A Rule-based Approach for the Conflation of Attributed Vector Data

  • Authors:

  • Maria A. Cobb;Miyi J. Chung;Harold Foley, III;Frederick E. Petry;Kevin B. Shaw;H. Vincent Miller

  • Affiliations:

  • Naval Research Laboratory, Stennis Space Center, MS 39529/;Naval Research Laboratory, Stennis Space Center, MS 39529/;Naval Research Laboratory, Stennis Space Center, MS 39529/;Naval Research Laboratory, Stennis Space Center, MS 39529/;Naval Research Laboratory, Stennis Space Center, MS 39529/;Planning Systems, Inc., Stennis Space Center, MS 39529

  • Venue:
  • Geoinformatica
  • Year:
  • 1998

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Abstract

In this paper we present a complete approach for the conflation ofattributed vector digital mapping data such as the Vector Product Format(VPF) datasets produced and disseminated by the National Imagery and MappingAgency (NIMA). While other work in the field of conflation has traditionallyused statistical techniques based on proximity of features, the approachpresented here utilizes all information associated with data, includingattribute information such as feature codes from a standardized set,associated data quality information of varying levels, and topology, as wellas more traditional measures of geometry and proximity. In particular, weaddress the issues associated with the problem of matching features andmaintaining accuracy requirements. A hierarchical rule-based approachaugmented with capabilities for reasoning under uncertainty is presented forfeature matching as well as for the determination of attribute sets andvalues for the resulting merged features. Additionally, an in-depth analysisof horizontal accuracy considerations with respect to point features isgiven. An implementation of the attribute and geometrical matching phaseswithin the scope of an expert system has proven the efficacy of the approachand is discussed within the context of the VPF data.