Oblique photogrammetry models are indispensable for implementing digital twins of cities. Geographic information system researchers have proposed plenty of methods to load and visualize these city-scaled scenes. However, when the area viewed changes quickly in real-time rendering, current methods still require excessive GPU calculation and memory occupation. In this study, we propose a data organization method in which we merged all quadtrees and used a binary encoding method to encode nodes in a merged tree so that the parent–child relationship between the tree nodes could be calculated using rapid binary operations. After that, we developed a strategy to cancel the loading of redundant nodes based on the parent–child relationship, which helped to reduce the hard disk loading time and the amount of memory occupied in visualization. Moreover, we introduced a parameter to measure the area of the triangle mesh per pixel to achieve unified data scheduling under different production standards. We implemented our method based on Unreal Engine (UE), and three experiments were designed to illustrate the advantages of our methods in index acceleration, frame time, and memory reduction. The results show that our methods can significantly improve visualization fluency and reduce memory usage.

Temporal knowledge graphs aim to enhance the dynamic and evolutionary representation of knowledge while enabling time-based reasoning. However, the reasoning based on temporal knowledge graphs in real geographic environments suffers from low accuracy due to the difficulty in effectively utilizing complex spatio-temporal information. Spatial attributes within entities typically encompass both relative and absolute spatial information types. However, during spatio-temporal reasoning, the deep coupling between the quadruple $(entities, relations, timestamp)$ and these two spatial information types is frequently overlooked, as they remain unintegrated in inference predictions. This paper proposes a novel Multi-Task Spatial Recurrent Evolution Graph Convolutional Network (MTS-RE-GCN) framework to enable temporal knowledge graph methods to better reason about spatial entities under time-varying conditions. Experiments on the spatio-temporal dataset and the benchmark dataset (i.e., ICEWS14s, ICEWS18) with spatio-temporal features demonstrate that MTS-RE-GCN significantly outperforms the baseline models (e.g., RE-GCN, TiRGN). For entity prediction tasks, MTS-RE-GCN achieves mean reciprocal rank (MRR) scores of 0.848, 0.739, 0.566, representing improvements of 9.00%, 6.03%, 3.28%, correspondingly. This provides a comprehensive and efficient solution for spatio-temporal entity prediction in temporal knowledge graphs, holding significant implications for spatio-temporal data analysis, event prediction, and related fields.