GENERALIZATION OF SEMANTICALLY ENHANCED 3D CITY MODELS

With the rapid advances in sensor – especially laser scanner – technology and the development of increasingly more sophisticated algorithms for the extraction of features from the data sets produced by those sensors, very detailed digital models are going to be produced for a large number of urban areas. In order to make these models available for different applications, concepts for the generalization of these models have to be developed to reduce the size and semantic complexity of the models to a degree that can be handled by the application without losing information that is relevant for the task at hand. Postulating a stricter separation and modularization of the processes of feature extraction and generalization, we present a workflow for the generalization of semantically enhanced models with a hierarchical structure and describe how such models can be used to integrate different algorithms for the generalization of special constellations of features. 1.1 Motivation 1