{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:44:54Z","timestamp":1775144694665,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T00:00:00Z","timestamp":1678838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017691","name":"Key Research and Development Program of Guangxi","doi-asserted-by":"publisher","award":["Guike-AB22035060"],"award-info":[{"award-number":["Guike-AB22035060"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017691","name":"Key Research and Development Program of Guangxi","doi-asserted-by":"publisher","award":["CBAS2022IRP04"],"award-info":[{"award-number":["CBAS2022IRP04"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017691","name":"Key Research and Development Program of Guangxi","doi-asserted-by":"publisher","award":["42171291"],"award-info":[{"award-number":["42171291"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017691","name":"Key Research and Development Program of Guangxi","doi-asserted-by":"publisher","award":["CDUT2022BJCX015"],"award-info":[{"award-number":["CDUT2022BJCX015"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["Guike-AB22035060"],"award-info":[{"award-number":["Guike-AB22035060"]}]},{"name":"Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["CBAS2022IRP04"],"award-info":[{"award-number":["CBAS2022IRP04"]}]},{"name":"Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["42171291"],"award-info":[{"award-number":["42171291"]}]},{"name":"Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals","award":["CDUT2022BJCX015"],"award-info":[{"award-number":["CDUT2022BJCX015"]}]},{"name":"National Natural Science Foundation of China","award":["Guike-AB22035060"],"award-info":[{"award-number":["Guike-AB22035060"]}]},{"name":"National Natural Science Foundation of China","award":["CBAS2022IRP04"],"award-info":[{"award-number":["CBAS2022IRP04"]}]},{"name":"National Natural Science Foundation of China","award":["42171291"],"award-info":[{"award-number":["42171291"]}]},{"name":"National Natural Science Foundation of China","award":["CDUT2022BJCX015"],"award-info":[{"award-number":["CDUT2022BJCX015"]}]},{"name":"Chengdu University of Technology Post-graduate Innovative Cultivation Program: Tunnel Geothermal Disaster Susceptibility Evaluation in Sichuan-Tibet Railway Based on Deep Learning","award":["Guike-AB22035060"],"award-info":[{"award-number":["Guike-AB22035060"]}]},{"name":"Chengdu University of Technology Post-graduate Innovative Cultivation Program: Tunnel Geothermal Disaster Susceptibility Evaluation in Sichuan-Tibet Railway Based on Deep Learning","award":["CBAS2022IRP04"],"award-info":[{"award-number":["CBAS2022IRP04"]}]},{"name":"Chengdu University of Technology Post-graduate Innovative Cultivation Program: Tunnel Geothermal Disaster Susceptibility Evaluation in Sichuan-Tibet Railway Based on Deep Learning","award":["42171291"],"award-info":[{"award-number":["42171291"]}]},{"name":"Chengdu University of Technology Post-graduate Innovative Cultivation Program: Tunnel Geothermal Disaster Susceptibility Evaluation in Sichuan-Tibet Railway Based on Deep Learning","award":["CDUT2022BJCX015"],"award-info":[{"award-number":["CDUT2022BJCX015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Acquiring road information is important for smart cities and sustainable urban development. In recent years, significant progress has been made in the extraction of urban road information from remote sensing images using deep learning (DL) algorithms. However, due to the complex shape, narrowness, and high span of roads in the images, the results are often unsatisfactory. This article proposes a Seg-Road model to improve road connectivity. The Seg-Road uses a transformer structure to extract the long-range dependency and global contextual information to improve the fragmentation of road segmentation and uses a convolutional neural network (CNN) structure to extract local contextual information to improve the segmentation of road details. Furthermore, a novel pixel connectivity structure (PCS) is proposed to improve the connectivity of road segmentation and the robustness of prediction results. To verify the effectiveness of Seg-Road for road segmentation, the DeepGlobe and Massachusetts datasets were used for training and testing. The experimental results show that Seg-Road achieves state-of-the-art (SOTA) performance, with an intersection over union (IoU) of 67.20%, mean intersection over union (MIoU) of 82.06%, F1 of 91.43%, precision of 90.05%, and recall of 92.85% in the DeepGlobe dataset, and achieves an IoU of 68.38%, MIoU of 83.89%, F1 of 90.01%, precision of 87.34%, and recall of 92.86% in the Massachusetts dataset, which is better than the values for CoANet. Further, it has higher application value for achieving sustainable urban development.<\/jats:p>","DOI":"10.3390\/rs15061602","type":"journal-article","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T02:40:11Z","timestamp":1678934411000},"page":"1602","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Seg-Road: A Segmentation Network for Road Extraction Based on Transformer and CNN with Connectivity Structures"],"prefix":"10.3390","volume":"15","author":[{"given":"Jingjing","family":"Tao","sequence":"first","affiliation":[{"name":"College of Geography and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7579-1968","authenticated-orcid":false,"given":"Zhe","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China"},{"name":"International Research Centre of Big Data for Sustainable Development Goals (CBAS), Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhongchang","family":"Sun","sequence":"additional","affiliation":[{"name":"International Research Centre of Big Data for Sustainable Development Goals (CBAS), Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Hainan Laboratory of Earth Observation, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0337-1862","authenticated-orcid":false,"given":"Huadong","family":"Guo","sequence":"additional","affiliation":[{"name":"International