Abstract
As one road network is often the backbone in a geo-spatial dataset, capturing and/or updating the road networks using remote sensing imagery play an important role in traffic management, urban planning, vehicle navigation, and emergency management. Along with the progress of remote sensing launching technologies and the successful applications of deep learning in the field of computer vision, it has become more and more efficient and economical to employ deep learning methods for road-features extractions from very high-resolution (VHR) remote sensing imagery. Meanwhile, as one of the most significant and popular volunteer geographic information data sources, the OpenStreetMap (OSM) including the complete road networks in the wide world has been accumulated in the past decades. In this article, a generic and automatic approach for extracting road networks from VHR remote sensing images has been proposed based on fully convolutional neural network, in which the road centerlines from OSM have been employed to construct the labels for the model training and validation. In the conducted experiments on various VHR image datasets with two different spatial resolutions of 0.3 and 1 m, the proposed model has demonstrated quite satisfactory results – the overall completeness and correctness of the roads extraction from VHR remote sensing images exceed 94.0% and 98.0%, respectively.
Highlights
A LONG with the rapid development of remote sensing technology and the successful launching of various satellite sensors with high resolutions, the extraction of road features from remote sensing images has become more and more significant in the areas of automated map making, autonomous driving, change detection for automatic geo-spatial dataset updating, etc.Manuscript received January 7, 2021; revised January 21, 2021 and January 29, 2021; accepted January 31, 2021
The experimental results showed that over 90.0% of road features have been correctly extracted, regardless that the final road network is wider than what is annotated by ground truth
This research is dedicated to an automatic approach for road extraction from very high-resolution (VHR) remote sensing imagery
Summary
A LONG with the rapid development of remote sensing technology and the successful launching of various satellite sensors with high resolutions, the extraction of road features from remote sensing images has become more and more significant in the areas of automated map making, autonomous driving, change detection for automatic geo-spatial dataset updating, etc.Manuscript received January 7, 2021; revised January 21, 2021 and January 29, 2021; accepted January 31, 2021. A LONG with the rapid development of remote sensing technology and the successful launching of various satellite sensors with high resolutions, the extraction of road features from remote sensing images has become more and more significant in the areas of automated map making, autonomous driving, change detection for automatic geo-spatial dataset updating, etc. As the manual processes to extract the road features from remote sensing images can be tedious, costly, and time consuming, it has been widely discussed to find an efficient way for automatic road-network extraction in the past decades [6]–[8]. A large number of approaches for automatic or semi-automatic road-network extraction from remote sensing images have been widely investigated and scrutinized with various algorithms and methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
