Journal of Geo-information Science, Volume. 22, Issue 6, 1383(2020)
Figures & Tables(14)
Fig. 1. Framework for AR spatial analysis based on edge-cloud integration
Fig. 2. Logical connection of cooperative computing between AR and the cloud server
Fig. 4. Dataset for the experimental building of AR spatial analysis framework based on edge-cloud integration
Fig. 5. Flowchart of AR spatial analysis framework based on edge-cloud integration
Fig. 8. Remote sensing images and 3D models generated by CityEngine
Table 1. Differences between AR data and cloud data
View tableView in ArticleTable 1. Differences between AR data and cloud data
AR端数据 云端数据 坐标系 AR本地坐标 地理坐标 属性信息 无 有 空间信息 方向关系 方向关系、距离关系、拓扑关系
Table 2. Development environment of AR for experiments
View tableView in ArticleTable 2. Development environment of AR for experiments
类型 详细信息 硬件环境 处理器：Intel Xeon E5-1630 v4 内存：64 GB 储存容量：3 TB 显卡：Nvidia GeForce GTX 1080 Ti显存：GDDR4 11GB 软件环境 操作系统：Windows 10 专业版 64位 City Engine 2016.0 Visual Studio 2017 Community Unity 2017.2.1f1 (64-bit)、MixedRealityToolkit
Table 3. Development environment of cloud for experiments
View tableView in ArticleTable 3. Development environment of cloud for experiments
类型 详细信息 硬件环境 处理器：Intel Xeon E5-1630 v4 内存：64 GB 储存容量：3 TB 软件环境 操作系统：Windows 10 专业版 64位 Python 3.6、Flask 0.12.2 MySQL 5.7、Redis 3.0 Qt 5.10
Table 4. Accuracy of SVF calculation based on the edge-cloud integration framework
View tableView in ArticleTable 4. Accuracy of SVF calculation based on the edge-cloud integration framework
R 2MSE MAE SVF计算结果 0.998 0.00002 0.00293
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Yongpan BIE, Qingfeng GUAN, Yao YAO.
Paper Information
Received: Aug. 27, 2019
Accepted: --
Published Online: Nov. 12, 2020
The Author Email: GUAN Qingfeng (guanqf@cug.edu.cn)
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