Abstract
BackgroundsThis 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. MethodsBesides, 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. ResultThe 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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