{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T17:13:25Z","timestamp":1773422005507,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Shandong Natural Science Foundation Innovation and Development Joint Fund","award":["ZR2024LZN008"],"award-info":[{"award-number":["ZR2024LZN008"]}]},{"name":"Jinan City\u2019s Self-Developed Innovative Team Project for Higher Educational Institutions","award":["20233040"],"award-info":[{"award-number":["20233040"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00371-026-04387-0","type":"journal-article","created":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T11:57:10Z","timestamp":1771156630000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DE-UNet: an enhanced UNet with dual-branch attention convolution and efficient multi-scale attention aggregation for UAV lane line segmentation"],"prefix":"10.1007","volume":"42","author":[{"given":"Yuehao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiqing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengmeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,15]]},"reference":[{"key":"4387_CR1","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.isprsjprs.2020.05.009","volume":"165","author":"Y Lyu","year":"2020","unstructured":"Lyu, Y., Vosselman, G., Xia, G.-S., Yilmaz, A., Yang, M.Y.: UAVid: a semantic segmentation dataset for UAV imagery. ISPRS J. Photogramm. Remote Sens. 165, 108\u2013119 (2020). https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.05.009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"4387_CR2","doi-asserted-by":"publisher","unstructured":"Bai, Y., Feng, Y.: A dynamic unmanned aerial vehicle routing framework for urban traffic monitoring. arXiv:2501.09249 (2025). https:\/\/doi.org\/10.48550\/arXiv.2501.09249","DOI":"10.48550\/arXiv.2501.09249"},{"key":"4387_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/rs16122056","volume":"16","author":"R Liu","year":"2024","unstructured":"Liu, R., Wu, J., Lu, W., Miao, Q., Zhang, H., Liu, X., Lu, Z., Li, L.: A review of deep learning-based methods for road extraction from high-resolution remote sensing images. Remote Sensing 16, 2056 (2024). https:\/\/doi.org\/10.3390\/rs16122056","journal-title":"Remote Sensing"},{"key":"4387_CR4","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s11390-020-0476-4","volume":"35","author":"D Liang","year":"2020","unstructured":"Liang, D., Guo, Y.C., Zhang, S.K., et al.: Lane detection: a survey with new results. J. Comput. Sci. Technol. 35, 493\u2013505 (2020). https:\/\/doi.org\/10.1007\/s11390-020-0476-4","journal-title":"J. Comput. Sci. Technol."},{"key":"4387_CR5","doi-asserted-by":"publisher","first-page":"5710","DOI":"10.1109\/TITS.2024.3524603","volume":"26","author":"J Bi","year":"2025","unstructured":"Bi, J., Song, Y., Jiang, Y., Sun, L., Wang, X., Liu, Z., Xu, J., Quan, S., Dai, Z., Yan, W.: Lane detection for autonomous driving: comprehensive reviews, current challenges, and future predictions. IEEE Trans. Intell. Transp. Syst. 26, 5710\u20135746 (2025). https:\/\/doi.org\/10.1109\/TITS.2024.3524603","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4387_CR6","doi-asserted-by":"publisher","unstructured":"Pan, X., Shi, J., Luo, P., Wang, X., Tang, X.: Spatial as deep: Spatial CNN for traffic scene understanding. In: Proceedings of the AAAI Conference Artificial Intelligent, New Orleans, LA, USA, 2\u20137 Feb 2018, 32 (2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.12301","DOI":"10.1609\/aaai.v32i1.12301"},{"key":"4387_CR7","doi-asserted-by":"crossref","unstructured":"Zheng, T., Huang, Y., Liu, Y., Tang, W., Yang, Z., Cai, D., He, X.: CLRNet: Cross layer refinement network for lane detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. (CVPR), New Orleans, LA, USA, 19\u201324 Jun 2022, pp. 888\u2013897 (2022)","DOI":"10.1109\/CVPR52688.2022.00097"},{"key":"4387_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/rs12091444","volume":"12","author":"A Abdollahi","year":"2020","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., Alamri, A.: Deep learning approaches applied to remote sensing datasets for road extraction: a state-of-the-art review. Remote Sensing 12, 1444 (2020). https:\/\/doi.org\/10.3390\/rs12091444","journal-title":"Remote Sensing"},{"key":"4387_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi12030132","volume":"12","author":"Y Song","year":"2023","unstructured":"Song, Y., Huang, T., Fu, X., Jiang, Y., Xu, J., Zhao, J., Yan, W., Wang, X.: A novel lane line detection algorithm for driverless geographic information perception using mixed-attention mechanism ResNet and row anchor classification. ISPRS Int. J. Geo-Inf. 12, 132 (2023). https:\/\/doi.org\/10.3390\/ijgi12030132","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"4387_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.isprsjprs.2024.03.012","volume":"211","author":"J Cheng","year":"2024","unstructured":"Cheng, J., Deng, C., Su, Y., An, Z., Wang, Q.: Methods and datasets on semantic segmentation for unmanned aerial vehicle remote sensing images: a review. ISPRS J. Photogramm. Remote Sens. 211, 1\u201334 (2024). https:\/\/doi.org\/10.1016\/j.isprsjprs.2024.03.012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"4387_CR11","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.neucom.2021.01.100","volume":"438","author":"Z Liu","year":"2021","unstructured":"Liu, Z., Zhu, L.: Label-guided attention distillation for lane segmentation. Neurocomputing 438, 312\u2013322 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2021.01.100","journal-title":"Neurocomputing"},{"key":"4387_CR12","doi-asserted-by":"publisher","first-page":"84893","DOI":"10.1109\/ACCESS.2020.2991930","volume":"8","author":"M Marzougui","year":"2020","unstructured":"Marzougui, M., Alasiry, A., Kortli, Y., Baili, J.: A lane tracking method based on progressive probabilistic Hough transform. IEEE Access 8, 84893\u201384905 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2991930","journal-title":"IEEE Access"},{"key":"4387_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12051079","volume":"12","author":"MA Javeed","year":"2023","unstructured":"Javeed, M.A., Ghaffar, M.A., Ashraf, M.A., Zubair, N., Metwally, A.S.M., Tag-Eldin, E.M., Bocchetta, P., Javed, M.S., Jiang, X.: Lane line detection and object scene segmentation using Otsu thresholding and the fast Hough transform for intelligent vehicles in complex road conditions. Electronics 12, 1079 (2023). https:\/\/doi.org\/10.3390\/electronics12051079","journal-title":"Electronics"},{"key":"4387_CR14","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1134\/S1054661818020049","volume":"28","author":"F Zheng","year":"2018","unstructured":"Zheng, F., Luo, S., Song, K., Yan, C.-W., Wang, M.-C.: Improved lane line detection algorithm based on Hough transform. Pattern Recognit Image Anal. 28, 254\u2013260 (2018). https:\/\/doi.org\/10.1134\/S1054661818020049","journal-title":"Pattern Recognit Image Anal."},{"key":"4387_CR15","doi-asserted-by":"publisher","DOI":"10.1177\/17298814211008752","volume":"18","author":"Q Huang","year":"2021","unstructured":"Huang, Q., Liu, J.: Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios. Int. J. Adv. Robot. Syst. 18, 17298814211008752 (2021). https:\/\/doi.org\/10.1177\/17298814211008752","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"4387_CR16","doi-asserted-by":"publisher","first-page":"347","DOI":"10.11591\/ijece.v13i1.pp347-357","volume":"13","author":"MA Noman","year":"2023","unstructured":"Noman, M.A., Li, Z., Al-Mukhtar, F., Rahaman, M., Omarov, B., Ray, S., Miah, S., Wang, C.: A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle. Int. J. Electr. Comput. Eng. 13, 347\u2013357 (2023). https:\/\/doi.org\/10.11591\/ijece.v13i1.pp347-357","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"4387_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/s23125751","volume":"23","author":"Q Zhang","year":"2023","unstructured":"Zhang, Q., Liu, J., Jiang, X.: Lane detection algorithm in curves based on multi-sensor fusion. Sensors 23, 5751 (2023). https:\/\/doi.org\/10.3390\/s23125751","journal-title":"Sensors"},{"key":"4387_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-76837-5","volume":"14","author":"S Zhai","year":"2024","unstructured":"Zhai, S., Zhao, X., Zu, G., Lu, L., Cheng, C.: An algorithm for lane detection based on RIME optimization and optimal threshold. Sci. Rep. 14, 27244 (2024). https:\/\/doi.org\/10.1038\/s41598-024-76837-5","journal-title":"Sci. Rep."},{"key":"4387_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/s21062133","volume":"21","author":"CN Khac","year":"2021","unstructured":"Khac, C.N., Choi, Y., Park, J.H., Jung, H.-Y.: A robust road vanishing point detection adapted to real-world driving scenes. Sensors 21, 2133 (2021). https:\/\/doi.org\/10.3390\/s21062133","journal-title":"Sensors"},{"key":"4387_CR20","doi-asserted-by":"publisher","unstructured":"Neven, D., De Brabandere, B., Georgoulis, S., Proesmans, M., Van Gool, L.: Towards end-to-end lane detection: An instance segmentation approach. In: Proceedings 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, Suzhou, China, 26\u201330 Jun 2018, pp. 286\u2013291 (2018). https:\/\/doi.org\/10.1109\/IVS.2018.8500547","DOI":"10.1109\/IVS.2018.8500547"},{"key":"4387_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-70116-z","volume":"14","author":"V Maddiralla","year":"2024","unstructured":"Maddiralla, V., Subramanian, S.: Effective lane detection on complex roads with convolutional attention mechanism in autonomous vehicles. Sci. Rep. 14, 19193 (2024). https:\/\/doi.org\/10.1038\/s41598-024-70116-z","journal-title":"Sci. Rep."