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

Authors :
Jens Klump
Andrea K. Thomer
Don V. Sullivan
Tom W. Bell
Josip Adams
Jane Wyngaard
Lindsay Barbieri
Christopher J. Crosby
Cynthia Parr
S. R. Shrestha
Source :
Remote Sensing, Vol 11, Iss 15, p 1797 (2019), Remote Sensing; Volume 11; Issue 15; Pages: 1797
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

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 the outcomes of a four-year-long community-engagement-based investigation into what tools, practices, and challenges 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 characterised 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 then 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.

Details

Language :
English
ISSN :
20724292
Volume :
11
Issue :
15
Database :
OpenAIRE
Journal :
Remote Sensing
Accession number :
edsair.doi.dedup.....efd06348253c6656e75aca79bbd28dba