{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:01:01Z","timestamp":1776441661168,"version":"3.51.2"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42201077"],"award-info":[{"award-number":["42201077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021QD074"],"award-info":[{"award-number":["ZR2021QD074"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M732105"],"award-info":[{"award-number":["2023M732105"]}],"id":[{"id":"10.13039\/501100002858","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":[[2025,9]]},"DOI":"10.1007\/s40747-025-01998-3","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:20:55Z","timestamp":1752139255000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mixed multi-branch feature fusion model for efficient automatic building extraction from high-resolution remote sensing images"],"prefix":"10.1007","volume":"11","author":[{"given":"Yaohui","family":"Liu","sequence":"first","affiliation":[]},{"given":"Shuzhe","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xinkai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Taoyi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hu","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Su","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"1998_CR1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.comnet.2015.12.023","volume":"101","author":"MM Rathore","year":"2016","unstructured":"Rathore MM, Ahmad A, Paul A, Rho S (2016) Urban planning and Building smart cities based on the internet of things using big data analytics. Comput Netw 101:63\u201380. https:\/\/doi.org\/10.1016\/j.comnet.2015.12.023","journal-title":"Comput Netw"},{"key":"1998_CR2","doi-asserted-by":"publisher","first-page":"112636","DOI":"10.1016\/j.rse.2021.112636","volume":"265","author":"Z Zheng","year":"2021","unstructured":"Zheng Z, Zhong Y, Wang J et al (2021) Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: from natural disasters to man-made disasters. Remote Sens Environ 265:112636. https:\/\/doi.org\/10.1016\/j.rse.2021.112636","journal-title":"Remote Sens Environ"},{"key":"1998_CR3","doi-asserted-by":"publisher","first-page":"7369","DOI":"10.1109\/TGRS.2018.2850972","volume":"56","author":"S Xu","year":"2018","unstructured":"Xu S, Pan X, Li E et al (2018) Automatic Building rooftop extraction from aerial images via hierarchical RGB-D priors. IEEE Trans Geosci Remote Sens 56:7369\u20137387. https:\/\/doi.org\/10.1109\/TGRS.2018.2850972","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR4","unstructured":"Simonyan K, Zisserman A (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition"},{"key":"1998_CR5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and, Recognition P, CVPR) (2016) (. IEEE, Las Vegas, NV, USA, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1998_CR6","unstructured":"Howard AG, Zhu M, Chen B et al (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"},{"key":"1998_CR7","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net. Convolutional Networks for Biomedical Image Segmentation","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1998_CR8","doi-asserted-by":"crossref","unstructured":"Chen L-C, Papandreou G, Kokkinos I et al (2017) DeepLab: semantic image segmentation with deep convolutional Nets. Atrous Convolution, and Fully Connected CRFs","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"1998_CR9","doi-asserted-by":"crossref","unstructured":"Zhao H, Shi J, Qi X et al (2016) Pyramid Scene Parsing Network","DOI":"10.1109\/CVPR.2017.660"},{"key":"1998_CR10","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R et al (2017) Feature Pyramid Networks for Object Detection","DOI":"10.1109\/CVPR.2017.106"},{"key":"1998_CR11","unstructured":"Vaswani A, Shazeer N, Parmar N et al Attention is All you Need"},{"key":"1998_CR12","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A et al (2021) An image is worth 16x16 words. Transformers for Image Recognition at Scale"},{"key":"1998_CR13","doi-asserted-by":"crossref","unstructured":"Zheng S, Lu J, Zhao H et al (2021) Rethinking semantic segmentation from a sequence. -to-Sequence Perspective with Transformers","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"1998_CR14","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/978-3-030-58452-8_13","volume-title":"Computer Vision\u2013 ECCV 2020","author":"N Carion","year":"2020","unstructured":"Carion N, Massa F, Synnaeve G et al (2020) End-to-End object detection with Transformers. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer Vision\u2013 ECCV 2020. Springer International Publishing, Cham, pp 213\u2013229"},{"key":"1998_CR15","unstructured":"Jiang Y, Chang S, Wang Z (2021) TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up"},{"key":"1998_CR16","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1007\/978-3-030-58517-4_31","volume-title":"Computer Vision\u2013 ECCV 2020","author":"Y Zeng","year":"2020","unstructured":"Zeng Y, Fu J, Chao H (2020) Learning joint Spatial-Temporal transformations for video inpainting. