This paper presents a generic scheme to extract traffic information from both optical satellite imagery and optical airborne image sequences. The extraction is based on an explicit semantic model of traffic, from which, depending on the characteristics of the input data, different strategies for vehicle detection, vehicle queue extraction and motion estimation are derived. The model comprises different scales to exploit the scale-dependent properties of traffic imaged by optical sensors. It is furthermore extended by context information to include knowledge about background objects as well as metadata from a road database in a consistent way. Various tests with different input data have been carried out and compared with manually acquired ground truth data of vehicles and vehicle tracks. The results show clearly the high potential of airborne and spaceborne traffic monitoring, but also indicate room for methodological improvements and exhibit some inherent sensor-related drawbacks.