Global observing needs in the deep ocean

LA Levin, BJ Bett, AR Gates, P Heimbach… - Frontiers in Marine …, 2019 - frontiersin.org
The deep ocean below 200 m water depth is the least observed, but largest habitat on our
planet by volume and area. Over 150 years of exploration has revealed that this dynamic …

Machine learning for the study of plankton and marine snow from images

JO Irisson, SD Ayata, DJ Lindsay… - Annual Review of …, 2022 - annualreviews.org
Quantitative imaging instruments produce a large number of images of plankton and marine
snow, acquired in a controlled manner, from which the visual characteristics of individual …

[HTML][HTML] The quest for seafloor macrolitter: a critical review of background knowledge, current methods and future prospects

M Canals, CK Pham, M Bergmann… - Environmental …, 2021 - iopscience.iop.org
The seafloor covers some 70% of the Earth's surface and has been recognised as a major
sink for marine litter. Still, litter on the seafloor is the least investigated fraction of marine …

BIIGLE 2.0-browsing and annotating large marine image collections

D Langenkämper, M Zurowietz, T Schoening… - Frontiers in Marine …, 2017 - frontiersin.org
Combining state-of-the art digital imaging technology with different kinds of marine
exploration techniques such as modern autonomous underwater vehicle (AUV), remote …

Recommendations for the standardisation of open taxonomic nomenclature for image-based identifications

T Horton, L Marsh, BJ Bett, AR Gates… - Frontiers in Marine …, 2021 - frontiersin.org
This paper recommends best practice for the use of open nomenclature (ON) signs
applicable to image-based faunal analyses. It is one of numerous initiatives to improve …

[HTML][HTML] Autonomous marine environmental monitoring: Application in decommissioned oil fields

DOB Jones, AR Gates, VAI Huvenne, AB Phillips… - Science of the total …, 2019 - Elsevier
Abstract Hundreds of Oil & Gas Industry structures in the marine environment are
approaching decommissioning. In most areas decommissioning operations will need to be …

Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity

AS Abangan, D Kopp, R Faillettaz - Frontiers in Marine Science, 2023 - frontiersin.org
Through the advancement of observation systems, our vision has far extended its reach into
the world of fishes, and how they interact with fishing gears—breaking through physical …

[HTML][HTML] Megafaunal variation in the abyssal landscape of the Clarion Clipperton Zone

E Simon-Lledó, BJ Bett, VAI Huvenne… - Progress in …, 2019 - Elsevier
The potential for imminent polymetallic nodule mining in the Clarion Clipperton Fracture
Zone (CCZ) has attracted considerable scientific and public attention. This concern stems …

A benthic substrate classification method for seabed images using deep learning: application to management of deep‐sea coral reefs

C Jackett, F Althaus, K Maguire, M Farazi… - Journal of Applied …, 2023 - Wiley Online Library
Protecting deep‐sea coral‐based vulnerable marine ecosystems (VMEs) from human
impacts, particularly bottom trawling, is a major conservation challenge in world oceans …

[HTML][HTML] Improving the predictive capability of benthic species distribution models by incorporating oceanographic data–Towards holistic ecological modelling of a …

TRR Pearman, K Robert, A Callaway, R Hall… - Progress in …, 2020 - Elsevier
Submarine canyons are associated with increased biodiversity, including cold-water coral
(CWC) colonies and reefs which are features of high conservation value that are under …