Next Article in Journal
Tree-Based and Optimum Cut-Based Origin-Destination Flow Clustering
Previous Article in Journal
Automated Road Curb Break Lines Extraction from Mobile LiDAR Point Clouds
Open AccessArticle

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

1
School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China
2
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; https://doi.org/10.3390/ijgi8110478
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop