Where is the terraced house?: on the use of ontologies for recognition of urban concepts in cartographic databases

In GIS datasets, it is rare that building objects are richly attributed. Yet having semantic information (such as tenement, terraced, semi-detached) has real practical application (in visualisation and in analysis). It is often the case that we can infer semantic information simply by visual inspection – based on metric and topological properties for example. This paper explores the application of pattern recognition techniques as a way of automatically extracting information from vector databases and attaching this information to the attributes of a building. Our methodology builds upon the idea of an ontology-driven pattern recognition approach. These ideas are explored through the automatic detection of terraced houses (based on Ordnance Survey MasterMap® vector data). The results appear to demonstrate the feasibility of the approach. In conclusion we discuss the benefits and difficulties encountered, suggest ways to deal with these challenges, and propose short and long term directions for future research.