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Title	Pvt-Unet: Road Extraction In Remote Sensing Imagery Based On U-Shaped Pyramid Vision Transformer Neural Network
ID_Doc	43783
Authors	Xiong Y.; Li L.; Wang H.; Ma T.; Wang Z.; Yang Y.; Wang S.; Zhang S.
Year	2024
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3647649.3647682
Abstract	Road extraction from remote sensing images has gradually become a prominent research hotspot in the field of autonomous driving and smart city construction. In recent years, with the developments of computing power, deep learning has been widely used in this field and convolution neural networks are usually used to extract roads. However, since the roads in the remote sensing images are easy to be occluded by trees and buildings, the roads extracted by these methods are usually fragmented. In this paper, a U-shaped Neural Network based on Pyramid Vision Transformer (PVT-Unet) is designed. This network combines Transformer's long term learning capability with U-shaped network multi-scale feature extraction capability to predict the roads well. Experimental results show that PVT-Unet outperforms the state-of-the-art methods in all evaluation metrics on the Istanbul City Road Dataset. The source code has been made publicly available at: https://github.com/XYQ1517/PVT-Unet. © 2024 ACM.
Author Keywords	Deep learning; Multiscale feature extraction; Pyramid Vision Transformer; Road extraction

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