SEMINAR

Department of Computer Science and Statistics
University of Southern Mississippi

Conflation is typically regarded as the combination of information from two digital maps to produce a third map which is "better" than either of its component sources. The history of map conflation goes back to the early to mid-1980's. While traditional work in the field of conflation has used statistical techniques based on proximity of features, the approach presented here utilizes all information associated with the map features, including: attribute information such as feature codes from a standardized set, associated data quality information of varying levels, and topology, as well as more traditional measures of geometry and proximity. In particular, we address the issues associated with the conflation of the attributed vector digital mapping datasets produced and disseminated by the National Imagery and Mapping Agency (NIMA) that conform to the Vector Product Format (VPF) standard.

Special consideration is given to the issues associated with the problem of matching features and maintaining accuracy requirements. A hierarchical rule-based approach augmented with capabilities for reasoning under uncertainty is presented for feature matching as well as for the determination of attribute sets and values for the resulting merged features. Additionally, an analysis of horizontal accuracy considerations with respect to point features is given. An implementation of the attribute and geometrical matching phases within the scope of an expert system has proven the efficacy of the approach.

WHERE: TEC 101

WHEN(day): Wednesday, October 8, 1997

WHEN(time): 12:00 NOON

EVERYBODY IS INVITED