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Schema integration of heterogeneous geospatial database

The integration of heterogeneous geospatial data offers possibilities to manually and automatically derive new information, which are not available when using only a single data source. This paper presents a three-level schema integration architecture which consists of local schemas, mapped schemas, and a global schema, for global heterogeneous geospatial systems. we describe a machine-learning based approach for GIS schema matching. Our approach extends existing machine-learning approaches for (traditional) data mapping but departs from them due to the nature of geographic data. Our solution reduces the complex mappings by identifying different values of a determining property.