Problem-Driven AI Methodology for Fisheries Innovation

The Norwegian Centre for Research-based Innovation, Visual Intelligence, advances deep learning in marine science. The work focuses on analyzing multifrequency echosounder data to support ecosystem and fisheries management. To address limited labeled data for identifying sand eels in the North Sea, a semi-supervised learning method was developed that combines labeled and unlabeled data, significantly improving accuracy (Choi et al., ICES JMS 2021). Building on this, the method was extended to semantic segmentation (Choi et al., IEEE J. Ocean. Eng. 2023), enabling detailed classification of acoustic signals while reducing the need for costly annotations. More recently, foundation models were explored to tackle challenges like changing conditions in marine environments. By aligning these models with echosounder data and using semantic tokenization, they achieved strong performance with minimal labeled data (Choi et al., NAIS 2025). These innovations highlight the transformative role of AI in exploring and understanding the underwater world.