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Open AccessArticle

Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline

School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China
Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(11), 478;
Received: 5 September 2019 / Revised: 11 October 2019 / Accepted: 22 October 2019 / Published: 24 October 2019
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. View Full-Text
Keywords: VHR images; road extraction; weakly supervised learning; OpenStreetMap VHR images; road extraction; weakly supervised learning; OpenStreetMap
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Wu, S.; Du, C.; Chen, H.; Xu, Y.; Guo, N.; Jing, N. Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline. ISPRS Int. J. Geo-Inf. 2019, 8, 478.

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