Applications such as landscape planing, environmental monitoring, and flight and driving simulators have a high demand for realistic landscape models. Quantity, precision and the type of models ask for methods which automate the model generation by evaluation of remote sensing data. The presented modeling system AIDA tackles the demand for efficient representation and high realism by integrating a priori knowledge about the appearance of the objects in the scene to derive object specific constraints for 3D-reconstruction. This requires an image interpretation to assign a meaning to the objects in the scene. For explicit representation of the declarative and procedural knowledge a problem-independent formalism based on semantic nets and rules is used. It provides both a data-driven and model-driven control strategy.