Jinxing Hu , Zhihan Lv , Diping Yuan , Bing He , Wenjiang Chen , Xiongfei Ye , Donghao Li , Ge Yang
{"title":"城市数字孪生的时空智能框架与实验平台","authors":"Jinxing Hu , Zhihan Lv , Diping Yuan , Bing He , Wenjiang Chen , Xiongfei Ye , Donghao Li , Ge Yang","doi":"10.1016/j.vrih.2022.08.018","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":"5 3","pages":"Pages 213-231"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins\",\"authors\":\"Jinxing Hu , Zhihan Lv , Diping Yuan , Bing He , Wenjiang Chen , Xiongfei Ye , Donghao Li , Ge Yang\",\"doi\":\"10.1016/j.vrih.2022.08.018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":33538,\"journal\":{\"name\":\"Virtual Reality Intelligent Hardware\",\"volume\":\"5 3\",\"pages\":\"Pages 213-231\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Virtual Reality Intelligent Hardware\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2096579622000936\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Virtual Reality Intelligent Hardware","FirstCategoryId":"1093","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096579622000936","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins
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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