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer Vision\u2013 ECCV 2020. Springer International Publishing, Cham, pp 528\u2013543"},{"key":"1998_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3144894","volume":"60","author":"C Zhang","year":"2022","unstructured":"Zhang C, Jiang W, Zhang Y et al (2022) Transformer and CNN hybrid deep neural network for semantic segmentation of Very-High-Resolution remote sensing imagery. IEEE Trans Geosci Remote Sens 60:1\u201320. https:\/\/doi.org\/10.1109\/TGRS.2022.3144894","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR18","unstructured":"Xia C, Wang X, Lv F, ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions"},{"key":"1998_CR19","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y et al (2021) Swin transformer. Hierarchical Vision Transformer using Shifted Windows","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1998_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3142279","volume":"19","author":"X Chen","year":"2022","unstructured":"Chen X, Qiu C, Guo W et al (2022) Multiscale feature learning by transformer for Building extraction from satellite images. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2022.3142279","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1998_CR21","doi-asserted-by":"publisher","first-page":"108705","DOI":"10.1016\/j.compbiomed.2024.108705","volume":"178","author":"MA Pfeffer","year":"2024","unstructured":"Pfeffer MA, Ling SSH, Wong JKW (2024) Exploring the frontier: Transformer-based models in EEG signal analysis for brain-computer interfaces. Comput Biol Med 178:108705. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108705","journal-title":"Comput Biol Med"},{"key":"1998_CR22","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1109\/TGRS.2019.2954461","volume":"58","author":"S Wei","year":"2020","unstructured":"Wei S, Ji S, Lu M (2020) Toward automatic Building footprint delineation from aerial images using CNN and regularization. IEEE Trans Geosci Remote Sens 58:2178\u20132189. https:\/\/doi.org\/10.1109\/TGRS.2019.2954461","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3152575","volume":"60","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Chen Z, Wang B et al (2022) BOMSC-Net: boundary optimization and Multi-Scale context awareness based Building extraction from High-Resolution remote sensing imagery. IEEE Trans Geosci Remote Sens 60:1\u201317. https:\/\/doi.org\/10.1109\/TGRS.2022.3152575","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR24","doi-asserted-by":"publisher","first-page":"269","DOI":"10.3390\/rs14020269","volume":"14","author":"Y Wang","year":"2022","unstructured":"Wang Y, Zeng X, Liao X, Zhuang D (2022) B-FGC-Net: A Building extraction network from high resolution remote sensing imagery. Remote Sens 14:269. https:\/\/doi.org\/10.3390\/rs14020269","journal-title":"Remote Sens"},{"key":"1998_CR25","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1109\/JSTARS.2021.3139017","volume":"15","author":"S Chen","year":"2022","unstructured":"Chen S, Shi W, Zhou M et al (2022) CGSANet: A Contour-Guided and local Structure-Aware Encoder\u2013Decoder network for accurate Building extraction from very High-Resolution remote sensing imagery. IEEE J Sel Top Appl Earth Observations Remote Sens 15:1526\u20131542. https:\/\/doi.org\/10.1109\/JSTARS.2021.3139017","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"key":"1998_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2023.3272353","volume":"20","author":"H Lin","year":"2023","unstructured":"Lin H, Hao M, Luo W et al (2023) BEARNet: A novel buildings Edge-Aware refined network for Building extraction from High-Resolution remote sensing images. IEEE Geosci Remote Sens Lett 20:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2023.3272353","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1998_CR27","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie E, Wang W, Yu Z et al (2021) SegFormer: simple and efficient design for semantic segmentation with Transformers. Adv Neur in 34:12077\u201312090","journal-title":"Adv Neur in"},{"key":"1998_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3093977","volume":"60","author":"R Li","year":"2022","unstructured":"Li R, Zheng S, Zhang C et al (2022) Multiattention network for semantic segmentation of Fine-Resolution remote sensing images. IEEE Trans Geosci Remote Sens 60:1\u201313. https:\/\/doi.org\/10.1109\/TGRS.2021.3093977","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3144165","volume":"60","author":"X He","year":"2022","unstructured":"He X, Zhou Y, Zhao J et al (2022) Swin transformer embedding UNet for remote sensing image semantic segmentation. IEEE Trans Geosci Remote Sens 60:1\u201315. https:\/\/doi.org\/10.1109\/TGRS.2022.3144165","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3314641","volume":"61","author":"H Wu","year":"2023","unstructured":"Wu H, Huang P, Zhang M et al (2023) CMTFNet: CNN and multiscale transformer fusion network for Remote-Sensing image semantic segmentation. IEEE Trans Geosci Remote Sens 61:1\u201312. https:\/\/doi.org\/10.1109\/TGRS.2023.3314641","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3476975","volume":"62","author":"H Chang","year":"2024","unstructured":"Chang H, Bi H, Li F et al (2024) Deep symmetric fusion transformer for multimodal remote sensing data classification. IEEE Trans Geosci Remote Sens 62:1\u201315. https:\/\/doi.org\/10.1109\/TGRS.2024.3476975","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR32","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.isprsjprs.2024.01.022","volume":"209","author":"Y Li","year":"2024","unstructured":"Li Y, Hong D, Li C et al (2024) HD-Net: High-resolution decoupled network for Building footprint extraction via deeply supervised body and boundary decomposition. ISPRS J Photogrammetry Remote Sens 209:51\u201365. https:\/\/doi.org\/10.1016\/j.isprsjprs.2024.01.022","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"1998_CR33","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.3390\/rs13061176","volume":"13","author":"C Zhang","year":"2021","unstructured":"Zhang C, Jiang W, Zhao Q (2021) Semantic segmentation of aerial imagery via Split-Attention networks with disentangled nonlocal and edge supervision. Remote Sens 13:1176. https:\/\/doi.org\/10.3390\/rs13061176","journal-title":"Remote Sens"},{"key":"1998_CR34","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards Real-Time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39:1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1998_CR35","doi-asserted-by":"crossref","unstructured":"Xiong Y, Liao R, Zhao H et al (2019) UPSNet: A Unified Panoptic Segmentation Network. