Experiments with Learning Techniques for Spatial Model Enrichment and Line Generalization

The nature of map generalization may be non-uniform along the length of an individual line, requiring the application of methods that adapt to the local geometry and the geographical context. Geographical databases need to be enriched in terms of shape description structures (geometrical knowledge), knowledge of appropriate order of operations and of appropriate algorithms (procedural knowledge). Stored knowledge should take account of semantic and morphological characteristics, and of cartographic constraints. This paper proposes and discusses three experiments on knowledge acquisition using unsupervised and supervised learning techniques. In order to exploit geometrical shape knowledge, classifications were computed according to a set of morphological measures using unsupervised learning. Choice of appropriate operations was determined by the results of a test with IGN cartographers considering line characteristics. These results were given to a supervised learning algorithm, along...

• Type: Journal
• Year: 1998
• Where: GEOINFORMATICA
• Authors: Corinne Plazanet, Nara Martini Bigolin, Anne Ruas
• Keywords: Algorithm, GEOINFORMATICA 1998, Geometrical Knowledge, Geometrical Shape Knowledge