Uncertainty Handling in Geospatial Data

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.