Energy co-simulation can be used to analyze the dynamic energy consumption of a building or a region, which is essential for decision making in the planning and management of smart cities. To increase the accessibility of energy simulation results, a dynamic online 3D city model visualization framework based on 3D Tiles is proposed in this paper. Two types of styling methods are studied, attribute-based and ID map-based. We first perform the energy co-simulation and save the results in CityGML format with EnergyADE. Then the 3D geometry data of these city objects are combined with its simulation results as attributes or just with object ID information to generate Batched 3D Models (B3DM) in 3D Tiles. Next, styling strategies are pre-defined and can be selected by end-users to show different scenarios. Finally, during the visualization process, dynamic interactions and data sources are integrated into the styling generation to support real-time visualization. This framework is implemented with Cesium. Compared with existing dynamic online 3D visualization framework such as directly styling or Cesium Language (CZML), a JSON format for describing a time-dynamic graphical scene, primarily for display in a web browser running Cesium, the proposed framework is more flexible and has higher performance in both data transmission and rendering which is essential for real-time GIS applications.

This work discusses the integration of multi-camera video moving objects (MCVO) and GIS. This integration was motivated by the characteristics of multi-camera videos distributed in the urban environment, namely, large data volume, sparse distribution and complex spatial–temporal correlation of MCVO, thereby resulting in low efficiency of manual browsing and retrieval of videos. To address the aforementioned drawbacks, on the basis of multi-camera video moving object extraction, this paper first analyzed the characteristics of different video-GIS Information fusion methods and investigated the integrated data organization of MCVO by constructing a spatial–temporal pipeline among different cameras. Then, the conceptual integration model of MCVO and GIS was proposed on the basis of spatial mapping, and the GIS-MCVO prototype system was constructed in this study. Finally, this study analyzed the applications and potential benefits of the GIS-MCVO system, including a GIS-based user interface on video moving object expression in the virtual geographic scene, video compression storage, blind zone trajectory deduction, retrieval of MCVO, and video synopsis. Examples have shown that the integration of MCVO and GIS can improve the efficiency of expressing video information, achieve the compression of video data, rapidly assisting the user in browsing video objects from multiple cameras.