Award Abstract # 1838236
BIGDATA: IA: Collaborative Research: Intelligent Solutions for Navigating Big Data from the Arctic and Antarctic

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: UNIVERSITY OF KANSAS CENTER FOR RESEARCH INC
Initial Amendment Date: August 30, 2018
Latest Amendment Date: June 25, 2021
Award Number: 1838236
Award Instrument: Standard Grant
Program Manager: Sylvia Spengler
sspengle@nsf.gov
 (703)292-7347
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2018
End Date: August 31, 2024 (Estimated)
Total Intended Award Amount: $373,338.00
Total Awarded Amount to Date: $381,338.00
Funds Obligated to Date: FY 2018 = $373,338.00
FY 2021 = $8,000.00
History of Investigator:
  • John Paden (Principal Investigator)
    paden@ku.edu
Recipient Sponsored Research Office: University of Kansas Center for Research Inc
2385 IRVING HILL RD
LAWRENCE
KS  US  66045-7563
(785)864-3441
Sponsor Congressional District: 01
Primary Place of Performance: University of Kansas Center for Research Inc
KS  US  66045-7568
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): SSUJB3GSH8A5
Parent UEI: SSUJB3GSH8A5
NSF Program(s): IIS Special Projects,
Big Data Science &Engineering
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9150, 8083, 062Z, 9251
Program Element Code(s): 748400, 808300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The objective of this research is to investigate artificial intelligence (AI) solutions for data collected by the Center for Remote Sensing of Ice Sheets (CReSIS) in order to provide an intelligent data understanding to automatically mine and analyze the heterogeneous dataset collected by CReSIS. Significant resources have been and will be spent in collecting and storing large and heterogeneous datasets from expensive Arctic and Antarctic fieldwork (e.g. through NSF Big Idea: Navigating the New Arctic). While traditional analyses provide some insight, the complexity, scale, and multidisciplinary nature of the data necessitate advanced intelligent solutions. This project will allow domain scientists to automatically answer questions about the properties of the data, including ice thickness, ice surface, ice bottom, internal layers, ice thickness prediction, and bedrock visualization. The planned approach will advance the broader big data research community by improving the efficiency of deep learning methods and in the investigation of methods to merge data-driven AI approaches with application-specific domain knowledge. Special attention will be given to women and minority involvement in the research and the project will develop new course materials for several classes in AI at a Hispanic and minority serving institute.

In polar radar sounder imagery, the delineation of the ice top and ice bottom and layering within the ice is essential for monitoring and modeling the growth of ice sheets and sea ice. The optimal approach to this problem should merge the radar sounder data with physical ice models and related datasets such as ice coverage and concentration maps, spatiotemporal meteorological maps, and ice velocity. Rather than directly engineering specific relations into the image analysis that require many parameters to be defined and tuned, data-dependent approaches let the machine learn these relationships. To devise intelligent solutions for navigating the big data from the Arctic and Antarctic and to scale up the current and traditional techniques to big data, this project plans several approaches for detecting ice surface, bottom, internal layers, 3D modeling of bedrock and spatial-temporal monitoring of the ice surface: 1) Devise new methodologies based on hybrid networks combining machine learning with traditional domain specific knowledge and transforming the entire deep learning network to the time-frequency domain. 2) Equip the machine with information that is not visible to the human eye or that is hard for a human operator to consider simultaneously, to be able to detect internal layers and 3D basal topography on a large scale. Using the results of the feature tracking of the ice surface in radar altimetry, the research effort will also develop new data-dependent techniques for predicting the ice thickness for following years based on deep recurrent neural networks.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Li, Jilu and Rodriguez-Morales, Fernando and Fettweis, Xavier and Ibikunle, Oluwanisola and Leuschen, Carl and Paden, John and Gomez-Garcia, Daniel and Arnold, Emily "Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S and C band airborne ultra-wideband FMCW (frequency-modulated continuous wave) radar" The Cryosphere , v.17 , 2023 https://doi.org/10.5194/tc-17-175-2023 Citation Details
Ibikunle, Oluwanisola and Varshney, Debvrat and Li, Jilu and Rahnemoonfar, Maryam and Paden, John "ECHOVIT: Vision Transformers Using Fast-And-Slow Time Embeddings" IEEE International Geoscience and Remote Sensing Symposium , 2023 https://doi.org/10.1109/IGARSS52108.2023.10281822 Citation Details
Ibikunle, Oluwanisola and Talasila, Hara Madhav and Varshney, Debvrat and Paden, John D. and Li, Jilu and Rahnemoonfar, Maryam "Snow Radar Echogram Layer Tracker: Deep Neural Networks for radar data from NASA Operation IceBridge" 2023 IEEE Radar Conference (RadarConf23) , 2023 https://doi.org/10.1109/RadarConf2351548.2023.10149734 Citation Details

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