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Proceedings Paper

Uncertainty handling in geospatial data
Author(s): Peter J. Doucette; Dennis J. Motsko; Matthew Sorensen; Devin A. White
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Paper Abstract

The topic of data uncertainty handling is relevant to essentially any scientific activity that involves making measurements of real world phenomena. A rigorous accounting of uncertainty can be crucial to the decision-making process. The purpose of this paper is to provide a brief overview on select issues in handling uncertainty in geospatial data. We begin with photogrammetric concepts of uncertainty handling, followed by investigating uncertainty issues related to processing vector (object) representations of geospatial information. Suggestions are offered for enhanced modeling, visualization, and exploitation of local uncertainty information in applications such as fusion and conflation. Stochastic simulation can provide an effective approach to improve understanding of the consequences uncertainty propagation in common geospatial processes such as path finding. Future work should consider the development of standardized modeling techniques for stochastic simulation for more complex object data, to include spatial and attribute information.

Paper Details

Date Published: 2 May 2012
PDF: 12 pages
Proc. SPIE 8396, Geospatial InfoFusion II, 83960H (2 May 2012); doi: 10.1117/12.918538
Show Author Affiliations
Peter J. Doucette, National Geospatial-Intelligence Agency (United States)
Dennis J. Motsko, National Geospatial-Intelligence Agency (United States)
Matthew Sorensen, National Geospatial-Intelligence Agency (United States)
Devin A. White, National Geospatial-Intelligence Agency (United States)

Published in SPIE Proceedings Vol. 8396:
Geospatial InfoFusion II
Matthew F. Pellechia; Richard J. Sorensen; Shiloh L. Dockstader; Kannappan Palaniappan; Xuan Liu, Editor(s)

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