OD41A:
Artificial Intelligence Systems for Advancing the Study of Aquatic Ecosystems I

Session ID#: 92488

Session Description:
Scientists studying aquatic ecosystems are increasingly able to collect big data; large and complex datasets necessitating more computing intensive analyses. While the data (e.g., from acoustics or omics) themselves can be quite different, the methods to analyze them are often rather similar. In many cases, artificial intelligence (AI; e.g., machine learning, deep learning) can expedite analyses by limiting the amount of human interaction needed. Furthermore, AI-based analyses are often able to detect patterns that traditional statistics do not pick up on. AI research has begun to surface in all corners of aquatic sciences. Researchers dealing with in situ imagery, and passive and active acoustic data have made particularly rapid progress, but other research areas are also pushing boundaries by applying AI techniques. Examples of such research include ocean -omics research and eDNA, autonomous sampling, fisheries research and management, as well as satellite imagery processing and the automated identification of sea surface features. We invite practitioners from various oceanographic disciplines to submit abstracts highlighting their research on big data and AI at all levels of biological organization (individual, population, ecosystems) and spatio-temporal scales. Given the nascent nature of this field, submissions that focus on methodological innovations are equally welcome to those delving into using AI to address ecological questions.
Co-Sponsor(s):
  • IS - Ocean Observatories, Instrumentation and Sensing Technologies
  • ME - Marine Ecology and Biodiversity
  • PI - Physical-Biological Interactions
Index Terms:

1942 Machine learning [INFORMATICS]
1942 Machine learning [INFORMATICS]
4264 Ocean optics [OCEANOGRAPHY: GENERAL]
4817 Food webs, structure, and dynamics [OCEANOGRAPHY: BIOLOGICAL]
4858 Population dynamics and ecology [OCEANOGRAPHY: BIOLOGICAL]
4894 Instruments, sensors, and techniques [OCEANOGRAPHY: BIOLOGICAL]
Primary Chair:  Moritz S Schmid, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States
Co-chairs:  Eric Coughlin Orenstein, Monterey Bay Aquarium Research Institute, Moss Landing, United States, Christian Briseño-Avena, Oregon State University, Hatfield Marine Science Center, Newport, United States and Emlyn Davies, SINTEF Ocean, Trondheim, Norway
Primary Liaison:  Moritz S Schmid, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States
Moderators:  Moritz S Schmid, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States and Eric Coughlin Orenstein, Monterey Bay Aquarium Research Institute, Moss Landing, United States
Student Paper Review Liaisons:  Moritz S Schmid, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States and Eric Coughlin Orenstein, Monterey Bay Aquarium Research Institute, Moss Landing, United States

Abstracts Submitted to this Session:

OD41A-01
Machine Learning Techniques Applied to Flow Cytometry and Flow Imaging Data to Assess Phytoplankton Community Dynamics in a Florida Coastal Lagoon and Estuary (655907)
Malcolm McFarland1, Dennis Hanisak1, Nicole Stockley1, Stephanie Schreiber1, Rachel Brewton2 and James Michael Sullivan1, (1)Florida Atlantic University, Harbor Branch Oceanographic Institute, Fort Pierce, FL, United States, (2)Florida Atlantic University, Harbor Branch Oceanographic Institute, Fort Pierce, United States
OD41A-02
Towards a Balanced-Labeled-Dataset of Planktons for a Better In-Situ Taxa Identification (647612)
Oda Scheen Kiese, Aya Saad and Annette Stahl Prof, Norwegian University of Science and Technology, Engineering Cybernetics, Trondheim, Norway
OD41A-03
The relationship between water column stratification, pelagic habitat heterogeneity and plankton diversity in a neritic, river-dominated environment (644406)
Christian Briseño-Avena, University of San Diego, Environmental and Ocean Sciences, San Diego, CA, United States, Adam T Greer, The University of Southern Mississippi, Division of Marine Science, Stennis Space Center, MS, United States, Luciano Chiaverano, University of Southern Mississippi, Marine Science, Stennis Space Center, MS, United States, William Monty Graham, University of Southern Mississippi, Marine Science, Stennis Space Center MS, United States and Robert Cowen, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States
OD41A-04
Automated biometric analysis of early fish life stages via instance segmentation using Mask-RCNN (650158)
Emlyn Davies1, Bjarne Kvæstad2 and Bjørn Henrik Hansen2, (1)SINTEF Ocean, Norway, (2)SINTEF Ocean, Trondheim, Norway
OD41A-05
Applying computer vision and machine learning tools to improve the diagnostic accuracy of in-situ and digital image-based assessments of coral health and disease (648879)
John Burns, University of Hawaii at Hilo, Department of Marine Science, Computer Science, Hilo, United States, Grady Weyenberg, University of Hawaii at Hilo, Natural Sciences Division, Hilo, HI, United States and Travis Mandel, University of Hawaii at Hilo, Computer Science, Hilo, HI, United States
OD41A-06
Automated Observation and Prediction of Fine-Scale Spatial Distributions of Benthic Fauna (657539)
Nader Boutros1, Jacquomo Monk2, Oscar Pizarro1,3, Stefan B Williams4 and Neville Barrett2, (1)University of Sydney, Australian Centre for Field Robotics, Sydney, NSW, Australia, (2)University of Tasmania, Institute for Marine and Antarctic Studies, Hobart, TAS, Australia, (3)ACFR, University Of Sydney, Australia, (4)The University of Sydney, Australian Centre for Field Robotics, Sydney, NSW, Australia
OD41A-07
Moving Towards Machine Learning for the Analysis of Deep-Sea Imagery Collected by Autonomous Underwater Vehicle. (655914)
Abigail Powell, Lynker - Under Contract to NOAA Northwest Fisheries Science Center, Seattle, WA, United States, M. Elizabeth Clarke, NOAA NWFSC, Seattle, WA, United States, Matthew David Dawkins, Kitware, Saratoga Springs, NY, United States, Benjamin Richards, NOAA, Honolulu, HI, United States and Anthony Hoogs, Kitware, Clifton Park, United States
OD41A-08
Image-Based Mapping and Semantic Segmentation for Depiction of Coral Reef Community Structure (646340)
Matan Yuval1, Iñigo Alonso2, Gal Eyal3, Dan Tchernov1, Ana C. Murillo2, Yossi Loya4 and Tali Treibitz5, (1)University of Haifa, Haifa, Israel, (2)University of Zaragoza, Zaragoza, Spain, (3)University of Queensland, Brisbane, QLD, Australia, (4)Tel Aviv University, Tel Aviv, Israel, (5)University of Haifa, Charney School Of Marine Sciences, Department of Marine Technologies, Haifa, Israel
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