Abstract
Road networks play a significant role in modern city management. It is necessary to continually extract current road structure, as it changes rapidly with the development of the city. Due to the success of semantic segmentation based on deep learning in the application of computer vision, extracting road networks from VHR (Very High Resolution) imagery becomes a method of updating geographic databases. The major shortcoming of deep learning methods for road networks extraction is that they need a massive amount of high quality pixel-wise training datasets, which is hard to obtain. Meanwhile, a large amount of different types of VGI (volunteer geographic information) data including road centerline has been accumulated in the past few decades. However, most road centerlines in VGI data lack precise width information and, therefore, cannot be directly applied to conventional supervised deep learning models. In this paper, we propose a novel weakly supervised method to extract road networks from VHR images using only the OSM (OpenStreetMap) road centerline as training data instead of high quality pixel-wise road width label. Large amounts of paired Google Earth images and OSM data are used to validate the approach. The results show that the proposed method can extract road networks from the VHR images both accurately and effectively without using pixel-wise road training data.
Highlights
IntroductionWith the rapid development of remote sensing technology, images obtained from the remote sensors (installed on drones or satellites) have made a considerable contribution to disaster/emergency management, urban planning, and object detection [1,2,3]
With the rapid development of remote sensing technology, images obtained from the remote sensors have made a considerable contribution to disaster/emergency management, urban planning, and object detection [1,2,3]
The regularized semi-supervised loss for weakly road extraction used in this paper is presented; the MD-ResUNet is described in detail for road extraction
Summary
With the rapid development of remote sensing technology, images obtained from the remote sensors (installed on drones or satellites) have made a considerable contribution to disaster/emergency management, urban planning, and object detection [1,2,3]. Deep learning techniques are widely used in different kinds of applications, and many road extraction methods based on deep learning are proposed. Most deep learning based end-to-end road extraction approaches need a large amount of high quality pixel-wise annotated datasets. Because the full annotation dataset is expensive to obtain and the scribble annotation is easy to generate, the study of road networks extraction using scribble labels is of great importance. Regularized Semi-Supervised Loss To extract pixel-wise roads from VHR images, we use the deep learning methods which are proved to be effective in these applications. We use a high order regularized loss (normalized cut loss [43]) to reflect the similarity between these pixels, which can reflect the feature of the pixels labeled unknown in the road extraction 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 callDisclaimer: 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.
