{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T01:28:11Z","timestamp":1767835691228,"version":"3.49.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Project of Zhejiang Federation of Social Sciences","award":["Grant No. 2021N112"],"award-info":[{"award-number":["Grant No. 2021N112"]}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["Grant No. LQ20F020025"],"award-info":[{"award-number":["Grant No. LQ20F020025"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007834","name":"Natural Science Foundation of Ningbo Municipality","doi-asserted-by":"publisher","award":["Grant No. 202003N4073"],"award-info":[{"award-number":["Grant No. 202003N4073"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s40747-024-01735-2","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T02:05:46Z","timestamp":1740708346000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ConvNeXt embedded U-Net for semantic segmentation in urban scenes of multi-scale targets"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6098-701X","authenticated-orcid":false,"given":"Yanyan","family":"Wu","sequence":"first","affiliation":[]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"1735_CR1","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","volume":"130","author":"R Alshehhi","year":"2017","unstructured":"Alshehhi R, Marpu PR, Woon WL, Dalla Mura M (2017) Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J Photogramm Remote Sens 130:139\u2013149","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1735_CR2","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","volume":"162","author":"FI Diakogiannis","year":"2020","unstructured":"Diakogiannis FI, Waldner F, Caccetta P, Wu C (2020) Resunet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogramm Remote Sens 162:94\u2013114","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"1","key":"1735_CR3","doi-asserted-by":"publisher","first-page":"274","DOI":"10.12677\/AAM.2021.101031","volume":"10","author":"Y Yao","year":"2021","unstructured":"Yao Y, Chunkang Z, Yutian J, Xiaomei S, Yongyu W (2021) A remote sensing image road extraction method based on improved encoder-decoder network. Adv Appl Math 10(1):274\u2013281","journal-title":"Adv Appl Math"},{"key":"1735_CR4","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.isatra.2023.07.043","volume":"142","author":"R Wang","year":"2023","unstructured":"Wang R, Zhuang Z, Tao H, Paszke W, Stojanovic V (2023) Q-learning based fault estimation and fault tolerant iterative learning control for mimo systems. ISA Trans 142:123\u2013135","journal-title":"ISA Trans"},{"key":"1735_CR5","doi-asserted-by":"publisher","first-page":"7451","DOI":"10.1007\/s40747-023-01135-y","volume":"9","author":"H Peng","year":"2023","unstructured":"Peng H, Song X, Song S, Stojanovic V (2023) Hysteresis quantified control for switched reaction-diffusion systems and its application. Complex Intell Syst 9:7451\u20137460","journal-title":"Complex Intell Syst"},{"issue":"10","key":"1735_CR6","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1177\/01423312231225782","volume":"46","author":"Y Tao","year":"2024","unstructured":"Tao Y, Tao H, Zhuang Z, Stojanovic V, Paszke W (2024) Quantized iterative learning control of communication constrained system with the encoding and decoding mechanism. Trans Inst Meas Control 46(10):1943\u20131954","journal-title":"Trans Inst Meas Control"},{"key":"1735_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126498","volume":"550","author":"X Song","year":"2023","unstructured":"Song X, Wu N, Song S, Zhang Y, Stojanovic V (2023) Bipartite synchronization for cooperative-competitive neural networks with reaction-diffusion terms via dual event-triggered mechanism. Neurocomputing 550:126498","journal-title":"Neurocomputing"},{"key":"1735_CR8","doi-asserted-by":"crossref","unstructured":"Liu S, Huang D, Yunhong W (2018) Receptive field block net for accurate and fast object detection. In: Proceedings of the European conference on computer vision, Munich","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"1735_CR9","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Boston","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1735_CR10","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"1735_CR11","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas","DOI":"10.1109\/CVPR.2016.90"},{"key":"1735_CR12","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger K (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Hawaii","DOI":"10.1109\/CVPR.2017.243"},{"key":"1735_CR13","doi-asserted-by":"crossref","unstructured":"Korez R, Likar B, Pernus F, Vrtovec T (2017) Segmentation of pathological spines in ct images using a two-way cnn and a collision based model. In: Computational methods and clinical applications in musculoskeletal imaging, Cham","DOI":"10.1007\/978-3-319-74113-0_9"},{"key":"1735_CR14","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Munich","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"1735_CR15","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1735_CR16","doi-asserted-by":"crossref","unstructured":"Chen L, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision, Munich","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1735_CR17","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Xiaojuan Q, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Hawaii","DOI":"10.1109\/CVPR.2017.660"},{"issue":"4","key":"1735_CR18","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1735_CR19","doi-asserted-by":"crossref","unstructured":"Yang M, Kun Y, Zhang C, Zhiwei L, Yang K (2018) Denseaspp for semantic segmentation in street scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City","DOI":"10.1109\/CVPR.2018.00388"},{"key":"1735_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas","DOI":"10.1109\/CVPR.2016.90"},{"key":"1735_CR21","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Yong L, Bao Y, Fang Z, Hanqing L (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Seattle","DOI":"10.1109\/CVPR.2019.00326"},{"key":"1735_CR22","unstructured":"Tao A, Sapra K, Catanzaro B (2020) Hierarchical multi-scale attention for semantic segmentation. arXiv:2005.10821"},{"key":"1735_CR23","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122"},{"issue":"145","key":"1735_CR24","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.isprsjprs.2018.01.021","volume":"11","author":"D Marcos","year":"2018","unstructured":"Marcos