{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T17:06:57Z","timestamp":1783444017180,"version":"3.54.6"},"reference-count":61,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T00:00:00Z","timestamp":1640736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.<\/jats:p>","DOI":"10.3390\/ijgi11010009","type":"journal-article","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T08:12:15Z","timestamp":1640765535000},"page":"9","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images"],"prefix":"10.3390","volume":"11","author":[{"given":"Shengfu","family":"Li","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"},{"name":"Sichuan Highway Planning, Survey, Design and Research Institute Ltd., Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9469-030X","authenticated-orcid":false,"given":"Cheng","family":"Liao","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulin","family":"Ding","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1137-2208","authenticated-orcid":false,"given":"Han","family":"Hu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Jia","sequence":"additional","affiliation":[{"name":"Sichuan Highway Planning, Survey, Design and Research Institute Ltd., Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Chen","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuming","family":"Ge","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyang","family":"Liu","sequence":"additional","affiliation":[{"name":"PLA Key Laboratory of Hydrographic Surveying and Mapping, Dalian Naval Academy, Dalian 116018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"PLA Key Laboratory of Hydrographic Surveying and Mapping, Dalian Naval Academy, Dalian 116018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,29]]},"reference":[{"key":"ref_1","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. (Engl. Ed.)"},{"key":"ref_2","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. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., and Alamri, A. (2020). Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-of-the-Art Review. Remote Sens., 12.","DOI":"10.3390\/rs12091444"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5489","DOI":"10.1109\/JSTARS.2020.3023549","article-title":"Road Extraction Methods in High-Resolution Remote Sensing Images: A Comprehensive Review","volume":"13","author":"Lian","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhu, Q., Xie, X., Hu, H., and Zeng, H. (2018). Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7090362"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","article-title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, H., Yang, D., Wang, S., Wang, S., and Li, Y. (2019). Road Extraction by Using Atrous Spatial Pyramid Pooling Integrated Encoder-Decoder Network and Structural Similarity Loss. Remote Sens., 11.","DOI":"10.3390\/rs11091015"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, S., Mu, X., Yang, D., He, H., and Zhao, P. (2021). Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields. Remote Sens., 13.","DOI":"10.3390\/rs13030465"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9362","DOI":"10.1109\/TGRS.2019.2926397","article-title":"Multi-scale and multi-task deep learning framework for automatic road extraction","volume":"57","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ding, L., and Bruzzone, L. (2021). DiResNet: Direction-Aware Residual Network for Road Extraction in VHR Remote Sensing Images. IEEE Trans. Geosci. Remote Sens., 1\u201312.","DOI":"10.1109\/TGRS.2020.3034011"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/JSTARS.2020.3042816","article-title":"Automatic Road Extraction from High-Resolution Remote Sensing Images Using a Method Based on Densely Connected Spatial Feature-Enhanced Pyramid","volume":"14","author":"Wu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1080\/17538947.2020.1773950","article-title":"The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks","volume":"13","author":"Zhang","year":"2020","journal-title":"Int. J. Digit. Earth"},{"key":"ref_15","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00035"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., and DeWitt, D. (2018, January 18\u201322). RoadTracer: Automatic Extraction of Road Networks from Aerial Images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00496"},{"key":"ref_18","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_19","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. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1109\/JSTARS.2020.3006192","article-title":"Unsupervised Feature Learning to Improve Transferability of Landslide Susceptibility Representations","volume":"13","author":"Zhu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1109\/TGRS.2020.3026051","article-title":"MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction From Remote Sensed Imagery","volume":"59","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liao, C., Hu, H., Li, H., Ge, X., Chen, M., Li, C., and Zhu, Q. (2021). Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction. Remote Sens., 13.","DOI":"10.3390\/rs13061049"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Ding, C., Weng, L., Xia, M., and Lin, H. (2021). Non-Local Feature Search Network for Building and Road Segmentation of Remote Sensing Image. