Product generation results from state-of-the-art machine learning (ML) and other artificial intelligence (AI) protocols for object detection and thematic mapping of environmental data and creation of visualization products. For example, marine habitat maps in coastal bays and inlets and automated recognition of marine mammals.

Central for a successful implementation of state-of-the-art ML algorithms are use of graphics processing units (GPUs) and fast-working storage. This will be provided by the UNINETT/Sigma2 high performance computation infrastructure.

A key part of the infrastructure is a framework that enables users to teach an algorithm to perform a specific task, to create a desired product, or identify objects of interest.

Further, protocols for re-analysis of previously recorded and stored data for new applications and/or research questions will be developed. This will provide powerful applications for reanalysis of previously collected data, for instance from seabird and mammal research, and for distribution of plastic debris.

A protocol for pre-processing and data analysis will be developed, incl. description of the data format and level of pre-processing needed before uploading data to the SeaBee infrastructure. The pre-processing software will be built from available software, standard methods, and algorithms.