Computer vision is an ever more important means for the navigation of UAVs. Here we propose a landmark recognition system looking for salient man-made infrastructure. An object-oriented structural system is preferred since it can utilize known properties of these objects such as part-of hierarchies, mutual geometric constraints of parts, generalization etc. The structure, available for use as landmark, will vary strongly with the region the UAV is supposed to navigate in. Clear object-oriented coding of the knowledge on the landmarks, their constraints, and their properties is a key to swift adaption. This contribution reports on an example: Adapting a system, designed for a central European country (Germany), for use in a more Eastern region (Turkey)

Computer vision is an ever more important means for the navigation of UAVs. Here we propose a landmark recognition system looking for salient man-made infrastructure. An object-oriented structural system is preferred since it can utilize known properties of these objects such as part-of hierarchies, mutual geo\'02 metric constraints of parts, generalization etc. The structure, available for use as landmark, will vary strongly with the region the UAV is supposed to navigate in. The structural knowledge can lose its meaning in two ways: 1) If the area contains a lot of non-intended structure fulfilling the demands modeled the system will start hallucinating lots of landmarks anywhere. 2) If the landmarks in the area do not fulfill the demands modeled they will not be detected. Up to a certain degree these semantics - or lack of meaning - can be investigated mathematically using probabilistic models. But the results from this are very optimistic. In reality the meaning breaks down much earlier. This contribution reports on an example: Testing a system, designed for a central European country (Germany), for use elsewhere (e.g., , Russia or Turkey)