},{"key":"4387_CR22","doi-asserted-by":"publisher","first-page":"13240","DOI":"10.1109\/TITS.2024.3410376","volume":"25","author":"B Liu","year":"2024","unstructured":"Liu, B., Ling, Q.: Hyper-anchor based lane detection. IEEE Trans. Intell. Transp. Syst. 25, 13240\u201313252 (2024). https:\/\/doi.org\/10.1109\/TITS.2024.3410376","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4387_CR23","doi-asserted-by":"publisher","unstructured":"Xiao, L., Li, X., Yang, S., Yang, W.: ADNet: Lane shape prediction via anchor decomposition. arXiv:2308.10481 (2023). https:\/\/doi.org\/10.48550\/arXiv.2308.10481","DOI":"10.48550\/arXiv.2308.10481"},{"key":"4387_CR24","doi-asserted-by":"publisher","first-page":"3663","DOI":"10.1007\/s00371-024-03626-6","volume":"41","author":"S Li","year":"2025","unstructured":"Li, S., Yao, S., Wang, Z., et al.: FFCANet: a frequency channel fusion coordinate attention mechanism network for lane detection. Vis. Comput. 41, 3663\u20133678 (2025). https:\/\/doi.org\/10.1007\/s00371-024-03626-6","journal-title":"Vis. Comput."},{"key":"4387_CR25","doi-asserted-by":"publisher","first-page":"5435","DOI":"10.1007\/s00371-024-03731-6","volume":"41","author":"W-J Yang","year":"2025","unstructured":"Yang, W.-J., Ho, L.-Y.: CSA-lanenet: a contiguous spatial attention lane detection network with vision transformer modules. Vis. Comput. 41, 5435\u20135445 (2025). https:\/\/doi.org\/10.1007\/s00371-024-03731-6","journal-title":"Vis. Comput."},{"key":"4387_CR26","doi-asserted-by":"publisher","first-page":"9321","DOI":"10.1109\/TITS.2024.3386531","volume":"25","author":"J Zhao","year":"2024","unstructured":"Zhao, J., Qiu, Z., Hu, H., Sun, S.: HWLane: HW-transformer for lane detection. IEEE Trans. Intell. Transp. Syst. 25, 9321\u20139331 (2024). https:\/\/doi.org\/10.1109\/TITS.2024.3386531","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"4387_CR27","doi-asserted-by":"publisher","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: Simple and efficient design for semantic segmentation with transformers. arXiv:2105.15203 (2021). https:\/\/doi.org\/10.48550\/arXiv.2105.15203","DOI":"10.48550\/arXiv.2105.15203"},{"key":"4387_CR28","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Munich, Germany, 5\u20139 Oct 2015, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4387_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/s24051708","volume":"24","author":"S Mo","year":"2024","unstructured":"Mo, S., Shi, Y., Yuan, Q., Li, M.: A survey of deep learning road extraction algorithms using high-resolution remote sensing images. Sensors 24, 1708 (2024). https:\/\/doi.org\/10.3390\/s24051708","journal-title":"Sensors"},{"key":"4387_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3150041","volume":"60","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Peng, Y., Li, W., Alexandropoulos, G.C., Yu, J., Ge, D., Xiang, W.: DDU-Net: Dual-decoder-U-Net for road extraction using high-resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1\u201312 (2022). https:\/\/doi.org\/10.1109\/TGRS.2022.3150041","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"4387_CR31","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.isprsjprs.2022.06.008","volume":"190","author":"L Wang","year":"2022","unstructured":"Wang, L., Li, R., Zhang, C., Fang, S., Duan, C., Meng, X., Atkinson, P.M.: UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery. ISPRS J. Photogramm. Remote Sens. 190, 196\u2013214 (2022). https:\/\/doi.org\/10.1016\/j.isprsjprs.2022.06.008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"4387_CR32","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C.: ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogramm. Remote Sens. 162, 94\u2013114 (2020). https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.01.013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"4387_CR33","doi-asserted-by":"publisher","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: A nested U-Net architecture for medical image segmentation. In: Stoyanov, D., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.M.R.S., Bradley, A., Papa, J.P., Belagiannis, V., Nascimento, J.C., Lu, Z., Conjeti, S., Moradi, M., Greenspan, H., Madabhushi, A. (eds.) DLMIA\/ML-CDS, MICCAI 2018, Granada, Spain, 20 Sep 2018, pp. 3\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"4387_CR34","doi-asserted-by":"publisher","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: TransUNet: Transformers make strong encoders for medical image segmentation. arXiv:2102.04306 (2021). https:\/\/doi.org\/10.48550\/arXiv.2102.04306","DOI":"10.48550\/arXiv.2102.04306"},{"key":"4387_CR35","doi-asserted-by":"publisher","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-Unet: UNet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision \u2013 ECCV 2022 Workshops, Tel Aviv, Israel, 23\u201327 Oct 2022, pp. 205\u2013218. