Automatic Iterative Generalization for Land Cover Data

The problem of generalizing spatial data is studied in the context of land cover data. The theory behind automatic generalization is reviewed and a raster based deterministic iterative generalization method is proposed. The methodology is based on the Map Algebra. The multivariate statistical testing methods are extended to deal also with generalized land cover data. The case study is related to the production of a European CORINE Land Cover map from Finland. It presents an implementation of a new methodological concept for land cover data production, supervised classification and automatic generalization.In the case study, the existing supervised classification is supported with digital maps and attribute databases. All input data is combined to a detailed land cover with a very small minimum feature size, and automatically generalized to the standard European Land Cover. According to the quality tests performed, the automatically generalized method used here meets the present quality specifications. The method is fast, and it gives an opportunity for multiple data products in various scales, optimized for different purposes.