Projects

Eco-Crossing – Development, investigation and demonstration of an intelligent route-optimizing system to reduce the fuel consumption of ferries

Eco-Crossing The key challenges in operational shipping are sustainable and optimal ship operation and the reduction of emissions. In international merchant shipping, weather routing is already being

APLASTIC-Q Arktis – Strandmüllmonitoring arktischer Küsten mittels fernerkundlicher Methoden und automatisierter Datenauswertung durch künstliche Intelligenz

Beach litter is increasingly considered an important indicator of ocean pollution in international agreements. In the past, numerous methods have been developed to record beach litter. However, these

DiTAq – Digital Twin of Aquatic Environments

Digital Twins (DTs) have gained significant popularity in various industries due to their real-time representation of physical objects. Recently, interest in applying DTs to environmental science has

XAI4SFAS – Intelligentes Assistenzsystem für die teilautonome Schiffsführung

Partners marinom GmbH Jade Hochschule Wilhelmshaven/Oldenburg/Elsfleth

HAI-x – Hybrid AI explainer

More and more industry, business and science applications employ multiple AI techniques and AI workflows to solve complex problems and automate as well as optimise complex processes and decision-makin

iMagine – Imaging data and services for aquatic science

Aquatic research often relies on image and video data to gain knowledge about our oceans, lakes and rivers. Current challenges in this research area include analyzing human-caused littering or oil spi

Green-AI Hub Mittelstand – Green-AI Hub Mittelstand

Partners Wuppertal Institut für Klima, Umwelt, Energie gGmbH VDI Technologiezentrum GmbH VDI Zentrum Ressourceneffizienz GmbH

ZLW – Zukunftslabor Wassermanagement

Partners Beteiligte wissenschaftliche Einrichtungen: − Carl von Ossietzky Universität Oldenburg (UOL) − Leibniz Universität Hannover (LUH) − Technische Universität Braunschweig (TUBS) − Technische Uni

APLASTIC-Q-Canada – Machine Learning identification of pollutants and other debris in Canadian Waterways

The project deals with the further development of AI methods for the detection and analysis of litter in rivers and canals. Stationary camera systems are used, which enable long-term monitoring of the

PlasticObs_plus – Verbund - KI: PlasticObs_plus - Maschinelles Lernen auf Multisensordaten der flugzeuggestützten Fernerkundung zur Bekämpfung von Plastikmüll in Meeren und Flüssen

Airborne monitoring for plastic litter detection represents a modern, universal observation method to address the urgent and rapidly increasing problem of plastic litter pollution on a global scale wi