In practice, the exchange of geodata often fails due to a lack of interoperability between municipalities. It is not enough to simply describe data fields; technical parameters such as reference systems also require comprehensive documentation. Previous attempts to solve this problem using rigid mapping tables have reached their limits, as they map dependencies only in a relational manner and require an enormous amount of maintenance. Smaller municipalities, in particular, are often deterred by this complexity.
This is where the SpaGAT project came in: Instead of relying on laborious individual linkages, the solution reduces complexity by focusing on core content aspects through a common semantic vocabulary (ontology). This user-friendly approach simplifies data preparation and ensures seamless distribution across all levels of government. The result of the project is a platform that consolidates, enriches, and links geodata in semantic formats—such as RDF or GeoJSON-LD—with Linked Open Data.
Using the practical example of “tree-felling permits,” the requirements were identified from both legal and organizational perspectives. This revealed significant hurdles regarding legal requirements and their operational implementation. In response, the SpaGAT infrastructure enables users to efficiently transform their datasets. The result is a self-explanatory dataset that remains usable in both the traditional geospatial data environment and in semantic systems. This facilitates cross-system queries and paves the way for modern AI applications, as agent models can derive content and schema directly from the semantics.
At the closing meeting on April 30, 2026, representatives from municipalities, companies, and the federal and state-level GDI discussed these results. While the advantages of semantic management were beyond question, the effort required for data preparation was identified as a critical factor. In addition, the governance question remains open: who will take the lead in defining a uniform ontology in the future? A promising outlook: The rise of AI models is seen as a valuable tool for partially automating the creation and maintenance of these standards in the future.
