IN12C:
Recent Advances in Deep Learning and Data Analytics in Earth, Atmospheric, and Planetary Sciences III: Machine Learning, Quantum Computing, and Big Data Platforms

Submit an Abstract to this Session



Session ID#: 33674

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 Earth Science Resource Engineering, 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:  Udit Bhatia, Northeastern University, Boston, MA, United States and Michael M Little, NASA Headquarters, Washington, DC, United States
OSPA Liaison:  Youzuo Lin, Los Alamos National Laboratory, Los Alamos, 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:

Evaluation of Deep Learning Models for Predicting CO2 Flux (234892)
Phuong Nguyen, David Frankel and Milton Halem, University of Maryland Baltimore County, Computer Science, Baltimore, MD, United States
Polar Domain Discovery with Sparkler (300425)
Siri Jodha S Khalsa, University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, Ruth Duerr, Ronin Institute for Independent Scholarship, Westminster, CO, United States, Chris A Mattmann, NASA Jet Propulsion Laboratory, Instrument and Data Systems Section, Pasadena, CA, United States, Nithin Krishna Ottilingam, University of Southern California, Los Angeles, CA, United States, Karanjeet Singh, NASA Jet Propulsion Laboratory, Pasadena, CA, United States and Luis Alberto Lopez, National Snow and Ice Data Center (NSIDC), CIRES, University of Colorado Boulder, Boulder, United States
A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization (242176)
Bailian Chen1, Dylan R Harp2, Youzuo Lin3, Elizabeth H Keating4 and Rajesh Pawar3, (1)Los Alamos National Laboratory, Earth and Environmental Sciences Division, Los Alamos, NM, United States, (2)The Freshwater Trust, Portland, United States, (3)Los Alamos National Laboratory, Los Alamos, United States, (4)Los Alamos National Laboratory, Los Alamos, NM, United States
Quantum Assisted Learning for Registration of MODIS Images (288100)
Craig Pelissier, Science Systems and Applications, Inc., Lanham, United States, Jacqueline Le Moigne, NASA Goddard Institute for Space Studies, Greenbelt, MD, United States, Gyorgy Fekete, Computer Science Corporation, Lanham, MD, United States and Milton Halem, University of Maryland Baltimore County, Computer Science, Baltimore, MD, United States
Multiplatform Mission Planning and Operations Simulation Environment for Adaptive Remote Sensors (Invited) (273811)
Graeme Smith, Ohio State University Main Campus, Department of Electrical and Computer Enginneering, Columbus, OH, United States, Christopher Ball, The Ohio State University, ElectroScience Laboratory, Columbus, United States, Andrew O'Brien, Ohio State University Main Campus, Electrical & Computer Engineering, Columbus, OH, United States and Joel T Johnson, Ohio State University, Department of Electrical and Computer Engineering, Columbus, United States
Humans as Sensors: Assessing the Information Value of Qualitative Farmer’s Crop Condition Surveys for Crop Yield Monitoring and Forecasting (274337)
Santiago Beguería, Consejo Superior de Investigaciones Científicas (CSIC), Estación Experimental de Aula Dei, Zaragoza, Spain
Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning (226152)
John Malone Beck1, Susan Bridges1, Charles Collins1, John Rushing2 and Sara J Graves2, (1)University of Alabama in Huntsville, Huntsville, AL, United States, (2)University of Alabama in Huntsville, Huntsville, United States

See more of: Earth and Space Science Informatics