FEATURE RECOGNITION AND CLUSTERING FOR URBAN MODELLING Exploration and analysis in GIS and CAD

In urban planning exploration and analysis assist the generation, measurement, interpretation and management of the modelled urban environments. This frequently involves categorisation of model elements and identification of element types. Such designation of elements can be achieved through attribution (e.g. 'tagging' or 'layering') or direct selection by model users. However, for large, complex models the number and arrangement of elements makes these approaches impractical in terms of time/effort and accuracy. This is particularly true of models which include substantial numbers of elements representing existing urban fabric, rather than only newly generated elements (which might be automatically attributed during the generation process). We present methods for identification and categorisation of model elements in models of existing and proposed urban agglomerations. We also suggest how these methods can enable exploration of models, discovery of identities and relationships not otherwise obvious, and acquisition of insights to the models' structure and contents which are not captured, and may even be obscured, by manual selection or automated pre-attribution.