{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T21:14:19Z","timestamp":1784236459322,"version":"3.55.0"},"reference-count":84,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,2]],"date-time":"2020-05-02T00:00:00Z","timestamp":1588377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"publisher","award":["The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)"],"award-info":[{"award-number":["The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)"]}],"id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["Researchers Supporting Project number RSP-2019 \/ 14"],"award-info":[{"award-number":["Researchers Supporting Project number RSP-2019 \/ 14"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.<\/jats:p>","DOI":"10.3390\/rs12091444","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T14:00:43Z","timestamp":1588600843000},"page":"1444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":280,"title":["Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1704-4670","authenticated-orcid":false,"given":"Arnick","family":"Abdollahi","sequence":"first","affiliation":[{"name":"The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, University of Technology Sydney, Sydney 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, University of Technology Sydney, Sydney 2007, Australia"},{"name":"Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjingu, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nagesh","family":"Shukla","sequence":"additional","affiliation":[{"name":"The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, University of Technology Sydney, Sydney 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0102-5424","authenticated-orcid":false,"given":"Subrata","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems and Modelling, University of Technology Sydney, Sydney 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Geology &amp; Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/10106049.2014.911967","article-title":"GIS-based sustainable city compactness assessment using integration of MCDM, Bayes theorem and RADAR technology","volume":"30","author":"Abdullahi","year":"2015","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1080\/19475705.2015.1012750","article-title":"Analysis on causes of flash flood in Jeddah city (Kingdom of Saudi Arabia) of 2009 and 2011 using multi-sensor remote sensing data and GIS","volume":"7","author":"Youssef","year":"2016","journal-title":"Geomat. Nat. Hazards"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2011.02.030","article-title":"Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends","volume":"117","author":"Weng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_4","first-page":"30","article-title":"Automatic road feature extraction from high resolution satellite images using LVQ neural networks","volume":"13","author":"Wijesingha","year":"2013","journal-title":"Asian J. Geoinform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"304","DOI":"10.7763\/IJMO.2015.V5.479","article-title":"Road detection from high satellite images using neural networks","volume":"5","author":"Kahraman","year":"2015","journal-title":"Int. J. Modeling Optim."},{"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":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2993","DOI":"10.1109\/TITS.2017.2665658","article-title":"Road recognition from remote sensing imagery using incremental learning","volume":"18","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1016\/j.protcy.2016.05.180","article-title":"Automated road extraction from high resolution satellite images","volume":"24","author":"Hormese","year":"2016","journal-title":"Procedia Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s12524-017-0702-x","article-title":"Investigation of SVM and level set interactive methods for road extraction from google earth images","volume":"46","author":"Abdollahi","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_10","first-page":"117","article-title":"Semi automatic road extraction from digital images","volume":"20","author":"Bakhtiari","year":"2017","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, B., Wu, H., Wang, Y., and Liu, W. (2015). Main road extraction from zy-3 grayscale imagery based on directional mathematical morphology and vgi prior knowledge in urban areas. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0138071"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4853","DOI":"10.1109\/JSTARS.2015.2443552","article-title":"An object-based method for road network extraction in VHR satellite images","volume":"8","author":"Miao","year":"2015","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.isprsjprs.2016.10.010","article-title":"MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images","volume":"122","author":"Grinias","year":"2016","journal-title":"ISPRS J. Photogramm."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1946","DOI":"10.1109\/JSTARS.2015.2449296","article-title":"Road extraction from very high resolution remote sensing optical images based on texture analysis and beamlet transform","volume":"9","author":"Sghaier","year":"2016","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1109\/JSTARS.2012.2219614","article-title":"Road extraction from SAR imagery based on multiscale geometric analysis of detector responses","volume":"5","author":"He","year":"2012","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_16","first-page":"1","article-title":"Road Extraction from High-Resolution SAR Images via Automatic Local Detecting and Human-Guided Global Tracking","volume":"2012","author":"Cheng","year":"2012","journal-title":"Int. J. Antennas Propag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2017.02.008","article-title":"Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images","volume":"126","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1080\/13658816.2017.1341632","article-title":"Quality assessment of building footprint data using a deep autoencoder network","volume":"31","author":"Xu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Audebert, N., Le Saux, B., and Lef\u00e8vre, S. (2017). Segment-before-detect: Vehicle detection and classification through semantic segmentation of aerial images. Remote Sens., 9.","DOI":"10.3390\/rs9040368"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Wang, J., Qin, Q., Gao, Z., Zhao, J., and Ye, X. (2016). A new approach to urban road extraction using high-resolution aerial image. