A benthic substrate classification method for seabed images using deep learning: Application to management of deep-sea coral reefs.
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Protecting deep-sea coral-based vulnerable marine ecosystems (VMEs) from human impacts, particularly bottom trawling, is a major conservation challenge in world oceans. Management processes for these ecosystems are weakened by key uncertainties that could be substantially addressed by having much greater volumes of quantitative image-derived data that detail the distribution and abundance of coral reefs and the nature of impacts upon them. Considerably greater volumes of data could be available if the resource costs of image annotation are reduced. In this paper we propose a solution: a deep learning system capable of automatically identifying reef-building stony corals amongst other seabed substrata in much larger volumes of seabed imagery than was previously possible. Using a previously annotated dataset, we trained a convolutional neural network on approximately 70,000 classified images (‘snips’) comprising six benthic substrate classes, including reef-building stony coral—‘coral m.....
JournalJournal of Applied Ecology
Sustainable Development Goals (SDG)14.a
Maturity LevelPilot or Demonstrated
CitationJackett, C., Althaus, F., Maguire, K., Farazi, M., Scoulding, B., Untiedt, C., Ryan, T., Shanks, P., Brodie, P.,and Williams, A. (2023) A benthic substrate classification method for seabed images using deep learning: Application to management of deep-sea coral reefs. Journal of Applied Ecology, 2023, 20pp. DOI: https://doi.org/10.1111/1365-2664.14408
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