SPACE PARTITIONING FOR PRIVACY ENABLED 3D CITY MODELS

Abstract. Due to recent technological progress, data capturing and processing of highly detailed (3D) data has become extensive. And despite all prospects of potential uses, data that includes personal living spaces and public buildings can also be considered as a serious intrusion into people’s privacy and a threat to security. It becomes especially critical if data is visible by the general public. Thus, a compromise is needed between open access to data and privacy requirements which can be very different for each application. As privacy is a complex and versatile topic, the focus of this work particularly lies on the visualization of 3D urban data sets. For the purpose of privacy enabled visualizations of 3D city models, we propose to partition the (living) spaces into privacy regions, each featuring its own level of anonymity. Within each region, the depicted 2D and 3D geometry and imagery is anonymized with cartographic generalization techniques. The underlying spatial partitioning is realized as a 2D map generated as a straight skeleton of the open space between buildings. The resulting privacy cells are then merged according to the privacy requirements associated with each building to form larger regions, their borderlines smoothed, and transition zones established between privacy regions to have a harmonious visual appearance. It is exemplarily demonstrated how the proposed method generates privacy enabled 3D city models.