Setting the stage for the machine intelligence era in marine science

Abstract

Machine learning, a subfield of artificial intelligence, offers various methods that can be applied in marine science. It supports data-driven learning, which can result in automated decision making of de novo data. It has significant advantages compared with manual analyses that are labour intensive and require considerable time. Machine learning approaches have great potential to improve the quality and extent of marine research by identifying latent patterns and hidden trends, particularly in large datasets that are intractable using other approaches. New sensor technology supports collection of large amounts of data from the marine environment. The rapidly developing machine learning subfield known as deep learning—which applies algorithms (artificial neural networks) inspired by the structure and function of the brain—is able to solve very complex problems by processing big datasets in a short time, sometimes achieving better performance than human experts. Given the opportunities that machine learning can provide, its integration into marine science and marine resource management is inevitable. The purpose of this themed set of articles is to provide as wide a selection as possible of case studies that demonstrate the applications, utility, and promise of machine learning in marine science. We also provide a forward-look by envisioning a marine science of the future into which machine learning has been fully incorporated.

Deep inference of seabird dives from GPS-only records: performance and generalization properties

“…We might expect GPS with lower consumption, higher resolution in the future. Such an expected trend would make more critical the exploitation of development of the proposed deep learning approaches to make the most of the collected high-resolution animal trajectories [5,31,61] The GPS tracks were characterized by short gaps in the regularly sampled sequence of locations, since these devices do not receive a satellite signal while submerged [6,59]. These gaps are therefore sometimes directly considered indicative of diving behaviour [56].…”

“…We might expect GPS with lower consumption and higher resolution in the future. Such an expected trend would make more critical the exploitation of the proposed deep learning approaches to make the most of the collected high-resolution animal trajectories [13], [57], [58].…”

Pulling Back the Blue Curtain: A Pelagic Monitoring Program for the Blue Belt

“…Progress in the automation of image analysis is slow (Lopez-Marcano et al, 2020). However, as with the automation of industries such as warehouses (Schmuck and Benke, 2020), this problem will inevitably be solved (Beyan and Browman, 2020), removing a final constraint on the widespread sampling of oceans with video-based techniques.…”

Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing

“…MLAI has been used in similar contexts to count seals on rocks from drones (McIntosh et al 2018) and identify fish species in baited remote underwater video (BRUVs; Siddiqui et al 2018). A large selection of applications of MLAI to fisheries have been shown recently in Beyan and Browman (2020). Lu et al (2020) for example applied deep CNNs to digital photos collected by observers on pelagic longliners over a 10-year period.…”