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As an important application of virtual geographic environments (VGEs), virtual disaster scenes are essential in enhancing the public's risk awareness. However, existing virtual disaster scene visualization methods lack expert guidance and fail to meet the public's requirements, resulting in an ineffective public understanding. Therefore, this paper proposes a knowledge-guided disaster scene 3D visualization framework. First, the public's demand for disaster scene visualization is analyzed, and a geographic knowledge graph of disaster scenes is constructed. Second, through the guidance of the knowledge graph, the virtual disaster scenes are fusion modeled and suitability represented. Third, a diverse organization and adaptive scheduling method of disaster scene data for multi-computing devices is established. Finally, we developed a prototype system for disaster scene visualization, selected a typical disaster, and conducted cognitive experiments with eye-tracking technology. The results show that the proposed method can effectively support the adaptive visualization of virtual disaster scenes for four computing devices and maintain an efficient frame rate. In addition, compared with other disaster scene visualization methods, our framework incorporates semantic knowledge of scene, user, demand, and space. It can effectively convey disaster information and help the public cognize disaster risks and has significant advantages in modeling standardization, personalization, and adaptability. [ABSTRACT FROM AUTHOR]

Efficient geovisualization is beneficial for understanding geospatial phenomena, an important research direction for GISers and Cartographers. However, the current research on geovisualization overemphasizes the visual effects while neglecting the prominent representation of crucial information and failing to consider the user's cognitive workload of information processing. Following the laws of visual perception of the human eyes, this article proposes a visual attention-guided augmented representation approach of geographic scenes that involves area of interest computation, background simplification, and compound graphic variables. Finally, we select bridge stress visualization as a case study for experimental analysis. The experimental results of eye-tracking show that augmented representation could draw the participants' attention to areas of interest in a short time, increasing their duration of fixations and the accuracy of completing given tasks. These findings suggest that our approach can enhance geographic scenes' cognitive efficiency, offers a new idea for the theoretical studies of geovisualization, and holds promising potential for broader application in various geographical phenomena visualization. [ABSTRACT FROM AUTHOR]

Efficient and intuitive representation of floods can improve people's perception, which is useful for flood emergency management and decision making. However, the current methods of visualizing flood disaster scenes have the shortcomings of data redundancy and low-efficiency. The interference of the complex background can be avoided through region of interest (ROI) extraction, because the attention will be quickly attracted by a few salient visual objects. First, the characteristics of the flood disaster scene object are analysed, and the method for scene division and data organization is established. Second, the region of interest is extracted according to the time series data of the flood evolution process simulated using cellular automata, and the dynamic identification model of the objects of interest is established. Then, a dynamic scheduling queue model with service interruption is designed to optimize the rendering efficiency of flood scenes and improve the perception efficiency. Finally, a prototype visualization system was developed and the experimental results show that approximately 30% of the redundant data are reduced, and the scene rendering efficiency is increased by approximately 15%. The non-ROI visualization is weakened by using the rules of human visual cognition, which improves the perception efficiency of flood scenes. [ABSTRACT FROM AUTHOR]

Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene. However, pre-rendering methods in the existing research work are usually used to implement holographic visualization, which is static display. The above-mentioned methods for static display have many shortcomings, such as poor adaptability, low rendering efficiency and lack of real-time. A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction. Firstly, a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes. Secondly, a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics. Finally, a prototype system was developed, and the corresponding experimental analysis was completed. The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction. The amount of scene data can be reduced to more than 30%, which significantly improves rendering efficiency and reduces glare. [ABSTRACT FROM AUTHOR]