Fraunhofer Institute for Computer Graphics Research IGD
The Ocean Technology Campus–ReefMonitor project aims to establish comprehensive, digitalized, and automated biodiversity monitoring at the Nienhagen artificial reef in the Baltic Sea. From 2025 to 2027, historical and current biodiversity data will be collected, consolidated, and analyzed using modern technologies such as underwater robotics and AI-assisted image processing. This will create new standards for biodiversity monitoring and provide recommendations for the sustainable protection of marine habitats.
The Baltic Sea faces major ecological challenges due to climate change and human activity. OTC-ReefMonitor consolidates more than 20 years of biodiversity data and, for the first time, relies on automated monitoring using underwater robotics and AI-supported camera systems. In its subproject, Fraunhofer IGD Rostock is developing algorithms for the optical detection and analysis of fish populations. The goal is to create a unified, future-proof database that makes changes in biodiversity traceable and can serve as a blueprint for further marine monitoring projects.
Project Description
Objectives and Motivation
Sustainable management of fish stocks requires reliable, continuous monitoring. OTC-ReefMonitor uses modern imaging methods to achieve this: fish are recorded optically by cameras—minimally invasive, cost-efficient, and therefore particularly sustainable. The resulting data provide the basis for identifying early changes and fluctuations in species populations and for aligning management measures with evidence-based insights.
Approach and Division of Work
Camera-based fish monitoring is a core competence of Fraunhofer IGD and is carried out in cooperation with FIUM GmbH. FIUM is responsible for deploying the cameras in the reef and conducts traditional monitoring in parallel with the camera-based surveys. Both data streams—from automated reef monitoring and conventional surveys—are quality-assured and integrated into a central database.
Analysis and Added Value
The camera data are analyzed using AI-based algorithms to detect species, estimate individual numbers, and derive spatial-temporal patterns. In combination with traditional monitoring data, a robust and scalable picture of population development emerges. OTC-ReefMonitor aims to provide an efficient monitoring infrastructure that reduces costs, minimizes interference, and forms the basis for long-term sustainable fish stock management.
