[MARMAM] Ocean Sciences Session 28080: Machine learning in biologial oceanography (acoustic DCL and Visual ID of marine mammals welcome)

Ludwig H. ludwig.houegnigan at gmail.com
Fri Aug 11 09:00:34 PDT 2017

Dear Colleagues,

The 2018 Ocean Sciences Meeting <http://osm.agu.org/2018/> will take
place 11-16
February 2018 in Portland, Oregon.  The meeting is an important venue for
scientific exchange across broad marine science disciplines, with sessions
on all aspects of oceanography.

We kindly invite you to submit an abstract
<https://agu.confex.com/agu/os18/is/papers/index.cgi?sessionid=28080> to
the Ocean Sciences 2018 Session entitled *Machine learning in biologial

A description of the session can be found here
<https://agu.confex.com/agu/os18/preliminaryview.cgi/Session28080>and at
the end of this message.

Dedicated to the applications of machine learning in biological
oceanography the session will be a great opportunity to discuss and
contrast the use of machine learning techniques in the wider realm of
biological oceanography with its particular use for detection,
classification and localization of marine mammals sounds, but also for
visual detection, classification, recognition and identification.

Please consider submitting and attending the meeting.  Abstracts are due by 6
September 2017.

with our best regards,
*Eric Coughlin Orenstein* (Primary Chair), University of California - San
Diego, Scripps Institution of Oceanography, San Diego, CA, USA
*Jessica Luo* (Co-chair), National Center for Atmospheric Research, USA*John
Burns* (Co-chair), University of Hawai'i, Hawai'i Institute of Marine
Biology, Papaikou, HI, USA*Ludwig Houegnigan *(Co-chair), Polytechnic
University of Catalonia, Department of Signal Theory and Communications,
Barcelona, Spain


Session ID: 28080
Session Title: Machine learning in biologial oceanography
Topic Area: Ocean Data Management

Session Description:
Recent technological advances in instrumentation and computing have allowed
scientists across all disciplines to collect an unprecedented amount of
data. Biological oceanographers in particular are now faced with vast
datasets that stymie traditional analysis methods. Scientists are
increasingly leveraging machine learning (ML) techniques to process and
analyze these information rich datasets. ML algorithms are designed to
learn from one dataset to make accurate predictions about a new,
independent one. While specific application domains might be quite
different, the ML approaches used for analysis are often very similar. This
session therefore aims to (1) identify new ML methods or applications, (2)
examine overlap in disciplines applying similar ML techniques, (3)
facilitate discussion and interdisciplinary collaborations among ML
practitioners in the ocean science community, and (4) identify gaps and
specific needs for oceanographers using ML. The session chairs welcome any
submission detailing work on ML methods for ecological data analysis and
inference in aquatic systems. The session is intended to have a broad scope
and we invite abstracts from diverse fields such as imaging, acoustics,
genomics, and modeling.
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