Automated classification of fauna in seabed photographs: The impact of training and validation dataset size, with considerations for the class imbalance

Three critical factors affecting automated image species recognition performance for camera traps

(2020) Stefan Schneider et al.   Ecology and Evolution

Ecological variables for developing a global deep-ocean monitoring and conservation strategy

(2020) Roberto Danovaro et al.   Nature Ecology & Evolution

Assessing the Repeatability of Automated Seafloor Classification Algorithms, with Application in Marine Protected Area Monitoring

(2020) America Zelada Leon et al.   Remote Sensing

Subtle variation in abyssal terrain induces significant change in benthic megafaunal abundance, diversity, and community structure

(2020) Jennifer M. Durden et al.   PROGRESS IN OCEANOGRAPHY

First network analysis of interspecific associations of abyssal benthic megafauna reveals potential vulnerability of abyssal hill community

(2020) Emily G. Mitchell et al.   PROGRESS IN OCEANOGRAPHY

Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification

(2020) Daniel Langenkämper et al.   Frontiers in Marine Science

Enduring science: Three decades of observing the Northeast Atlantic from the Porcupine Abyssal Plain Sustained Observatory (PAP-SO)

(2020) Susan E. Hartman et al.   PROGRESS IN OCEANOGRAPHY

Monitoring mosaic biotopes in a marine conservation zone by autonomous underwater vehicle

(2019) Noëlie M.A. Benoist et al.   CONSERVATION BIOLOGY

New high-tech flexible networks for the monitoring of deep-sea ecosystems

(2019) Jacopo Aguzzi et al.   ENVIRONMENTAL SCIENCE & TECHNOLOGY

Automated identification of benthic epifauna with computer vision

(2019) N Piechaud et al.   MARINE ECOLOGY PROGRESS SERIES

Biological effects 26 years after simulated deep-sea mining

(2019) Erik Simon-Lledó et al.   Scientific Reports

Climate-Driven Shifts in Marine Species Ranges: Scaling from Organisms to Communities

(2019) Malin L. Pinsky et al.   Annual Review of Marine Science

Response of deep-sea deposit-feeders to detrital inputs: A comparison of two abyssal time-series sites

(2019) Jennifer M. Durden et al.   DEEP-SEA RESEARCH PART II-TOPICAL STUDIES IN OCEANOGRAPHY

Mask R-CNN

(2018) Kaiming He et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Tracking Fish Abundance by Underwater Image Recognition

(2018) Simone Marini et al.   Scientific Reports

Megafaunal variation in the abyssal landscape of the Clarion Clipperton Zone

(2018) Erik Simon-Lledó et al.   PROGRESS IN OCEANOGRAPHY

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Differences in the carbon flows in the benthic food webs of abyssal hill and plain habitats

(2017) Jennifer M. Durden et al.   LIMNOLOGY AND OCEANOGRAPHY

Effectiveness of a deep-sea cold-water coral Marine Protected Area, following eight years of fisheries closure

(2016) V.A.I. Huvenne et al.   BIOLOGICAL CONSERVATION

Fish identification from videos captured in uncontrolled underwater environments

(2016) Faisal Shafait et al.   ICES JOURNAL OF MARINE SCIENCE

Fish species classification in unconstrained underwater environments based on deep learning

(2016) Ahmad Salman et al.   LIMNOLOGY AND OCEANOGRAPHY-METHODS

Comparison of image annotation data generated by multiple investigators for benthic ecology

(2016) JM Durden et al.   MARINE ECOLOGY PROGRESS SERIES

DeepFish: Accurate underwater live fish recognition with a deep architecture

(2016) Hongwei Qin et al.   NEUROCOMPUTING

Comparison of image annotation data generated by multiple investigators for benthic ecology

(2016) JM Durden et al.   MARINE ECOLOGY PROGRESS SERIES

A new method for ecological surveying of the abyss using autonomous underwater vehicle photography

(2016) Kirsty J. Morris et al.   LIMNOLOGY AND OCEANOGRAPHY-METHODS

Landscape-scale spatial heterogeneity in phytodetrital cover and megafauna biomass in the abyss links to modest topographic variation

(2016) Kirsty J. Morris et al.   Scientific Reports

Abyssal hills – hidden source of increased habitat heterogeneity, benthic megafaunal biomass and diversity in the deep sea

(2015) Jennifer M. Durden et al.   PROGRESS IN OCEANOGRAPHY

Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation

(2015) Oscar Beijbom et al.   PLoS One

Marine Litter Distribution and Density in European Seas, from the Shelves to Deep Basins

(2014) Christopher K. Pham et al.   PLoS One

Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies

(2013) Anne Chao et al.   ECOLOGICAL MONOGRAPHS

Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN

(2012) Timm Schoening et al.   PLoS One

Time to automate identification

(2010) Norman MacLeod et al.   NATURE

Long-term change in the abyssal NE Atlantic: The ‘Amperima Event’ revisited

(2009) D.S.M. Billett et al.   DEEP-SEA RESEARCH PART II-TOPICAL STUDIES IN OCEANOGRAPHY

Use of machine-learning algorithms for the automated detection of cold-water coral habitats: a pilot study

(2009) A Purser et al.   MARINE ECOLOGY PROGRESS SERIES