Automated processing for map generalization using web services
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In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing in order to speed up the search for optimized generalization operator sequences.
KeywordsMap generalization Data enrichment Cartographic constraints Combinatorial optimization Parallel processing Web services Service oriented architecture
This research was partially funded by the Swiss National Science Foundation (grant 20-101798, project DEGEN). Thanks go to Ingo Petzold, Stefan Steiniger and three anonymous reviewers for helpful comments.
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