OD34D:
New Information Systems Tools for Implementing Autonomous Multisource, Multipoint Observing Systems II Posters
OD34D:
New Information Systems Tools for Implementing Autonomous Multisource, Multipoint Observing Systems II Posters
New Information Systems Tools for Implementing Autonomous Multisource, Multipoint Observing Systems II Posters
Session ID#: 84492
Session Description:
An exciting array of new and emerging space-borne and in-situ observing technologies are coming to the ocean science community, including, inter alia, autonomous surface and underwater vehicles, ocean observing CubeSats, and next-generation large satellite missions. The true power behind the proliferation of these new platforms and tools for remote and in-situ ocean measurements lies in integrating them into observing systems that can predict and then act to observe transient or temporary phenomena, and that are flexible and autonomous in their control and data acquisition and processing. This session aims to: 1. Identify new methods and techniques for autonomously processing communicating, and acting on information from observing systems with different instruments at different vantage points, e.g., combinations of traditional and small-satellite observations, UAVs/AUVs, moored instrumentation, etc. 2. Facilitate cross-disciplinary discussion and collaboration between practitioners and technologists in the ocean science community, and 3. Identify the gaps and needs in the ocean science community for advanced information systems technologies to advance flexible, autonomous observing systems. The session chairs welcome any submission related to technology development or needs in areas such as machine learning and other advanced processing techniques, real- or near real-time processing and telemetry, goal- or prediction-directed autonomy, dynamic inter-calibration, evaluation/comparison of alternative observing strategies, network communications etc. Data science abstracts (e.g., machine learning, artificial intelligence, computational techniques) are welcome, but should contribute to implementing autonomous multi-source, multi-point observing systems rather than post-collection data analysis or fusion.
Co-Sponsor(s):
- ME - Marine Ecology and Biodiversity
- OB - Ocean Biology and Biogeochemistry
- PS - Physical Oceanography: Mesoscale and Smaller
Index Terms:
1940 Machine-to-machine communication [INFORMATICS]
1942 Machine learning [INFORMATICS]
1968 Scientific reasoning/inference [INFORMATICS]
1972 Sensor web [INFORMATICS]
Primary Chair: Ian G Brosnan, NASA Ames Research Center, Earth Science Division, Moffett Field, CA, United States
Co-chairs: Wu-Jung Lee, Applied Physics Laboratory University of Washington, Acoustics Department, Seattle, WA, United States, Laura Rogers, NASA Langley Research Center, Hampton, VA, United States and Robert Heitsenrether, NOAA Chesapeake, Chesapeake, VA, United States
Primary Liaison: Ian G Brosnan, NASA Ames Research Center, Earth Science Division, Moffett Field, CA, United States
Moderators: Robert Heitsenrether, NOAA Chesapeake, Chesapeake, VA, United States and Sarah Webster, University of Washington, Applied Physics Laboratory, WA, United States
Student Paper Review Liaison: Wu-Jung Lee, Applied Physics Laboratory University of Washington, Acoustics Department, Seattle, WA, United States
Abstracts Submitted to this Session:
See more of: Physical Oceanography: Mesoscale and Smaller