Automating the generalisation process, a major issue for national mapping agencies, is extremely complex. Several works have proposed to deal with this complexity using a trial and error strategy. The performance of systems based on such a strategy is directly dependent on the quality
of the control knowledge (i.e. heuristics) used to guide the trials. Unfortunately, most of the time, the definition and updation of knowledge is a fastidious task. In this context, automatic knowledge revision can not only improve the performance of the generalisation, but also allow it to
automatically adapt to various usages and evolve when new elements are introduced. In this article, an offline knowledge revision approach is proposed, based on a logging of the system and on the analysis of outcoming logs. This approach is dedicated to the revision of control knowledge expressed
by production rules. We have implemented and tested this approach for the automated generalisation of groups of buildings within a generalisation model called AGENT, from initial data that reference a scale of approximately 1:15,000 compared with the target map's scale of 1:50,000. The results
show that our approach improves the quality of the control knowledge and thus the performance of the system. Moreover, the approach proposed is generic and can be applied to other systems based on a trial and error strategy, dedicated to generalisation or not.
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trial and error strategy
Document Type: Research Article
Institut de la Francophonie pour l'Informatique (IFI), Modélisation Simulation Informatique (MSI),Unité Mixte Internationale (UMI), Ha Noi, Vietnam
Institut Géographique National (IGN), Conception Objet et Généralisation de l'Information Topographique (COGIT), Saint-Mandé, France
Publication date: 2011-12-01
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