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Land cover and land use exhibit strong contextual dependencies. We propose a novel approach for the simultaneous classification ofland cover and land use, where semantic and spatial context is considered. The image sites for land cover and land use classificationform a hierarchy consisting of two layers: a <i>land cover layer</i> and a <i>land use layer</i>. We apply Conditional Random Fields (CRF) atboth layers. The layers differ with respect to the image entities corresponding to the nodes, the employed features and the classes tobe distinguished. In the land cover layer, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects froma geospatial database. Both CRFs model spatial dependencies between neighbouring image sites. The complex semantic relationsbetween land cover and land use are integrated in the classification process by using contextual features. We propose a new iterativeinference procedure for the simultaneous classification of land cover and land use, in which the two classification tasks mutuallyinfluence each other. This helps to improve the classification accuracy for certain classes. The main idea of this approach is thatsemantic context helps to refine the class predictions, which, in turn, leads to more expressive context information. Thus, potentiallywrong decisions can be reversed at later stages. The approach is designed for input data based on aerial images. Experiments arecarried out on a test site to evaluate the performance of the proposed method. We show the effectiveness of the iterative inferenceprocedure and demonstrate that a smaller size of the super-pixels has a positive influence on the classification result

AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE