizing the quantification of Swedish surface water changes with deep learning