Award Abstract # 1642611
Collaborative Proposal: EarthCube RCN: Connecting the Earth Science and Cyberinfrastructure communities to advance the analysis of high resolution topography data

NSF Org: EAR
Division Of Earth Sciences
Recipient: UNIVERSITY OF TEXAS AT AUSTIN
Initial Amendment Date: August 10, 2017
Latest Amendment Date: June 23, 2022
Award Number: 1642611
Award Instrument: Standard Grant
Program Manager: Eva Zanzerkia
EAR
 Division Of Earth Sciences
GEO
 Directorate for Geosciences
Start Date: August 15, 2017
End Date: July 31, 2023 (Estimated)
Total Intended Award Amount: $102,000.00
Total Awarded Amount to Date: $102,000.00
Funds Obligated to Date: FY 2017 = $102,000.00
History of Investigator:
  • Paola Passalacqua (Principal Investigator)
    paola@austin.utexas.edu
Recipient Sponsored Research Office: University of Texas at Austin
110 INNER CAMPUS DR
AUSTIN
TX  US  78712-1139
(512)471-6424
Sponsor Congressional District: 25
Primary Place of Performance: University of Texas at Austin
101 E. 27th Street, Suite 5.300
Austin
TX  US  78712-1532
Primary Place of Performance
Congressional District:
25
Unique Entity Identifier (UEI): V6AFQPN18437
Parent UEI:
NSF Program(s): EarthCube
Primary Program Source: 01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 807400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.050

ABSTRACT

This EarthCube Research Coordination Network (RCN) will bring together communities that create and use high resolution map data, including those that conduct research on earth surface processes and those that create the technology to make these types of complex data usable. Members of this network will work to share currently available resources and best practices, and to develop new tools to make data more available to researchers. Training will focus on teaching graduate student and early career researchers to access and use high resolution map data to answer earth science research questions.

Vast quantities of High Resolution Terrain (HRT) data have been collected, with applications ranging from scientific research to commercial sector engineering. Full scientific utilization of these HRT data is still limited due to challenges associated with the storage, manipulation, processing, and analysis of these data. The cyberinfrastructure community, including computer vision, computer science, informatics, and related engineering fields are developing advanced tools for visualizing, cataloging, and classifying imagery data including point clouds. Yet, many of these tools are most applicable to engineered structures and small datasets, and not to heterogeneous landscapes. Together the earth science and cyberinfrastructure communities have the opportunity to test and validate emerging tools in challenging landscapes (e.g., heterogeneous and multiscale landforms, vegetation structures, urban footprints). In particular, this RCN will be focused on four themes: (1) coordination of the analysis of HRT data across the earth surface processes and hydrology communities to identify work-flows and best practices for data analysis; (2) identification of cyberinfrastructure tool development needs as new technologies for HRT data acquisition emerge; (3) use of HRT data for numerical models validation and integration of HRT data information in models; (4) training in HRT best practices, and data processing and analysis work-flows.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This EarthCube Research Coordination Network advanced the analysis of high resolution topographic (HRT) data for earth science investigations by leveraging open source tools and techniques developed in the cyberinfrastructure community. A framework to promote collaboration between the earth science and cyberinfrastructure communities is critical to utilizing HRT data to advance the understanding of Earth surface processes. 

 

This project’s outcomes include:

  1. Advanced the analysis of high resolution topography data in collaboration with the engineering and computer science communities in the United States and internationally. Our Steering Committee members and the RCN participants brought advanced techniques for point cloud data analysis and tools for waveform data exploitation;

  2. Coordinated the analysis of existing and new datasets by establishing methods and work-flows. Coordination is needed to integrate understanding across disciplines and to standardize data access, file formats and metadata, and processing and analysis;

  3. Promoted data, tools, methods, and work-flow sharing with the community at large and identified and coordinated the development of new tools and analysis methods to extract the full richness of information from HRT data and their integration with other remotely sensed and field data;

  4. Promoted and fostered educational opportunities across disciplines on the common denominators of high resolution, big data, high information content data, temporal dynamics, and spatial heterogeneity;

  5. Coordinated the HRT data community such that integration with cyberinfrastructure and modeling environments is promoted and facilitated. Access to data and analysis tools promoted the development of new modeling approaches able to integrate the spatial and temporal variability captured in the data.

The project hosted two in-person workshops, serving over 100 participants, instructors, and steering committee members. The community ranged from undergraduate to senior career scientists and engineers. The workshops provided hands-on training, technical presentations, poster sessions and dissemination, and networking opportunities. All materials from the workshops are openly and freely available on the OpenTopography website.


 

 


Last Modified: 10/16/2023
Modified by: Paola Passalacqua

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