{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T11:15:31Z","timestamp":1785496531711,"version":"3.56.0"},"reference-count":47,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T00:00:00Z","timestamp":1624665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The pair segmentation images are not easy to prepare. Thus, to alleviate the burden of acquiring large quantities of training images, this study designed an improved generative adversarial network to extract road networks through a weakly supervised process named WSGAN. The proposed method is divided into two steps: generating the mapping image and post-processing the binary image. During the generation of the mapping image, unlike other road extraction methods, this method overcomes the limitations of manually annotated segmentation images and uses mapping images that can be easily obtained from public data sets. The residual network block and Wasserstein generative adversarial network with gradient penalty loss were used in the mapping network to improve the retention of high-frequency information. In the binary image post-processing, this study used the dilation and erosion method to remove salt-and-pepper noise and obtain more accurate results. By comparing the generated road network results, the Intersection over Union scores reached 0.84, the detection accuracy of this method reached 97.83%, the precision reached 92.00%, and the recall rate reached 91.67%. The experiments used a public dataset from Google Earth screenshots. Benefiting from the powerful prediction ability of GAN, the experiments show that the proposed method performs well at extracting road networks from remote sensing images, even if the roads are covered by the shadows of buildings or trees.<\/jats:p>","DOI":"10.3390\/rs13132506","type":"journal-article","created":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T23:57:22Z","timestamp":1624838242000},"page":"2506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["WSGAN: An Improved Generative Adversarial Network for Remote Sensing Image Road Network Extraction by Weakly Supervised Processing"],"prefix":"10.3390","volume":"13","author":[{"given":"Anna","family":"Hu","sequence":"first","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqiong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1304-6353","authenticated-orcid":false,"given":"Liang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinjun","family":"Qiu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-4915","authenticated-orcid":false,"given":"Yongyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2021.02.014","article-title":"Building outline delineation: From aerial images to polygons with an improved end-to-end learning framework","volume":"175","author":"Zhao","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1111\/tgis.12514","article-title":"Multilane roads extracted from the OpenStreetMap urban road network using random forests","volume":"23","author":"Xu","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.trc.2018.05.004","article-title":"Development of road grade data using the United States geological survey digital elevation model","volume":"92","author":"Liu","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"314","DOI":"10.31035\/cg2020041","article-title":"Main active faults and seismic activity along the Yangtze River Economic Belt: Based on remote sensing geological survey","volume":"3","author":"Wu","year":"2020","journal-title":"China Geol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"263","DOI":"10.2193\/0022-541X(2006)70[263:COCARS]2.0.CO;2","article-title":"Comparison of Camera and Road Survey Estimates for White-Tailed Deer","volume":"70","author":"Roberts","year":"2006","journal-title":"J. Wildl. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.oraloncology.2012.01.021","article-title":"Saliva: A potential media for disease diagnostics and monitoring","volume":"48","author":"Liu","year":"2012","journal-title":"Oral Oncol."},{"key":"ref_7","first-page":"188","article-title":"Remote sensing monitoring of winter wheat powdery mildew based on wavelet analysis and support vector machine","volume":"33","author":"Huang","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3390\/rs9020127","article-title":"Soybean Disease Monitoring with Leaf Reflectance","volume":"9","author":"Sreekala","year":"2017","journal-title":"Remote Sens."},{"key":"ref_9","first-page":"134","article-title":"Road traffic accident data collection and analysis for road safety research","volume":"22","author":"Guo","year":"2005","journal-title":"Proc. Infants"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.aap.2013.04.035","article-title":"Multilevel analysis in road safety research","volume":"60","author":"Dupont","year":"2013","journal-title":"Accid. Anal. Prev."},{"key":"ref_11","first-page":"39","article-title":"Finite element method research on road slide stability analysis","volume":"24","author":"Zhu","year":"2007","journal-title":"J. Highw. Transp. Res. Dev."},{"key":"ref_12","first-page":"269","article-title":"Epidemiological research and analysis on the impaired person in road traffic accident in Chengdu area","volume":"23","author":"Huang","year":"2007","journal-title":"Fa Yi Xue Za Zhi"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/S0924-2716(99)00018-0","article-title":"Suitability of laser data for DTM generation: A case study in the context of road planning and design","volume":"54","author":"Pereira","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1080\/13658816.2020.1800016","article-title":"Measuring the similarity between multipolygons using convex hulls and position graphs","volume":"35","author":"Xu","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1142\/S0218127410027568","article-title":"Road Planning with Slime Mould: If Physarum Built Motorways it Would Route M6\/M74 Through Newcastle","volume":"20","author":"Adamatzky","year":"2010","journal-title":"Int. J. Bifurc. Chaos"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1061\/(ASCE)0733-947X(1990)116:3(261)","article-title":"Automation and Robotics for Road Construction and Maintenance","volume":"116","author":"Skibniewski","year":"1990","journal-title":"J. Transp. