A Parallel-Optimized Visualization Method for Large-Scale Multiple Video-Augmented Geographic Scenes on Cesium

Surveillance video has emerged as a crucial data source for web Geographic Information Systems (GIS), playing a vital role in traffic management, facility monitoring, and anti-terrorism inspections. However, previous methods encountered significant challenges in achieving effective large-scale multi-video overlapping visualization and efficiency, particularly when organizing and visualizing large-scale video-augmented geographic scenes. Therefore, we propose a parallel-optimized visualization method specifically for large-scale multi-video augmented geographic scenes on Cesium. Firstly, our method employs an improved octree-based model for the unified management of large-scale overlapping videos. Then, we introduce a novel scheduling algorithm based on Cesium, which leverages a Web Graphics Library (WebGL) parallel-optimized and dynamic Level-of-Detail (LOD) strategy. This algorithm is designed to enhance the visualization effects and efficiency of large-scale video-integrated geographic scenes. Finally, we perform comparative experiments to demonstrate that our proposed method significantly optimizes the visualization of video overlapping areas and achieves a rendering efficiency increase of up to 95%. Our method can provide a solid technical foundation for large-scale surveillance video scene management and multi-video joint monitoring.