Research Centre of Big Data for Sustainable Development Goals (CBAS), Beijing 100094, China"},{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Hainan Laboratory of Earth Observation, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}]},{"given":"Bo","family":"Leng","sequence":"additional","affiliation":[{"name":"College of Management Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5377-4163","authenticated-orcid":false,"given":"Zhengbo","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yanli","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Ziqiong","family":"He","sequence":"additional","affiliation":[{"name":"College of Management Science, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Xiangqi","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Jinpei","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,15]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"126719","DOI":"10.1016\/j.conbuildmat.2022.126719","article-title":"Innovative Method for Pavement Multiple Damages Segmentation and Measurement by the Road-Seg-CapsNet of Feature Fusion","volume":"324","author":"Dong","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_3","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_4","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_5","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_6","doi-asserted-by":"crossref","unstructured":"Guo, M., Liu, H., Xu, Y., and Huang, Y. (2020). Building Extraction Based on U-Net with an Attention Block and Multiple Losses. Remote Sens., 12.","DOI":"10.3390\/rs12091400"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, Z., Chang, R., and Chen, Z. (2022). Automatic Detection Method for Loess Landslides Based on GEE and an Improved YOLOX Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14184599"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, Z., Chen, Z., Sun, Z., Guo, H., Leng, B., He, Z., Yang, J., and Xing, S. (2022). SegDetector: A Deep Learning Model for Detecting Small and Overlapping Damaged Buildings in Satellite Images. Remote Sens., 14.","DOI":"10.3390\/rs14236136"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mosinska, A., Marquez-Neila, P., Kozinski, M., and Fua, P. (2018, January 18\u201323). Beyond the Pixel-Wise Loss for Topology-Aware Delineation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00331"},{"key":"ref_10","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\u201323). RoadTracer: Automatic Extraction of Road Networks from Aerial Images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00496"},{"key":"ref_11","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 Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_12","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 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00893"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vasu, S., Kozinski, M., Citraro, L., and Fua, P. (2020, January 23\u201328). TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation. Proceedings of the Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Glasgow, UK.","DOI":"10.1007\/978-3-030-58583-9_14"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8540","DOI":"10.1109\/TIP.2021.3117076","article-title":"CoANet: Connectivity Attention Network for Road Extraction from Satellite Imagery","volume":"30","author":"Mei","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cao, X., Zhang, K., and Jiao, L. (2022). CSANet: Cross-Scale Axial Attention Network for Road Segmentation. Remote Sens., 15.","DOI":"10.3390\/rs15010003"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104689","DOI":"10.1016\/j.autcon.2022.104689","article-title":"Automatic Pixel-Level Detection of Vertical Cracks in Asphalt Pavement Based on GPR Investigation and Improved Mask R-CNN","volume":"146","author":"Liu","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yuan, W., and Xu, W. (2022). GapLoss: A Loss Function for Semantic Segmentation of Roads in Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14102422"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107155","DOI":"10.1016\/j.compeleceng.2021.107155","article-title":"Multi-Feature Fusion Network for Road Scene Semantic Segmentation","volume":"92","author":"Sun","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1109\/JSTARS.2020.2983788","article-title":"DeepWindow: Sliding Window Based on Deep Learning for Road Extraction from Remote Sensing Images","volume":"13","author":"Lian","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, D., Liu, Z., Gu, X., Wu, W., Chen, Y., and Wang, L. (2022). Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks. Remote Sens., 14.","DOI":"10.3390\/rs14163892"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tardy, H., Soil\u00e1n, M., Mart\u00edn-Jim\u00e9nez, J.A., and Gonz\u00e1lez-Aguilera, D. (2023). Automatic Road Inventory Using a Low-Cost Mobile Mapping System and Based on a Semantic Segmentation Deep Learning Model. Remote Sens., 15.","DOI":"10.3390\/rs15051351"},{"key":"ref_22","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_23","first-page":"12077","article-title":"SegFormer: Simple and efficient design for semantic segmentation with transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., and Dong, L. (2022, January 18\u201324). Swin transformer v2: Scaling up capacity and resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106985","DOI":"10.1016\/j.enggeo.2023.106985","article-title":"Tunnel Geothermal Disaster Susceptibility Evaluation Based on Interpretable Ensemble Learning: A Case Study in Ya\u2019an\u2013Changdu Section of the Sichuan\u2013Tibet Traffic Corridor","volume":"313","author":"Chen","year":"2023","journal-title":"Eng. Geol."