Comparing learning strategies for topographic object classification

Two methods of topographic object classification through shape are described. Unsupervised classification through clustering analysis is compared with supervised classification based on a Bayesian framework. Both are applied to the real world problem of checking and assigning feature-codes in large-scale topographic data for use in computer cartography and Geographical Information Systems (GIS). Categorisation is accompanied by a confidence measure that the classification is correct. Both types of classification were implemented and their outcomes evaluated and compared. As a case study, results and conclusions are presented on the classification and identification of archaeological feature shapes on OS large-scale maps. It was found that the supervised classification model used out-performed the unsupervised classification model to a considerable degree.