D, Volpi M, Kellenberger B, Tuia D (2018) Land cover mapping at very high resolution with rotation equivariant cnns: towards small yet accurate models. ISPRS J Photogramm Remote Sens 11(145):96\u2013107","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1735_CR25","doi-asserted-by":"crossref","unstructured":"Zhang RQ, Yao J, Zhang K et al (2016) S-cnn-based ship detection from high-resolution remote sensing images. In: ISPRS international archives of the photogrammetry remote sensing & spatial information photogramm., vol 46, pp 423\u2013431","DOI":"10.5194\/isprsarchives-XLI-B7-423-2016"},{"issue":"58","key":"1735_CR26","doi-asserted-by":"publisher","first-page":"5367","DOI":"10.1109\/TGRS.2020.2964675","volume":"8","author":"L Ding","year":"2020","unstructured":"Ding L, Zhang J, Bruzzone L (2020) Semantic segmentation of large size VHR remote sensing images using a two-stage multiscale training architecture. IEEE Trans Geosci Remote Sens 8(58):5367\u20135376","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1735_CR27","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the British machine vision conference, Cham","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"5","key":"1735_CR28","first-page":"657","volume":"45","author":"P Haipeng","year":"2021","unstructured":"Haipeng P, Yuntao W, Miao M (2021) Vehicle identification methods based on attention mechanism and multiscale fusion learning. J Zhejiang Sci-Tech Univ 45(5):657\u2013665","journal-title":"J Zhejiang Sci-Tech Univ"},{"issue":"12","key":"1735_CR29","doi-asserted-by":"publisher","first-page":"2860","DOI":"10.11834\/jig.200426","volume":"26","author":"X Zhao","year":"2021","unstructured":"Zhao X, Wang J, Li Y, Wang Y, Miao Z (2021) Complemented attention method for fine-grained image classification. J Image Graph 26(12):2860\u20132869","journal-title":"J Image Graph"},{"key":"1735_CR30","doi-asserted-by":"crossref","unstructured":"Zhu X, Cheng D, Zhang Z, Lin S, Dai J (2019) An empirical study of spatial attention mechanisms in deep networks. arXiv:1904.05873","DOI":"10.1109\/ICCV.2019.00679"},{"key":"1735_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/rs11202380","author":"H Liu","year":"2019","unstructured":"Liu H, Luo J, Huang B (2019) al: De-net: deep encoding network for building extraction from high-resolution remote sensing imagery. Remote Sens. https:\/\/doi.org\/10.3390\/rs11202380","journal-title":"Remote Sens"},{"issue":"1","key":"1735_CR32","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1109\/TGRS.2020.2994150","volume":"59","author":"L Ding","year":"2021","unstructured":"Ding L, Tang H, Bruzzone L (2021) Lanet: local attention embedding to improve the semantic segmentation of remote sensing image. IEEE Trans Geosci Remote Sens 59(1):426\u2013435","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"5","key":"1735_CR33","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1109\/TIP.2018.2887029","volume":"28","author":"C Guo","year":"2018","unstructured":"Guo C, Li C, Guo J, Cong R, Fu H, Han P (2018) Hierarchical features driven residual learning for depth map super-resolution. IEEE Trans Image Process 28(5):2545\u20132557","journal-title":"IEEE Trans Image Process"},{"key":"1735_CR34","doi-asserted-by":"crossref","unstructured":"Liu Y, Gross L, Li Z, Li X, Fan X, Qi W (2019) Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling. IEEE Access 7:128774\u2013128786","DOI":"10.1109\/ACCESS.2019.2940527"},{"key":"1735_CR35","doi-asserted-by":"crossref","unstructured":"Su Y, Wu Y, Wang M, Wang F, Cheng J (2019) Semantic segmentation of high resolution remote sensing image based on batch-attention mechanism. In: Proceedings of IEEE international geoscience and remote sensing symposium, Yokohama","DOI":"10.1109\/IGARSS.2019.8898198"},{"key":"1735_CR36","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y et al (2017) Deformable convolutional networks. arXiv:1703.06211","DOI":"10.1109\/ICCV.2017.89"},{"issue":"4","key":"1735_CR37","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2018","unstructured":"Chen LC, Papandreou G, Kokkinos I et al (2018) Deeplab: semantic segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1735_CR38","doi-asserted-by":"crossref","unstructured":"Chen LC, Zhu Y, Papandreou G et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the 15th European conference on computer vision, Cham","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1735_CR39","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Proceedings of IEEE conference on computer vision and pattern recognition, Las Vegas","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"1735_CR40","first-page":"51","volume":"45","author":"G Zhengsheng","year":"2020","unstructured":"Zhengsheng G, Li C, Wang Z (2020) U-shaped convolutional neural network zy-3 image path extraction method. Sci Surv Mapp 45(4):51\u201357","journal-title":"Sci Surv Mapp"},{"key":"1735_CR41","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.3390\/rs10101602","volume":"10","author":"RD Xu","year":"2018","unstructured":"Xu RD, Tao YT, Lu ZY, Zhong Y (2018) Attention-mechanism-containing neural networks for high resolution remote sensing image classification. Remote Sens 10:1602","journal-title":"Remote Sens"},{"key":"1735_CR42","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587"},{"key":"1735_CR43","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"1735_CR44","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1109\/LGRS.2018.2795531","volume":"15","author":"W Sun","year":"2018","unstructured":"Sun W, Wang R (2018) Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with dsm. IEEE Geosci Remote Sens Lett 15:474\u2013478","journal-title":"IEEE Geosci Remote Sens Lett"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01735-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01735-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01735-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T09:18:28Z","timestamp":1743412708000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01735-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,28]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["1735"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01735-2","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,28]]},"assertion":[{"value":"17 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2025","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"181"}}