{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T14:27:08Z","timestamp":1780496828695,"version":"3.54.1"},"reference-count":60,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:00:00Z","timestamp":1571875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61806211"],"award-info":[{"award-number":["61806211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971362"],"award-info":[{"award-number":["41971362"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/ijgi8110478","type":"journal-article","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T04:41:27Z","timestamp":1571978487000},"page":"478","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline"],"prefix":"10.3390","volume":"8","author":[{"given":"Songbing","family":"Wu","sequence":"first","affiliation":[{"name":"School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Du","sequence":"additional","affiliation":[{"name":"School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7880-3394","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingxiao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4759-7521","authenticated-orcid":false,"given":"Ning","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"},{"name":"Department of Computer Science and Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1109\/JSTARS.2008.922318","article-title":"Incorporating Generic and Specific Prior Knowledge in a Multiscale Phase Field Model for Road Extraction From VHR Images","volume":"1","author":"Peng","year":"2008","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1080\/15481603.2016.1250328","article-title":"Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data","volume":"54","author":"Guo","year":"2017","journal-title":"GIscience Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5493","DOI":"10.1080\/01431160500300354","article-title":"The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics","volume":"26","author":"Zhu","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3359","DOI":"10.1109\/TGRS.2013.2272593","article-title":"An integrated method for urban main-road centerline extraction from optical remotely sensed imagery","volume":"52","author":"Shi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1080\/01431161.2015.1054049","article-title":"Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine","volume":"36","author":"Wang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","first-page":"271","article-title":"A review of road extraction from remote sensing images","volume":"3","author":"Wang","year":"2016","journal-title":"J. Traffic Transp. Eng."},{"key":"ref_9","unstructured":"(2019, April 22). OpenStreetMap. Available online: https:\/\/www.openstreetmap.org\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MPRV.2008.80","article-title":"OpenStreetMap: User-Generated Street Maps","volume":"7","author":"Haklay","year":"2008","journal-title":"IEEE Pervasive Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1068\/b35097","article-title":"How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets","volume":"37","author":"Haklay","year":"2010","journal-title":"Environ. Plan. Plan. Des."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1111\/j.1467-9671.2010.01203.x","article-title":"Quality Assessment of the French OpenStreetMap Dataset","volume":"14","author":"Girres","year":"2010","journal-title":"Trans. Gis"},{"key":"ref_13","unstructured":"Pathak, D., Shelhamer, E., Long, J., and Darrell, T. (2014). Fully convolutional multi-class multiple instance learning. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Chen, L.C., Murphy, K., and Yuille, A.L. (2015). Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation. arXiv.","DOI":"10.1109\/ICCV.2015.203"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dai, J., He, K., and Sun, J. (2015). BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. arXiv.","DOI":"10.1109\/ICCV.2015.191"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bearman, A., Russakovsky, O., Ferrari, V., and Fei-Fei, L. (2015). What\u2019s the Point: Semantic Segmentation with Point Supervision. arXiv.","DOI":"10.1007\/978-3-319-46478-7_34"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lin, D., Dai, J., Jia, J., He, K., and Sun, J. (2016, January 27\u201330). Scribblesup: Scribble-supervised convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.344"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, J., Schwing, A.G., and Urtasun, R. (2015, January 7\u201312). Learning to segment under various forms of weak supervision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299002"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khoreva, A., Benenson, R., Hosang, J., Hein, M., and Schiele, B. (2017, January 21\u201326). Simple does it: Weakly supervised instance and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.181"},{"key":"ref_20","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_21","unstructured":"Yang, C., Duraiswami, R., DeMenthon, D., and Davis, L. (2003, January 14\u201317). Mean-shift analysis using quasinewton methods. Proceedings of the 2003 International Conference on Image Processing (Cat. No. 03CH37429), Barcelona, Spain."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/LGRS.2014.2312000","article-title":"A semi-automatic method for road centerline extraction from VHR images","volume":"11","author":"Miao","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1109\/TGRS.2012.2190078","article-title":"Road network detection using probabilistic and graph theoretical methods","volume":"50","author":"Unsalan","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pawar, V., and Zaveri, M. (2014, January 19\u201321). Graph based K-nearest neighbor minutiae clustering for fingerprint recognition. Proceedings of the 2014 10th International Conference on Natural Computation (ICNC), Xiamen, China.","DOI":"10.1109\/ICNC.2014.6975917"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kirthika, A., and Mookambiga, A. (2011, January 3\u20135). Automated road network extraction using artificial neural network. Proceedings of the 2011 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, Tamil Nadu, India.","DOI":"10.1109\/ICRTIT.2011.5972323"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"George, J., Mary, L., and Riyas, K. (2013, January 13\u201315). Vehicle detection and classification from acoustic signal using ANN and KNN. Proceedings of the 2013 International Conference on Control Communication and Computing (ICCC), Thiruvananthapuram, India.","DOI":"10.1109\/ICCC.2013.6731694"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Simler, C. (2011, January 24\u201329). An improved road and building detector on VHR images. