Imagine the ocean as a giant puzzle where most of the pieces are hidden in old files or locked away. This effort uses AI to find those missing pieces and plug them into a high-tech digital map of the sea. It allows people to run 'what if' tests to see how the ocean reacts to different changes without having to go out and guess.
Marine biodiversity data is often 'sleeping' or inaccessible, making it impossible for companies and policymakers to accurately predict ocean changes or simulate the impact of human activity.
An AI-powered integration system that converts fragmented biodiversity data into automated flows for the Digital Twin Ocean. This includes a biological component for the DTO and a set of policy-relevant use cases.
If you are a shipping company dealing with strict environmental regulations and route planning — this project developed automated data flows and AI tools that provide a clearer picture of marine ecosystem health. This helps in avoiding sensitive biodiversity zones and ensuring compliance with EU Green Deal targets.
If you are a consultancy dealing with fragmented biodiversity data for clients — this project developed a biological component for the Digital Twin Ocean that enables simulation of scenarios. This allows you to provide evidence-based predictions for marine policy and restoration projects.
If you are a fish farm operator dealing with unpredictable environmental pressures — this project developed new digital tools and services that integrate real-time biodiversity monitoring. This helps in predicting how local ecosystems will change, reducing risk to stock.
Based on available project data, no specific pricing or cost structures are mentioned as the project is EU-funded.
The project is currently developing the biological component and integrating data flows into the EU DTO infrastructure, with a completion date of February 2027.
Based on available project data, specific licensing terms are not provided, but the outputs are intended to serve the EU DTO and EMODnet infrastructures.
It uses AI to activate 'sleeping' data and creates automated flows into EMODnet and the EDITO infrastructure.
The project period runs from September 2023 to February 28, 2027.
The project is backed by a large, diverse group of 32 partners across 14 countries. While heavily weighted toward research (20) and university (7) entities, there is a 12% industry presence with 4 industrial partners and 5 SMEs, suggesting the project is designed for scientific rigor but with a clear path toward practical application in the marine sector.
Contact VLAAMS INSTITUUT VOOR DE ZEE in Belgium
Contact us to explore how to integrate DTO-BioFlow's AI data flows into your marine monitoring strategy.