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5070114"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral image classification using deep pixel-pair features","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.biosystemseng.2015.12.003","article-title":"Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV","volume":"146","author":"Senthilnath","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wegner, J.D., Montoya-Zegarra, J.A., and Schindler, K. (2013, January 23\u201328). A higher-order CRF model for road network extraction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.222"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Maurya, R., Gupta, P., and Shukla, A.S. (2011, January 3\u20135). Road extraction using k-means clustering and morphological operations. Proceedings of the 2011 International Conference on Image Information Processing, Shimla, India.","DOI":"10.1109\/ICIIP.2011.6108839"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mattyus, G., Wang, S., Fidler, S., and Urtasun, R. (2015, January 7\u201313). Enhancing road maps by parsing aerial images around the world. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.197"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.patrec.2004.11.005","article-title":"An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery","volume":"26","author":"Mena","year":"2005","journal-title":"Pattern Recognit. Lett."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Panboonyuen, T., Vateekul, P., Jitkajornwanich, K., and Lawawirojwong, S. (2017). An enhanced deep convolutional encoder-decoder network for road segmentation on aerial imagery. International Conference on Computing and Information Technology, Springer.","DOI":"10.1007\/978-3-319-60663-7_18"},{"key":"ref_31","unstructured":"Tang, S., and Yuan, Y. (2015). Object Detection Based on Convolutional Neural Network, Stanford University. Available online: http:\/\/cs231n.stanford.edu\/reports\/2015\/pdfs\/CS231n_final_writeup_sjtang.pdf."},{"key":"ref_32","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/LGRS.2016.2542358","article-title":"Convolutional Neural Network Based Automatic Object Detection on Aerial Images","volume":"13","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TGRS.2016.2616585","article-title":"Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks","volume":"55","author":"Volpi","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2016, January 10\u201315). Fully convolutional neural networks for remote sensing image classification. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730322"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2352\/ISSN.2470-1173.2016.10.ROBVIS-392","article-title":"Multiple object extraction from aerial imagery with convolutional neural networks","volume":"2016","author":"Saito","year":"2016","journal-title":"Electron. Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Cui, W., and Jiang, H. (2016, January 10\u201315). Fully convolutional networks for building and road extraction: Preliminary results. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729406"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mnih, V., and Hinton, G.E. (2010). Learning to Detect Roads in High-Resolution Aerial Images, Springer.","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"ref_41","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Identity mappings in deep residual networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., and The, P.G. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med., 6.","DOI":"10.1371\/journal.pmed.1000097"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"264","DOI":"10.7326\/0003-4819-151-4-200908180-00135","article-title":"Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement","volume":"151","author":"Moher","year":"2009","journal-title":"Ann. Intern. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1080\/07408170600940005","article-title":"Toward modeling and simulation of critical national infrastructure interdependencies","volume":"39","author":"Min","year":"2007","journal-title":"IIE Trans."},{"key":"ref_47","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 Sensing Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.neucom.2018.10.036","article-title":"Multiscale road centerlines extraction from high-resolution aerial imagery","volume":"329","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, P., Zang, Y., Wang, C., Li, J., Cheng, M., Luo, L., and Yu, Y. (2016, January 10\u201315). Road network extraction via deep learning and line integral convolution. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729408"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Varia, N., Dokania, A., and Senthilnath, J. (2018, January 18\u201321). DeepExt: A Convolution Neural Network for Road Extraction using RGB images captured by UAV. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628717"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., and Shukla, N. (2019). Extraction of road features from UAV images using a novel level set segmentation approach. Int. J. Urban Sci.","DOI":"10.1080\/12265934.2019.1596040"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"6356","DOI":"10.1109\/TGRS.2013.2296351","article-title":"Detecting cars in UAV images with a catalog-based approach","volume":"52","author":"Moranduzzo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.isprsjprs.2014.12.025","article-title":"Automatic registration of UAV-borne sequent images and LiDAR data","volume":"101","author":"Yang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"016020","DOI":"10.1117\/1.JRS.12.016020","article-title":"UFCN: A fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle","volume":"12","author":"Kestur","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1109\/LGRS.2018.2864342","article-title":"Road Segmentation in SAR Satellite Images With Deep Fully Convolutional Neural Networks","volume":"15","author":"Henry","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","unstructured":"Panboonyuen, T., Jitkajornwanich, K., Lawawirojwong, S., Srestasathiern, P., and Vateekul, P. (2017). Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields. J. Remote Sens., 9.","DOI":"10.20944\/preprints201706.0012.v3"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Constantin, A., Ding, J.-J., and Lee, Y.-C. (2018, January 26\u201330). Accurate Road Detection from Satellite Images Using Modified U-net. Proceedings of the 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Chengdu, China.","DOI":"10.1109\/APCCAS.2018.8605652"},{"key":"ref_60","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_61","doi-asserted-by":"crossref","first-page":"46988","DOI":"10.1109\/ACCESS.2018.2867210","article-title":"Road Extraction From a High Spatial Resolution Remote Sensing Image Based on Richer Convolutional Features","volume":"6","author":"Hong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Xin, J., Zhang, X., Zhang, Z., and Fang, W. (2019). Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet. Remote Sens., 11.","DOI":"10.3390\/rs11212499"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/2150704X.2018.1557791","article-title":"A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images","volume":"10","author":"Li","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_64","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_65","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_66","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Seferbekov, S., Iglovikov, V., and Shvets, A. (2018, January 18\u201322). Fully convolutional network for automatic road extraction from satellite imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00035"},{"key":"ref_67","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_68","doi-asserted-by":"crossref","unstructured":"Doshi, J. (2018, January 18\u201322). Residual inception skip network for binary segmentation. Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00037"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Xu, Y., Feng, Y., Xie, Z., Hu, A., and Zhang, X. (2018, January 28\u201330). A Research on Extracting Road Network from High Resolution Remote Sensing Imagery. Proceedings of the 2018 26th International Conference on Geoinformatics, Kunming, China.","DOI":"10.1109\/GEOINFORMATICS.2018.8557042"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"He, H., Yang, D., Wang, S., Wang, S., and Liu, X. (2018). Road segmentation of cross-modal remote sensing images using deep segmentation network and transfer learning. Ind. Robot Int. J.","DOI":"10.1108\/IR-05-2018-0112"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Xia, W., Zhang, Y.-Z., Liu, J., Luo, L., and Yang, K. (2018). Road Extraction from High Resolution Image with Deep Convolution Network\u2014A Case Study of GF-2 Image. Proceedings, 2.","DOI":"10.3390\/ecrs-2-05138"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Gao, L., Song, W., Dai, J., and Chen, Y. (2019). Road Extraction from High-Resolution Remote Sensing Imagery Using Refined Deep Residual Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11050552"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Xie, Y., Miao, F., Zhou, K., and Peng, J. (2019). HsgNet: A Road Extraction Network Based on Global Perception of High-Order Spatial Information. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120571"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"25486","DOI":"10.1109\/ACCESS.2017.2773142","article-title":"Road detection from remote sensing images by generative adversarial networks","volume":"6","author":"Shi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Costea, D., Marcu, A., Slusanschi, E., and Leordeanu, M. (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 Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.246"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_77","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2017, January 22\u201329). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Venice, Italy."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_80","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_81","unstructured":"Luc, P., Couprie, C., Chintala, S., and Verbeek, J. (2016). Semantic segmentation using adversarial networks. arXiv."},{"key":"ref_82","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/JSTARS.2019.2955277","article-title":"Road extraction from high-resolution satellite images based on multiple descriptors","volume":"13","author":"Dai","year":"2020","journal-title":"IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Ghasemkhani, N., Vayghan, S.S., Abdollahi, A., Pradhan, B., and Alamri, A. (2020). Urban Development Modeling Using Integrated Fuzzy Systems, Ordered Weighted Averaging (OWA), and Geospatial Techniques. Sustainability, 12.","DOI":"10.3390\/su12030809"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1444\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:52:23Z","timestamp":1760363543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/9\/1444"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,2]]},"references-count":84,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["rs12091444"],"URL":"https:\/\/doi.org\/10.3390\/rs12091444","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,2]]}}}