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A User-Centric Gamified Crowdsourcing System for Geospatial Analysts

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047619C0079
Agency Tracking Number: NGA-P1-19-20
Amount: $99,954.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA191-007
Solicitation Number: 19.1
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-22
Award End Date (Contract End Date): 2020-06-08
Small Business Information
714 East Monument Avenue Suite 201
Dayton, OH 45402
United States
DUNS: 601628717
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Michael J Mccloskey
 President/Chief Scientist
 (937) 684-3339
Business Contact
 Michael McCloskey
Phone: (937) 684-3339
Research Institution

Creating comprehensive geospatial datasets requires that National Geospatial Agency (NGA) analysts spend large amounts of time searching for, delineating, and labeling non-moving features in overhead imagery. This tiresome and tedious task can negatively impact not only the analysts’ work satisfaction but also the resulting data quality. Fortunately, recent advances in gamification and crowdsourcing provide an opportunity to improve address this situation. 361 Interactive brings together in game design and gamification, intelligence analysis, crowdsourcing, human factors, interface design, and experimental design to design and develop an engaging gamified crowdsourcing system for NGA analysts. Our user-centric approach, which is grounded on deep knowledge of the domain and end users, will ensure that gamification is applied judiciously to enhance motivation and engagement and ensure it is well received by the NGA community. Furthermore, our design will include gamification elements that appeal to different types of analysts to successfully engage more end users. The resulting user-centric gamified crowdsourcing system will enhance NGA analysts’ motivation to contribute to crowdsourcing geospatial datasets, as well as their engagement and enjoyment while they complete these tasks. At least as importantly, the resulting system will result in enhanced-quality geospatial datasets, which will be leveraged to train automated algorithms.

* Information listed above is at the time of submission. *

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