A Scalable Approach for Generalization of Land Cover Data.

The paper presents a scalable approach for generalization of large land-cover data sets using partitioning in a spatial database and fast generalization algorithms. In the partitioning step, the data set is split into rectangular overlapping tiles. These are processed independently and then composed into one result. For each tile, semantic and geometric generalization operations are performed to remove features that are too small from the data set. The generalization approach is composed of several steps consisting of topologic cleaning, aggregation, feature partitioning, identification of mixed feature classes to form heterogeneous classes, and simplification of feature outlines. The workflow will be presented with examples for generating CORINE Land Cover (CLC) features from the high resolution German authoritative land-cover data set of the whole area of Germany (DLM-DE). The results will be discussed in detail, including runtimes as well as dependency of the result on the parameter setting.