Backgrounds 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 G...Backgrounds 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.Methods 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.Result The MultiplyAccumulate value of MPSiam is 0.5B,and that of ResNet50-Siam is 4.5B.Besides,the model is compressed by 4.8times.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.展开更多
基金Supported by Key R&D Program of the Ministry of Science and Technology (2019YFC0810704)Key R&D Program of Guangdong Province (2019B111102002)Shenzhen Science and Technology Program (KCXFZ202002011007040)。
文摘Backgrounds 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.Methods 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.Result The MultiplyAccumulate value of MPSiam is 0.5B,and that of ResNet50-Siam is 4.5B.Besides,the model is compressed by 4.8times.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.
