IN13B:
Recent Advances in Deep Learning and Data Analytics in Earth, Atmospheric, and Planetary Sciences I Posters

Submit an Abstract to this Session



Session ID#: 25606

Session Description:
Machine learning methods have revolutionized many aspects of our daily life and are poised to revolutionize far more. Some areas of Earth and space science have long established histories of use, but most have seen only limited application. As Deep Learning and Deep Belief emerge as potentially benefiting areas such as hydrometerological prediction, they also gain more visibility in other fields (and even TV programs). The incorporation of uncertainty, physics and domain knowledge, and space-time dependence in highly scalable Deep Learning architectures may provide a base to model and learn abstract representations of complex earth science datasets. This session seeks papers in theory, adoptions, practice, anecdotal experiences, use cases, and lessons learned in applying machine learning, deep learning and deep belief in Earth and space science.
Primary Convener:  Jens F Klump, CSIRO Mineral Resources, Perth, Australia
Conveners:  Michael M Little, NASA Headquarters, Earth Science Technology Office, Washington, DC, United States, Youzuo Lin, Los Alamos National Laboratory, Los Alamos, United States and Udit Bhatia, Northeastern University, Boston, MA, United States
Chairs:  Jens F Klump, CSIRO Mineral Resources, Perth, Australia and Michael M Little, NASA Headquarters, Washington, DC, United States
OSPA Liaison:  Udit Bhatia, Northeastern University, Boston, MA, United States

Cross-Listed:
  • A - Atmospheric Sciences
  • EP - Earth and Planetary Surface Processes
  • H - Hydrology
  • S - Seismology
Index Terms:

1914 Data mining [INFORMATICS]
1916 Data and information discovery [INFORMATICS]
1942 Machine learning [INFORMATICS]
1968 Scientific reasoning/inference [INFORMATICS]

Abstracts Submitted to this Session:

Geologic Carbon Sequestration Leakage Detection: A Physics-Guided Machine Learning Approach (236249)
Youzuo Lin1, Dylan R Harp2, Bailian Chen1 and Rajesh Pawar2, (1)Los Alamos National Laboratory, Los Alamos, United States, (2)Los Alamos National Laboratory, Los Alamos, NM, United States
Exploring prediction uncertainty of spatial data in geostatistical and machine learning Approaches (247009)
Jens F Klump, CSIRO Mineral Resources, Perth, Australia and Francky Fouedjio, CSIRO Mineral Resources, Perth, WA, Australia
Oil Spill detection off the eastern coast of India using Sentinel-1 dual polarimeteric SAR imagery (293518)
Shaunak De, Indian Institute of Technology Bombay, Mumbai, India, Avik Bhattacharya, Indian Institute of Technology Bombay, Center of Studies in Resources Engineering, Mumbai, India and Ritesh Gautam, Environmental Defense Fund, New York, United States
Deep learning for the detection of barchan dunes in satellite images (278209)
Amine Mohammed Azzaoui1, Manare Adnani2, Hicham Elbelrhiti3, Brahim El Khalil Chaouki4 and Lhoussaine Masmoudi1, (1)Mohammed V University, Laboratoire d’Electronique et de Traitement du Signal/ Géomatique (LETS/Géomat), Rabat, Morocco, (2)Mohammed V University, Rabat, Morocco, (3)Institut Agronomique et Vétérinaire Hassan II, Département des Sciences Fondamentales et Appliquées, Rabat, Morocco, (4)Ecole Nationale des Sciences Appliquées d'Agadir, Computer science, Agadir, Morocco
Application of Deep Learning to Detect Precursors of Tropical Cyclone (265281)
Daisuke Matsuoka, Japan Agency for Marine-Earth Science and Technology, Yokohama-shi, Kanagawa, Japan, Masuo Nakano, JAMSTEC, Yokohama-City, Japan, Daisuke Sugiyama, JAMSTEC Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan and Seiichi Uchida, Kyushu University, Fukuoka, Japan
Quo vadis: Hydrologic inverse analyses using high-performance computing and a D-Wave quantum annealer (275065)
Daniel O'Malley, Los Alamos National Laboratory, Computational Earth Sciences, Los Alamos, NM, United States and Dr. Velimir monty V Vesselinov, Los Alamos National Laboratory, Los Alamos, United States
End-to-End Trade-space Analysis for Designing Constellation Missions (293535)
Jacqueline LeMoigne1, Philip Dabney2, Veronica Foreman3, Paul Grogan4, Sigfried Hache5, Matthew P Holland6, Steven P. Hughes6, Sreeja Nag7 and Afreen Siddiqi8, (1)NASA, Earth Science Technology Office, Greenbelt, MD, United States, (2)NASA Goddard Space Flight Cent, Greenbelt, MD, United States, (3)Massachusetts Inst of Tech, Cambridge, MA, United States, (4)Stevens Institute of Technology, Hoboken, United States, (5)Stevens Institute of Technology, Union City, NJ, United States, (6)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (7)Bay Area Environmental Research Institute Moffett Field, Moffett Field, United States, (8)Massachusetts Institute of Technology, and Harvard Kennedy School, Cambridge, United States
Tactical Approaches for Trading Science Objectives Against Measurements and Mission Design: Science Traceability Techniques at the Jet Propulsion Laboratory (233864)
Alfred Edward Nash III, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
Towards Finding the Global Minimum of the D-Wave Objective Function for Improved Neural Network Regressions  (252703)
John Edward Dorband, University of Maryland Baltimore County, Computer Science, Baltimore, MD, United States

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