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_9","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"4387_CR36","doi-asserted-by":"publisher","unstructured":"Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M.J., Heinrich, M.P., Misawa, K., Mori, K., McDonagh, S.G., Hammerla, N.Y., Kainz, B., Glocker, B., Rueckert, D.: Attention U-Net: learning where to look for the pancreas. arXiv:1804.03999 (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.03999","DOI":"10.48550\/arXiv.1804.03999"},{"key":"4387_CR37","doi-asserted-by":"publisher","unstructured":"Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., Fan, C.: SA-UNet: Spatial attention U-Net for retinal vessel segmentation. In: Proceedings of 25th International Conference on Pattern Recognition. (ICPR 2020), Milan, Italy, 10\u201315 Jan 2021, pp. 1236\u20131242 (2021). https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413346","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"4387_CR38","doi-asserted-by":"publisher","unstructured":"Wang, J., Long, X., Chen, G., Wu, Z., Chen, Z., Ding, E.: U-HRNet: Delving into improving semantic representation of High Resolution network for dense prediction. arXiv:2210.07140 (2022). https:\/\/doi.org\/10.48550\/arXiv.2210.07140","DOI":"10.48550\/arXiv.2210.07140"},{"key":"4387_CR39","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1109\/JSTARS.2023.3336924","volume":"17","author":"A Akhtarmanesh","year":"2024","unstructured":"Akhtarmanesh, A., Abbasi-Moghadam, D., Sharifi, A., Yadkouri, M.H., Tariq, A., Lu, L.: Road extraction from satellite images using attention-assisted UNet. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 17, 1126\u20131136 (2024). https:\/\/doi.org\/10.1109\/JSTARS.2023.3336924","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"4387_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/math12081206","volume":"12","author":"S-H Lee","year":"2024","unstructured":"Lee, S.-H., Lee, S.-H.: U-Net-based learning using enhanced lane detection with directional lane attention maps for various driving environments. Mathematics 12, 1206 (2024). https:\/\/doi.org\/10.3390\/math12081206","journal-title":"Mathematics"},{"key":"4387_CR41","doi-asserted-by":"publisher","DOI":"10.3390\/s21062153","volume":"21","author":"Y Hou","year":"2021","unstructured":"Hou, Y., Liu, Z., Zhang, T., Li, Y.: C-UNet: complement UNet for remote sensing road extraction. Sensors 21, 2153 (2021). https:\/\/doi.org\/10.3390\/s21062153","journal-title":"Sensors"},{"key":"4387_CR42","doi-asserted-by":"publisher","DOI":"10.3390\/s25061786","volume":"25","author":"Z Zhang","year":"2025","unstructured":"Zhang, Z., Li, G.: UAV imagery real-time semantic segmentation with global\u2013local information attention. Sensors 25, 1786 (2025). https:\/\/doi.org\/10.3390\/s25061786","journal-title":"Sensors"},{"key":"4387_CR43","doi-asserted-by":"publisher","DOI":"10.3390\/app12041953","volume":"12","author":"F Sultonov","year":"2022","unstructured":"Sultonov, F., Park, J.-H., Yun, S., Lim, D.-W., Kang, J.-M.: Mixer U-Net: an improved automatic road extraction from UAV imagery. Appl. Sci. 12, 1953 (2022). https:\/\/doi.org\/10.3390\/app12041953","journal-title":"Appl. Sci."},{"key":"4387_CR44","doi-asserted-by":"publisher","unstructured":"Rahman, M.M., Munir, M., Marculescu, R.: EMCAD: Efficient multi-scale convolutional attention decoding for medical image segmentation. arXiv:2405.06880 (2024). https:\/\/doi.org\/10.48550\/arXiv.2405.06880","DOI":"10.48550\/arXiv.2405.06880"},{"key":"4387_CR45","doi-asserted-by":"publisher","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018, Munich, Germany, 8\u201314 Sep 2018, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49","DOI":"10.1007\/978-3-030-01234-2_49"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04387-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-026-04387-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-026-04387-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T16:27:42Z","timestamp":1773419262000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-026-04387-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["4387"],"URL":"https:\/\/doi.org\/10.1007\/s00371-026-04387-0","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"3 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}],"article-number":"170"}}