A feature-based approach to conflation of geospatial sources

A Geographic Information System (GIS) populated with disparate data sources has multiple and different representations of the same real-world object. Often, the type of information in these sources is different, and combining them to generate one composite representation has many benefits. The first step in this conflation process is to identify the features in different sources that represent the same real-world entity. The matching process is not simple, since the identified features from different sources do not always match in their location, extent, and description. We present a new approach to matching GIS features from disparate sources. A graph theoretic approach is used to model the geographic context and to determine the matching features from multiple sources. Experiments on implementation of this approach demonstrate its viability.