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 technolo...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-06-01
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| Series: | Virtual Reality & Intelligent Hardware |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2096579622000936 |
| _version_ | 1827923374686339072 |
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| author | Jinxing Hu Zhihan Lv Diping Yuan Bing He Wenjiang Chen Xiongfei Ye Donghao Li Ge Yang |
| author_facet | Jinxing Hu Zhihan Lv Diping Yuan Bing He Wenjiang Chen Xiongfei Ye Donghao Li Ge Yang |
| author_sort | Jinxing Hu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T04:55:50Z |
| format | Article |
| id | doaj.art-b06bc5da91754b4e980ac21ff1542d8a |
| institution | Directory Open Access Journal |
| issn | 2096-5796 |
| language | English |
| last_indexed | 2024-03-13T04:55:50Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Virtual Reality & Intelligent Hardware |
| spelling | doaj.art-b06bc5da91754b4e980ac21ff1542d8a2023-06-18T05:01:31ZengKeAi Communications Co., Ltd.Virtual Reality & Intelligent Hardware2096-57962023-06-0153213231A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital TwinsJinxing Hu0Zhihan Lv1Diping Yuan2Bing He3Wenjiang Chen4Xiongfei Ye5Donghao Li6Ge Yang7Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaDepartment of Game Design, Faculty of Arts, Uppsala University, Sweden; Corresponding author.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen Urban Public Safety and Technology Institute Co.Ltd, Shenzhen 518172, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen Urban Public Safety and Technology Institute Co.Ltd, Shenzhen 518172, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen Urban Public Safety and Technology Institute Co.Ltd, Shenzhen 518172, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaThis 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.http://www.sciencedirect.com/science/article/pii/S2096579622000936Spatiotemporal Intelligenceurban Digital TwinsGeographic Information SystemArtificial IntelligenceSmall Target Detection |
| spellingShingle | Jinxing Hu Zhihan Lv Diping Yuan Bing He Wenjiang Chen Xiongfei Ye Donghao Li Ge Yang A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins Virtual Reality & Intelligent Hardware Spatiotemporal Intelligence urban Digital Twins Geographic Information System Artificial Intelligence Small Target Detection |
| title | A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins |
| title_full | A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins |
| title_fullStr | A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins |
| title_full_unstemmed | A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins |
| title_short | A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins |
| title_sort | spatiotemporal intelligent framework and experimental platform for urban digital twins |
| topic | Spatiotemporal Intelligence urban Digital Twins Geographic Information System Artificial Intelligence Small Target Detection |
| url | http://www.sciencedirect.com/science/article/pii/S2096579622000936 |
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