A knowledge-guided visualization framework of disaster scenes for helping the public cognize risk information

可视化 计算机科学 场景图 应急管理 个性化 数据科学 人机交互 万维网 人工智能 渲染(计算机图形) 政治学 法学
作者
Jun Zhu,Jinbin Zhang,Qing Zhu,Weilian Li,Jianlin Wu,Yukun Guo
出处
期刊:International Journal of Geographical Information Science [Taylor & Francis]
卷期号:38 (4): 626-653 被引量:18
标识
DOI:10.1080/13658816.2023.2298299
摘要

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.
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