A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins

Q1 Computer Science

Virtual Reality Intelligent Hardware Pub Date : 2023-06-01 DOI:10.1016/j.vrih.2022.08.018

Jinxing Hu , Zhihan Lv , Diping Yuan , Bing He , Wenjiang Chen , Xiongfei Ye , Donghao Li , Ge Yang

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Abstract

This work emphasizes the current research status of the urban Digital Twins to establish an intelligent spatiotemporal framework. A Geospatial Artificial Intelligent (GeoAI) system is developed based on the Geographic Information System and Artificial Intelligence. It integrates multi-video technology and Virtual City in urban Digital Twins. Besides, an improved small object detection model is proposed: YOLOv5-Pyramid, and Siamese network video tracking models, namely MPSiam and FSSiamese, are established. Finally, an experimental platform is built to verify the georeferencing correction scheme of video images. The experimental results show that the Multiply-Accumulate value of MPSiam is 0.5B, and that of ResNet50-Siam is 4.5B. Besides, the model is compressed by 4.8 times. The inference speed has increased by 3.3 times, reaching 83 Frames Per Second. 3% of the Average Expectation Overlap is lost. Therefore, the urban Digital Twins-oriented GeoAI framework established here has excellent performance for video georeferencing and target detection problems.

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城市数字孪生的时空智能框架与实验平台

本工作强调了城市数字孪生的研究现状，以建立一个智能时空框架。在地理信息系统和人工智能的基础上，开发了一个地理空间人工智能系统。它集成了多视频技术和城市数字双胞胎中的虚拟城市。此外，提出了一种改进的小目标检测模型：YOLOv5 Pyramid，并建立了暹罗网络视频跟踪模型MPSiam和FSDiames。最后，搭建了一个实验平台，对视频图像的地理参考校正方案进行了验证。实验结果表明，MPSiam的乘法累加值为0.5B，ResNet50Siam的加法累加值为4.5B，并且模型被压缩了4.8倍。推理速度提高了3.3倍，达到每秒83帧。失去了平均期望重叠的3%。因此，本文建立的面向城市数字孪生的GeoAI框架在视频地理参考和目标检测问题上具有良好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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