Urban models can be collected area covering and efficiently based on aerial data like stereo images or LIDAR. While a large number of applications like simulations are already feasible based on the available three-dimensional building representations, a further quality improvement is required for some other tasks. Especially, if very realistic visualisations from pedestrian viewpoints have to be generated, the quality and amount of detail, which is available for urban models generated from aerial data has to be improved. As an example, due to the viewpoint restrictions of airborne platforms, image texture for the facades of the buildings frequently is not available from airborne sensors. Since this information is crucial to improve the visual appearance of urban models for pedestrian perspectives, alternative data sources have to be applied. Thus, image texture for the faces of existing building models is frequently generated based on manual mapping of terrestrial images. For this purpose, correspondences between the existing model and the terrestrial image have to be provided. As it will be discussed within the paper, the efficiency of the manual mapping process can be increased considerably by the application of panoramic scenes. If these panoramic scenes are collected from a high-level system based on a rotating CCD line scanner, large areas are covered at high resolution and superb image quality. Thus, the processing of a single scene is sufficient to extract texture for a considerable number of buildings. Texture mapping can additionally be sped up if the required correspondences between image and model are provided from automatic tools. If multiple images are collected, these tools should also allow for an automatic selection of the image, which is most suitable for texture mapping of a specific part of the building. While the main emphasis of the paper is on the refinement of existing urban models based on terrestrial images, the final part of the paper will briefly discuss the benefits of integrating existing building models to the processing of data from terrestrial LIDAR.