Project
DEAL – DEcentrAlised Learning for automated image analysis and biodiversity monitoring
Active project
Project start: May 2024 | Project end: April 2027Funder: The Natural Environment Research Council (NERC)
Principal Investigator: Dr James Clark
Other participants from PML: Dr Tom Masfield (WP1 lead), Dr Dan Clewley (WP2 lead), Jane Netting, David Moffat, Claire Widdicombe (WP4 lead), Elaine Fileman, Saskia Ruhl, Prof Kerry Howell (WP5 lead)
Other participants from University of Glasgow - Dr Nicolas Pugeault (WP3 lead)
Background
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.
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Image Credit: Claire Widdicombe - Plymouth Marine Laboratory
The Challenge
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.
DEAL's Solution
In this project, we will develop a web-based application which addresses these problems by using the Swarm Learning Framework which has been developed by our project partner Hewitt Packard Enterprise (HPE). Swarm Learning allows users to participate in a decentralised, collaborative network without a central server. It makes it possible for users to benefit from each other’s data and learnings, and to collaborate in the building of better classification models with lower biases. At the same time, the system preserves data privacy and reduces inefficiencies and carbon costs associated with the transfer and duplication of large volumes of data. By further partnering with world leaders in plankton and sea floor imaging, we will deliver two operational networks for classifying plankton and sea floor image data.Legacy
We intend the tool and the initial networks we form to act as catalysts, helping to build communities in which data producers coalesce around a set of shared standards, and cooperate in making marine image data suitable for operational biodiversity monitoring
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