19 June 2001




M. Cobb    M. Chung    H. Foley
F. Petry    K. Shaw
Naval Research Laboratory
Stennis Space Center, MS 39529-5004
H. V. Miller
Planning Systems Inc.
Stennis Space Center, MS 39529-5004



In this paper we present a complete approach for the conflation of attributed vector digital mapping data such as the Vector Product Format (VPF) datasets produced and disseminated by the National Imagery and Mapping Agency (NIMA). While other work in the field of conflation has traditionally used statistical techniques based on proximity of features, the approach presented here utilizes all information associated with data, 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 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 in-depth 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 and is discussed within the context of the VPF data.

Sponsored by the Office of Naval Research.

Published in the GeoInformatica in March 1998.
Naval Research Laboratory Contribution Number NRL/JA/7441 96-0010.
Journal Article