Cartographic generalization of roads in a local and adaptive approach: A knowledge acquistion problem
This paper presents a local and adaptive approach to road generalization, where different algorithms may be successively applied to each part of a road. The specific problem addressed is how to acquire and formalize cartographic knowledge in order to guide the application of the algorithms during the process. Our approach requires toolboxes of algorithms to transform and analyse the data, as well as an engine to chain them together. First, we present the toolboxes used in our experiments for road generalization. Then, we present two different engines, as well as the knowledge-acquisition processes used to determine them. The first engine, named GALBE, is an empirically determined process, where the application of algorithms is mainly based on a single criterion: the coalescence. The second engine, which is more complex, uses multiple measures to describe the road. The choice of which algorithm to use given a particular set of measures is determined from examples using supervised learning techniques. Results obtained with both engines are presented.
cartographic generalization knowledge acquistion line generalization machine learning