Das Projekt "Providing Enriched Spatial Data - Ontology-driven Recognition of Urban Structures from Spatial Databases (ORUS)" wird vom Umweltbundesamt gefördert und von Universität Zürich, Geographisches Institut durchgeführt. Most of spatial databases that exist today have been designed to serve multiple purposes and hence concentrate on the 'least common denominator'. These general purpose spatial databases are rich in geometry, yet they are poor in semantics - in particular with regards to the representation of higher order semantic concepts that extend beyond the semantics of individual, discrete objects. While such higher level semantic concepts are not explicitly coded in current cartographic databases, they are nevertheless implicitly contained, owing to the fact that there often exists a relationship between the form (i.e. geometry) and function (i.e. semantics) of real-world phenomena, particularly in the built environment. Hence, it is possible - at least to some extent - to 'enrich' cartographic databases retrospectively, making implicitly contained higher level semantic concepts explicit through cartographic pattern recognition processes. The main goal of our project is therefore to develop automated methods to make this hidden information explicit. There are a number of solutions for the enrichment of cartographic/spatial databases, especially in the domain of automated cartographic generalisation. We argue, however, that the versatility and reusability of these solutions is often rather limited, since they were developed for specific databases and geospatial concepts, and encapsulated in algorithms. In our work, we have aimed to provide a more general approach by formalising the definition of semantic concepts through ontologies, and investigate how these formal definitions can be used to drive cartographic pattern recognition processes in order to enrich spatial databases. We argue that following this approach, enhanced understanding of generated patterns, easier adaptibility for different patterns, and enhanced interoperability can be provided. To this end, following issues have been adressed in our research: 1) Identification and formalisation of relevant urban concepts and their spatial properties. 2) Transformation from ontologies to algorithms that allow their automatic detection in existing spatial databases. 3) Design of intuitive human-computer interaction methods with the pattern recognition system: How can a human operator define concepts and how can he/she explore generated patterns/relations? 4) Evaluation of the enriched database, in order to demonstrate the utility of ontology-enriched databases. Objective 1 has been addressed by extracting knowledge from various sources about urban morphology, urban design, and city guides, and using this knowledge to define ontologies. Concerning objective 2, a methodology and framework for ontology-driven pattern recognition has been developed and published. It builds on a formalisation of the pattern recognition process by relating geographic concepts to cartographic measures and to other geographic concepts. (abridged text)