Abstract
This paper discusses the application of satellite remote sensing images in urban rail transit safety evaluation. First of all, the article expounds the significance of satellite remote sensing images in the rail transit safety evaluation system, which can provide the overall information of the rail transit network, be used for traffic flow analysis, land use monitoring, etc., and provide a basis for planning decision-making. Then, the article introduces the application of artificial intelligence technology in the segmentation of railway remote sensing images. The deep learning model can realize automatic and efficient image segmentation and improve the segmentation accuracy. Then, the article designs a railway remote sensing image segmentation system based on PaddleSeg, and uses Google Earth remote sensing images for training and testing. Finally, the article analyzes the role of U-Net in the railway segmentation task, and the reflection of different evaluation indicators on the performance of the model. Research shows that artificial intelligence technology can greatly improve the segmentation efficiency and accuracy of railway remote sensing images, and provide strong support for rail transit safety evaluation.
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