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Article

Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

1
Department of Information Engineering, China University of Geosciences, Wuhan 430074, China
2
National Engineering Research Center of Geographic Information System, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 20 August 2018 / Revised: 7 September 2018 / Accepted: 11 September 2018 / Published: 13 September 2018
(This article belongs to the Special Issue Pattern Analysis and Recognition in Remote Sensing)
The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods. View Full-Text
Keywords: road network extraction; deep learning; pyramid attention; global attention; high resolution road network extraction; deep learning; pyramid attention; global attention; high resolution
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MDPI and ACS Style

Xu, Y.; Xie, Z.; Feng, Y.; Chen, Z. Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning. Remote Sens. 2018, 10, 1461. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091461

AMA Style

Xu Y, Xie Z, Feng Y, Chen Z. Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning. Remote Sensing. 2018; 10(9):1461. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091461

Chicago/Turabian Style

Xu, Yongyang, Zhong Xie, Yaxing Feng, and Zhanlong Chen. 2018. "Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning" Remote Sensing 10, no. 9: 1461. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10091461

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