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.trd.2014.03.006","article-title":"Environmental assessment of road construction and maintenance policies using LCA","volume":"29","author":"Jullien","year":"2014","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2013Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","first-page":"788","article-title":"Spectral\u2013Spatial Classification and Shape Features for Urban Road Centerline Extraction","volume":"11","author":"Shi","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TIP.2006.887731","article-title":"Accurate Centerline Detection and Line Width Estimation of Thick Lines Using the Radon Transform","volume":"16","author":"Zhang","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TGRS.2016.2514602","article-title":"Road Network Extraction via Aperiodic Directional Structure Measurement","volume":"54","author":"Zang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","first-page":"106","article-title":"Automatic road extraction based on multi-scale modeling, context, and snakes","volume":"32","author":"Mayer","year":"1997","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.14358\/PERS.70.12.1365","article-title":"Road Extraction Using SVM and Image Segmentation","volume":"70","author":"Song","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/36.662728","article-title":"Detection of linear features in SAR images: Application to road network extraction","volume":"36","author":"Tupin","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wegner, J.D., Montoya-Zegarra, J.A., and Schindler, K. (2021, June 23). A Higher-Order CRF Model for Road Network Extraction. Available online: https:\/\/ethz.ch\/content\/dam\/ethz\/special-interest\/baug\/igp\/photogrammetry-remote-sensing-dam\/documents\/pdf\/cvpr2013_1227_cr.pdf.","DOI":"10.1109\/CVPR.2013.222"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_29","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P.H.S. (2021, June 23). Conditional Random Fields as Recurrent Neural Networks. Available online: https:\/\/www.robots.ox.ac.uk\/~szheng\/papers\/CRFasRNN.pdf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wan, J., Xie, Z., Xu, Y., Chen, S., and Qiu, Q. (2021). DA-RoadNet: A Dual-Attention Network for Road Extraction from High Resolution Satellite Imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.","DOI":"10.1109\/JSTARS.2021.3083055"},{"key":"ref_31","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2021, June 23). Generative Adversarial Nets. Available online: https:\/\/papers.nips.cc\/paper\/2014\/hash\/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html."},{"key":"ref_32","unstructured":"Rezaei, M., Harmuth, K., Gierke, W., Kellermeier, T., Fischer, M., Yang, H., and Meinel, C. (2021, June 23). A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor. Available online: https:\/\/arxiv.org\/abs\/1708.05227."},{"key":"ref_33","unstructured":"Pan, X., Zhao, J., and Xu, J. (2020). Conditional Generative Adversarial Network-Based Training Sample Set Improvement Model for the Semantic Segmentation of High-Resolution Remote Sensing Images. IEEE Trans. Geosci. Remote Sens., 1\u201317."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, A., Xie, Z., Xu, Y., Xie, M., Wu, L., and Qiu, Q. (2020). Unsupervised Haze Removal for High-Resolution Optical Remote-Sensing Images Based on Improved Generative Adversarial Networks. Remote Sens., 12.","DOI":"10.3390\/rs12244162"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, M., Li, Y., Zhong, J., Zhang, Y., and Liu, X. (October, January 26). Edge Prediction Net for Reconstructing Road Labels Contaminated by Clouds. Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa Village, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323273"},{"key":"ref_36","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017). Improved training of wasserstein gans. Advances in neural information processing systems. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (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_38","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1109\/TPAMI.2002.1114852","article-title":"Efficient dilation, erosion, opening, and closing algorithms","volume":"24","author":"Gil","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.neucom.2021.02.010","article-title":"Convolutional neural network with median layers for denoising salt-and-pepper contaminations","volume":"442","author":"Liang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, C., and Wand, M. (2016, January 8\u201316). Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks. Proceedings of the European Conference on Computer Vision, Cham, Switzerland.","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1109\/TIT.2014.2320500","article-title":"R\u00e9nyi Divergence and Kullback-Leibler Divergence","volume":"60","author":"Harremos","year":"2014","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"041905","DOI":"10.1103\/PhysRevE.65.041905","article-title":"Analysis of symbolic sequences using the Jensen-Shannon divergence","volume":"65","author":"Grosse","year":"2002","journal-title":"Phys. Rev. E"},{"key":"ref_44","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein gan. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A.A. (2016, January 27\u201330). Context Encoders: Feature Learning by Inpainting. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073659","article-title":"Globally and locally consistent image completion","volume":"36","author":"Iizuka","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., and Huang, T.S. (2018, January 18\u201323). Generative Image Inpainting with Contextual Attention. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00577"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2506\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:50Z","timestamp":1760163890000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,26]]},"references-count":47,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132506"],"URL":"https:\/\/doi.org\/10.3390\/rs13132506","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,26]]}}}