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10040245"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, X., Tao, R., Li, W., Philips, W., and Liao, W. (2021). Fractional Gabor Convolutional Network for Multisource Remote Sensing Data Classification. IEEE Trans. Geosci. Remote Sens., 60.","DOI":"10.1109\/TGRS.2021.3065507"},{"key":"ref_27","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_28","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_29","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_30","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1109\/JSTARS.2021.3053603","article-title":"Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ren, Y., Yu, Y., and Guan, H. (2020). DA-CapsUNet: A dual-attention capsule U-Net for road extraction from remote sensing imagery. Remote Sens., 12.","DOI":"10.3390\/rs12182866"},{"key":"ref_32","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_35","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. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, S., Yang, H., Wu, Q., Zheng, Z., Wu, Y., and Li, J. (2020). An Improved Method for Road Extraction from High-Resolution Remote-Sensing Images that Enhances Boundary Information. Sensors, 20.","DOI":"10.3390\/s20072064"},{"key":"ref_37","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 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.222"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4283","DOI":"10.1109\/TITS.2019.2939536","article-title":"Corse-to-fine road extraction based on local Dirichlet mixture models and multiscale-high-order deep learning","volume":"21","author":"Chen","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiong, Z., Zang, Y., Wang, C., Li, J., and Li, X. (2019). Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks. Remote Sens., 11.","DOI":"10.3390\/rs11091017"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.isprsjprs.2020.08.019","article-title":"BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery","volume":"168","author":"Zhou","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","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. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Batra, A., Singh, S., Pang, G., Basu, S., Jawahar, C., and Paluri, M. (2019, January 15\u201320). Improved Road Connectivity by Joint Learning of Orientation and Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01063"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tan, Y.Q., Gao, S.H., Li, X.Y., Cheng, M.M., and Ren, B. (2020, January 13\u201319). VecRoad: Point-Based Iterative Graph Exploration for Road Graphs Extraction. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00893"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local Neural Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6302","DOI":"10.1109\/JSTARS.2021.3083055","article-title":"DA-RoadNet: A Dual-Attention Network for Road Extraction from High Resolution Satellite Imagery","volume":"14","author":"Wan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, J., Liu, Y., Zhang, Y., and Zhang, Y. (2021). Cascaded Attention DenseUNet (CADUNet) for Road Extraction from Very-High-Resolution Images. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10050329"},{"key":"ref_51","unstructured":"Luc, P., Couprie, C., Chintala, S., and Verbeek, J. (2016). Semantic Segmentation using Adversarial Networks. arXiv."},{"key":"ref_52","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 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, Italy.","DOI":"10.1109\/ICCVW.2017.246"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, X., Li, C., Tang, X., Zhou, H., and Jiao, L. (2019). Aerial Image Road Extraction Based on an Improved Generative Adversarial Network. Remote Sens., 11.","DOI":"10.3390\/rs11080930"},{"key":"ref_54","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_55","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_56","unstructured":"Bahdanau, D., Cho, K.H., and Bengio, Y. (2015, January 7\u20139). Neural machine translation by jointly learning to align and translate. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_58","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling. [Ph.D. Thesis, University of Toronto]."},{"key":"ref_59","unstructured":"(2021, December 26). Huawei Cloud Road Extraction Challenge 2020. Available online: https:\/\/competition.huaweicloud.com\/information\/1000041322\/introduction."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Geng, K., Sun, X., Yan, Z., Diao, W., and Gao, X. (2020). Topological Space Knowledge Distillation for Compact Road Extraction in Optical Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12193175"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Wang, Y. (2019). JointNet: A Common Neural Network for Road and Building Extraction. Remote Sens., 11.","DOI":"10.3390\/rs11060696"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/1\/9\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:55:25Z","timestamp":1760169325000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/1\/9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,29]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["ijgi11010009"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11010009","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,29]]}}}