Abstract

Recognition of urban structures is of interest in cartography and urban modelling.
While a broad range of typologies of urban patterns have been published in the last century, relatively little research on the automated recognition of such structures
exists. This work presents a sample-based approach for the recognition of five types of urban structures: (1) inner city areas, (2) industrial and commercial areas, (3) urban
areas, (4) suburban areas and (5) rural areas. The classification approach is based only on the characterisation of building geometries with morphological measures derived from perceptual principles of Gestalt psychology. Thereby, size, shape and density of buildings are evaluated. After defining the research questions we develop the classification methodology and evaluate the approach with respect to several aspects. The experiments focus on the impact of different classification algorithms,
correlations and contributions of measures, parameterisation of buffer-based indices, and mode filtering. In addition to that, we investigate the influence of scale and regional factors. The results show that the chosen approach is generally successful.
It turns out that scale, algorithm parameterisation, and regional heterogeneity of building structures substantially influence the classification performance.