Experiments with Learning Techniques for Spatial Model Enrichment and Line Generalization

TL;DR: This paper proposes and discusses three experiments on knowledge acquisition using unsupervised and supervised learning techniques and results are promising with a prediction rate higher than 80% having been obtained.

Abstract: 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 with corresponding computed measures in order to discover rules. The approach and the resulting rules are presented and discussed. Tests have also been conducted on the tuning of parameter values, applying a Gaussian smoothing tolerance value to a set of lines using the supervised learning algorithm. The values obtained by means of the learning algorithm have been compared with interactive choices of an expert. Results are promising with a prediction rate higher than 80% having been obtained.