Abstract. Urban digital twins (UDTs) play a pivotal role in advancing smart city development, enabling virtual representations of complex urban environments and supporting advanced planning, monitoring, and decision-making. Despite significant progress, challenges remain in intuitively visualizing dynamic, heterogeneous data, managing real-time updates, and representing temporal occupancy in a comprehensible way. This research introduces a visual metaphor, “Colorful Smoke”, for temporal space occupancy visualization within voxel-based UDTs. The approach first generates a voxel-based urban digital twin by fusing CityGML, Digital Terrain Model (DTM), and point cloud data. Elements assumed to be static at the time of observation are represented by voxels, whereas dynamic objects, such as vehicles and pedestrians, are represented using the ‘Colorful Smoke’ metaphor, in which smoke density encodes occupied space, transparency indicates the probability of occupancy, and color represents the type of dynamic object. In contrast to existing voxel-based methods, this design enables users to intuitively perceive temporal occupancy, assess data reliability, and identify areas of change in urban spaces without visual overload. Future work will enhance the metaphor’s clarity through additional colors and adjustment of rendering parameters, and evaluate its perceptual effectiveness via user studies and continuous dynamic updates. The proposed “Colorful Smoke” approach offers a scalable and intuitive method for representing temporal occupancy in UDTs, bridging the gap between complex data and human perception, and providing a foundation for future dynamic urban visual analytics.
Shkedova et al. (Tue,) studied this question.