DEAL - DEcentrAlised Learning for automated image analysis and biodiversity monitoring

As recognised in the UN Decade of Ocean Science for Sustainable Development (2021-2030) programme (https://oceandecade.org/), frequent and high-quality ocean observations are critical for effective marine management and decision-making. However, at the current time, marine biodiversity remains poorly observed, with sampling methods heavily reliant on infrequent and expensive ship-based observations.
Significant advances have been made in developing marine autonomous imaging platforms that collect data for specific organisms, including microscopic plankton that sit at the base of the ocean food web, and sea floor biota. Such platforms can generate millions of images that have the potential to revolutionise our understanding of marine biodiversity, and to facilitate a step change in marine biodiversity monitoring, allowing fine-scale spatial and temporal trends to be resolved. However, for this revolution to be realised, first high-throughput and high-efficacy classification and analysis tools must also be developed.
Fully supervised machine learning models that are given sufficient, high-quality, labelled data have been demonstrated to achieve reliable, high efficacy classification results. This is a pre-requisite for any operational, automated classification system. The main challenge encountered in the field of marine biodiversity monitoring is obtaining sufficient labelled data to employ a supervised learning approach. At the present time, work on automated image classifiers is highly fragmented, and there is an absence of common standards. Many research teams working on plankton act as individuals, building bespoke classifiers which are trained against limited image data, often from a single instrument. The problem with this approach is that individual researchers are ultimately limited by the amount of data they have been able to collate and label, which leads to results with higher biases that inhibit their incorporation into operational biodiversity...