The AIME project utilises artificial intelligence to integrate heterogeneous data — satellite imagery, underwater images and videos, environmental DNA, GPS coordinates and telemetry — and to produce marine biodiversity indicators at different scales. The methods combine deep learning, ontologies and knowledge bases, natural language processing, machine learning and dynamic Bayesian networks, with a hybridisation of symbolic and sub-symbolic approaches aimed at enhancing the robustness and accuracy of the indicators.

marine biology, bioindicators, oceanography, artificial intelligence

The SMART-WING project is breaking new ground by developing a smart wing that can automatically switch between high and low altitude modes depending on the presence of animals. Thanks to its on-board artificial intelligence, the wing detects megafauna in real time, automatically descends to capture high-resolution images, and then resumes flight. This system will enable accurate data to be collected over large areas in an autonomous, efficient and environmentally friendly manner.

marine biology, biological oceanography

The Nawras project utilises artificial intelligence to assess the law's ability to protect the marine environment. It is developing a methodology that combines legal science and AI to identify indicators for measuring 'where, when and how the law protects the oceans', with artificial intelligence automating the extraction of legal information.

law, marine biology, oceanography, artificial intelligence