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049176"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, D.M., Wen, X., and Ling, C.L. (2011, January 9\u201311). Road extraction based on the algorithms of MRF and hybrid model of SVM and FCM. Proceedings of the 2011 International Symposium on Image and Data Fusion, Tengchong, China.","DOI":"10.1109\/ISIDF.2011.6024291"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2006.09.002","article-title":"Road tracking in aerial images based on human\u2013computer interaction and Bayesian filtering","volume":"61","author":"Zhou","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, J., and Chen, M. (2014, January 26\u201327). On-road multiple obstacles detection in dynamical background. Proceedings of the 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2014.33"},{"key":"ref_31","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, The Pennsylvania State University."},{"key":"ref_32","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Costea, D., Marcu, A., Leordeanu, M., and Slusanschi, E. (2017, January 22\u201329). Creating Roadmaps in Aerial Images with Generative Adversarial Networks and Smoothing-Based Optimization. Proceedings of the IEEE International Conference on Computer Vision Workshop, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.246"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/LGRS.2017.2672734","article-title":"Road structure refined CNN for road extraction in aerial image","volume":"14","author":"Wei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xie, Z., Feng, Y., and Chen, Z. (2018). Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning. Remote Sens., 10.","DOI":"10.3390\/rs10091461"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1080\/09540091.2018.1510902","article-title":"Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold","volume":"31","author":"Wu","year":"2018","journal-title":"Connect. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. (2018, January 18\u201322). DeepGlobe 2018: A Challenge to Parse the Earth Through Satellite Images. Proceedings of the The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Aich, S., van der Kamp, W., and Stavness, I. (2018). Semantic Binary Segmentation using Convolutional Networks without Decoders. arXiv.","DOI":"10.1109\/CVPRW.2018.00032"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Sun, T., Chen, Z., Yang, W., and Wang, Y. (2018, January 18\u201322). Stacked U-Nets With Multi-Output for Road Extraction. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00033"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., and Wu, M. (2018, January 18\u201322). D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sun, T., Di, Z., Che, P., Liu, C., and Wang, Y. (2019, January 16\u201320). Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00769"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., and Schroers, C. (2018, January 18\u201322). Normalized cut loss for weakly-supervised CNN segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00195"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","unstructured":"Tang, M., Marin, D., Ayed, I.B., and Boykov, Y. (2015). Kernel Cuts: MRF meets kernel and spectral clustering. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tang, M., Marin, D., Ayed, I.B., and Boykov, Y. (2016). Normalized cut meets MRF. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46475-6_46"},{"key":"ref_47","unstructured":"Ng, A.Y., Jordan, M.I., and Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","article-title":"A tutorial on spectral clustering","volume":"17","year":"2007","journal-title":"Stat. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1162\/089976603321780317","article-title":"Laplacian eigenmaps for dimensionality reduction and data representation","volume":"15","author":"Belkin","year":"2003","journal-title":"Neural Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1145\/1531326.1531327","article-title":"Gaussian kd-trees for fast high-dimensional filtering","volume":"Volume 28","author":"Adams","year":"2009","journal-title":"ACM Transactions on Graphics (ToG)"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1111\/j.1467-8659.2009.01645.x","article-title":"Fast high-dimensional filtering using the permutohedral lattice","volume":"Volume 29","author":"Adams","year":"2010","journal-title":"Computer Graphics Forum"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Jian, S. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_55","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Taylor, G.W., and Fergus, R. (2011, January 6\u201313). Adaptive deconvolutional networks for mid and high level feature learning. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TGRS.2017.2669341","article-title":"Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network","volume":"55","author":"Cheng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in pytorch. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1109\/TPAMI.2004.1273918","article-title":"Learning to detect natural image boundaries using local brightness, color, and texture cues","volume":"55","author":"Martin","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/11\/478\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:29:05Z","timestamp":1760189345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/11\/478"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,24]]},"references-count":60,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["ijgi8110478"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8110478","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,24]]}}}