And in the case of SeaClear2.0, data on the 20 most common litter types found in our demo and pilot sites.

Our colleagues at TU Delft are collecting images of marine litter items in various environments and under various conditions, to train the SeaClear2.0 AI system so it can effectively detect and classify litter items.

The advanced AI algorithms developed by our colleagues at TU Delft and UNIDU will be integrated within the SeaClear2.0 system for dynamic mapping, detection and classification of seabed marine litter.