| 引用本文: | 刘泽邦,陈荦,马梦宇,等.面向大规模地理矢量线数据的多层级实时可视化技术.[J].国防科技大学学报,2023,45(5):173-183.[点击复制] |
| LIU Zebang,CHEN Luo,MA Mengyu,et al.Multilevel real-time visualization technology for large-scale geographic vector linestring data[J].Journal of National University of Defense Technology,2023,45(5):173-183[点击复制] |
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| 面向大规模地理矢量线数据的多层级实时可视化技术 |
| 刘泽邦,陈荦,马梦宇,杨岸然,钟志农,景宁 |
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(国防科技大学 电子科学学院, 湖南 长沙 410073)
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| 摘要: |
| 针对主流方法难以满足多层级实时可视化的需求,提出面向大规模地理矢量线数据的多层级实时可视化技术。建立面向多层级瓦片绘制的自适应可视化模型,设计像元四叉R(pixel quad R, PQR)树空间索引和基于PQR树的自适应可视化算法,分别用于支撑模型的数据组织和可视绘制。在10亿规模数据集上的实验表明:该技术在0.57 s内可计算任一层级上的可视结果,并且计算耗时大幅小于主流方法。当数据规模急剧增长时,该技术在各显示层级上仍具有较好的可视性能,最低可视速率超过100张/s,大幅优于主流方法。该技术在单机条件下即可支撑大规模地理矢量线数据的多层级实时可视化,在空间大数据探索分析领域具备较好的应用前景。 |
| 关键词: 大数据 地理矢量线数据 空间索引 多层级 实时可视化 |
| DOI：10.11887/j.cn.202305020 |
| 投稿日期：2023-03-20 |
| 基金项目:国家自然科学基金资助项目(42101432,41971362)；湖南省自然科学基金资助项目(2022JJ40546) |
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| Multilevel real-time visualization technology for large-scale geographic vector linestring data |
| LIU Zebang, CHEN Luo, MA Mengyu, YANG Anran, ZHONG Zhinong, JING Ning |
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(College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Aiming at the difficulty of mainstream methods to support the multilevel real-time visualization of large-scale geographic vector linestring data, a multilevel real-time visualization technique for large-scale geographic vector linestring data was proposed. An adaptive visualization model for multilevel tile rendering was established, and a PQR (pixel-quad-R) tree spatial index and an adaptive visualization algorithm based on PQR-tree were designed to support the data organization and visualization of the model, respectively. Experiments on billion-scale datasets show that the technique can calculate visualization results at any zoom level within 0.57 s. Meanwhile, its visualization time is significantly less than mainstream methods. When the data scale increases sharply, the technology still has good visualization performance at each zoom level, and the lowest visualization rate exceeds 100 tiles/s, which is much better than mainstream methods. The technique can support multilevel real-time visualization of large-scale geographic vector linestring data in the single machine, and has a good application prospect in the field of exploration and analysis of spatial big data. |
| Keywords: big data geographic vector linestring data spatial index multilevel real-time visualization |
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