},{"key":"ref_26","unstructured":"Singh, S., Batra, A., Pang, G., Torresani, L., Basu, S., Paluri, M., and Jawahar, C.V. (2019, January 3\u20136). Self-Supervised Feature Learning for Semantic Segmentation of Overhead Imagery. Proceedings of the British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.isprsjprs.2021.05.016","article-title":"Urban Road Mapping Based on an End-to-End Road Vectorization Mapping Network Framework","volume":"178","author":"Chen","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Batra, A., Singh, S., Pang, G., Basu, S., Jawahar, C.V., and Paluri, M. (2019, January 15\u201320). Improved Road Connectivity by Joint Learning of Orientation and Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01063"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xie, Y., Zhang, J., Shen, C., and Xia, Y. (October, January 27). CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2021, Strasbourg, France. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fang, J., Lin, H., Chen, X., and Zeng, K. (2022, January 18\u201324). A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00119"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Pinto, F., Torr, P.H., and Dokania, P.K. (2022, January 23\u201324). An impartial take to the cnn vs transformer robustness contest. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19778-9_27"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.inffus.2022.10.030","article-title":"Shape-Former: Bridging CNN and Transformer via ShapeConv for multimodal image matching","volume":"91","author":"Chen","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_33","unstructured":"Kitaev, N., Kaiser, A., and Levskaya, A. (2020). Reformer: The efficient transformer. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rao, R.M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., and Rives, A. (2021, January 18\u201324). Msa transformer. Proceedings of the International Conference on Machine Learning: PMLR, Online.","DOI":"10.1101\/2021.02.12.430858"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., and Wu, J. (2020, January 4\u20138). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. Proceedings of the ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","article-title":"NAS-Unet: Neural Architecture Search for Medical Image Segmentation","volume":"7","author":"Weng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A.L., and Fei-Fei, L. (2019, January 15\u201320). Auto-Deeplab: Hierarchical Neural Architecture Search for Semantic Image Segmentation. Proceedings of the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00017"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhu, Y., Adam, H., Yuille, A., and Chen, L.C. (2021, January 20\u201325). Max-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00542"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raska, R. (2018, January 18\u201323). DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yan, H., Zhang, C., Yang, J., Wu, M., and Chen, J. (2021, January 11\u201316). Did-Linknet: Polishing D-Block with Dense Connection and Iterative Fusion for Road Extraction. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554534"},{"key":"ref_41","first-page":"1","article-title":"NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Nonlocal Operations","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Mattyus, G., Luo, W., and Urtasun, R. (2017, January 22\u201329). DeepRoadMapper: Extracting Road Topology from Aerial Images. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.372"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"106211","DOI":"10.1016\/j.cmpb.2021.106211","article-title":"PSP Net-Based Automatic Segmentation Network Model for Prostate Magnetic Resonance Imaging","volume":"207","author":"Yan","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1080\/17538947.2022.2061055","article-title":"Quantitative Prediction and Evaluation of Geothermal Resource Areas in the Southwest Section of the Mid-Spine Belt of Beautiful China","volume":"15","author":"Chen","year":"2022","journal-title":"Int. J. Digit. Earth"},{"key":"ref_45","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 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2019.06.010","article-title":"Pyramid Scene Parsing Network in 3D: Improving Semantic Segmentation of Point Clouds with Multi-Scale Contextual Information","volume":"154","author":"Fang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Shaw, P., Uszkoreit, J., and Vaswani, A. (, January June). Self-Attention with Relative Position Representations. Proceedings of the NAACL HLT 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\u2014Proceedings of the Conference, New Orleans, LA, USA.","DOI":"10.18653\/v1\/N18-2074"},{"key":"ref_48","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 10\u201315). Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0065-2601(08)60321-4","article-title":"Self-Attention and Behavior: A Review and Theoretical Update","volume":"23","author":"Gibbons","year":"1990","journal-title":"Adv. Exp. Soc. Psychol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, Z., Chang, R., Guo, H., Pei, X., Zhao, W., Yu, Z., and Zou, L. (2022). Prediction of Potential Geothermal Disaster Areas along the Yunnan\u2013Tibet Railway Project. Remote Sens., 14.","DOI":"10.3390\/rs14133036"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Pan, X., Shi, J., Luo, P., Wang, X., and Tang, X. (2018, January 2\u20137). Spatial as Deep: Spatial CNN for Traffic Scene Understanding. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.1109\/TPAMI.2019.2926463","article-title":"The ApolloScape Open Dataset for Autonomous Driving and Its Application","volume":"42","author":"Huang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1602\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:56:02Z","timestamp":1760122562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,15]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061602"],"URL":"https:\/\/doi.org\/10.3390\/rs15061602","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,15]]}}}