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Article

Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development

1
Center for Research Computing, University of Notre Dame, Notre Dame, IN 46556, USA
2
Rubenstein School of Environment and Natural Resources, Gund Institute for Environment, University of Vermont, Burlington, VT 05401, USA
3
School of Information, University of Michigan, Ann Arbor, MI 48109, USA
4
UAS Project Office, U.S. Geological Survey, Denver, CO 80225, USA
5
NASA Ames Research Center, Moffett Field, CA 94035-0001, USA
6
UNAVCO, Boulder, CO 80301, USA
7
Agricultural Research Service National Agricultural, Library United States Department of Agriculture, Beltsville, MD 20705, USA
8
Mineral Resources, Commonwealth Scientific and Industrial Research Organisation, Kensington, WA 6151, Australia
9
Esri Washington DC Regional Office, Vienna, VA 22182, USA
10
Earth Research Institute, University of California, Santa Barbara, CA 93106, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2019, 11(15), 1797; https://doi.org/10.3390/rs11151797
Received: 20 May 2019 / Revised: 20 June 2019 / Accepted: 10 July 2019 / Published: 31 July 2019
(This article belongs to the Special Issue Trends in UAV Remote Sensing Applications)
The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost. View Full-Text
Keywords: sUAS; drone; RPAS; UAV; data; management; FAIR; community; standards; practices sUAS; drone; RPAS; UAV; data; management; FAIR; community; standards; practices
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MDPI and ACS Style

Wyngaard, J.; Barbieri, L.; Thomer, A.; Adams, J.; Sullivan, D.; Crosby, C.; Parr, C.; Klump, J.; Raj Shrestha, S.; Bell, T. Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development. Remote Sens. 2019, 11, 1797. https://doi.org/10.3390/rs11151797

AMA Style

Wyngaard J, Barbieri L, Thomer A, Adams J, Sullivan D, Crosby C, Parr C, Klump J, Raj Shrestha S, Bell T. Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development. Remote Sensing. 2019; 11(15):1797. https://doi.org/10.3390/rs11151797

Chicago/Turabian Style

Wyngaard, Jane, Lindsay Barbieri, Andrea Thomer, Josip Adams, Don Sullivan, Christopher Crosby, Cynthia Parr, Jens Klump, Sudhir Raj Shrestha, and Tom Bell. 2019. "Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development" Remote Sensing 11, no. 15: 1797. https://doi.org/10.3390/rs11151797

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