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, CA, USA, pp 8810\u20138818","DOI":"10.1109\/CVPR.2019.00902"},{"key":"1998_CR36","doi-asserted-by":"crossref","unstructured":"Fan C-M, Liu T-J, Liu K-H (2022) Compound Multi-. branch Feature Fusion for Real Image Restoration","DOI":"10.1109\/ICIP49359.2023.10222907"},{"key":"1998_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2024.3361211","volume":"62","author":"J Li","year":"2024","unstructured":"Li J, He W, Cao W et al (2024) UANet: an Uncertainty-Aware network for Building extraction from remote sensing images. IEEE Trans Geosci Remote Sens 62:1\u201313. https:\/\/doi.org\/10.1109\/TGRS.2024.3361211","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR38","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J (2019) Deep High-Resolution Representation Learning for Human Pose Estimation","DOI":"10.1109\/CVPR.2019.00584"},{"key":"1998_CR39","doi-asserted-by":"publisher","first-page":"123616","DOI":"10.1016\/j.eswa.2024.123616","volume":"249","author":"Y Gao","year":"2024","unstructured":"Gao Y, Luo X, Gao X et al (2024) Semantic segmentation of remote sensing images based on multiscale features and global information modeling. Expert Syst Appl 249:123616. https:\/\/doi.org\/10.1016\/j.eswa.2024.123616","journal-title":"Expert Syst Appl"},{"key":"1998_CR40","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1109\/JSTARS.2022.3141826","volume":"15","author":"W Chen","year":"2022","unstructured":"Chen W, Ouyang S, Tong W et al (2022) GCSANet: A global context Spatial attention deep learning network for remote sensing scene classification. IEEE J Sel Top Appl Earth Observations Remote Sens 15:1150\u20131162. https:\/\/doi.org\/10.1109\/JSTARS.2022.3141826","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"key":"1998_CR41","doi-asserted-by":"publisher","first-page":"3109","DOI":"10.3390\/rs14133109","volume":"14","author":"R Liu","year":"2022","unstructured":"Liu R, Tao F, Liu X et al (2022) RAANet: A residual ASPP with attention framework for semantic segmentation of High-Resolution remote sensing images. Remote Sens 14:3109. https:\/\/doi.org\/10.3390\/rs14133109","journal-title":"Remote Sens"},{"key":"1998_CR42","doi-asserted-by":"publisher","first-page":"6169","DOI":"10.1109\/TGRS.2020.3026051","volume":"59","author":"Q Zhu","year":"2021","unstructured":"Zhu Q, Liao C, Hu H et al (2021) MAP-Net: multiple attending path neural network for Building footprint extraction from remote sensed imagery. IEEE Trans Geosci Remote Sens 59:6169\u20136181. https:\/\/doi.org\/10.1109\/TGRS.2020.3026051","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3185641","volume":"19","author":"L Bai","year":"2022","unstructured":"Bai L, Lin X, Ye Z et al (2022) MsanlfNet: semantic segmentation network with multiscale attention and nonlocal filters for High-Resolution remote sensing images. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2022.3185641","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1998_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2021.3063381","volume":"19","author":"R Li","year":"2022","unstructured":"Li R, Zheng S, Duan C et al (2022) Multistage attention ResU-Net for semantic segmentation of Fine-Resolution remote sensing images. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2021.3063381","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1998_CR45","unstructured":"Cheng B, Wei Y, Feris R et al (2018) Decoupled classification refinement. Hard False Positive Suppression for Object Detection"},{"key":"1998_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2024.3397851","volume":"21","author":"X Li","year":"2024","unstructured":"Li X, Xu F, Li L et al (2024) AAFormer: Attention-Attended transformer for semantic segmentation of remote sensing images. IEEE Geosci Remote Sens Lett 21:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2024.3397851","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1998_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3186634","volume":"60","author":"L Wang","year":"2022","unstructured":"Wang L, Fang S, Meng X, Li R (2022) Building extraction with vision transformer. IEEE Trans Geosci Remote Sens 60:1\u201311. https:\/\/doi.org\/10.1109\/TGRS.2022.3186634","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1998_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3215200","volume":"19","author":"X Meng","year":"2022","unstructured":"Meng X, Yang Y, Wang L et al (2022) Class-Guided Swin transformer for semantic segmentation of remote sensing imagery. IEEE Geosci Remote Sens Lett 19:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2022.3215200","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1998_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2024.3403088","volume":"21","author":"J Liu","year":"2024","unstructured":"Liu J, Cheng S, Du A (2024) ER-Swin: feature enhancement and refinement network based on Swin transformer for semantic segmentation of remote sensing images. IEEE Geosci Remote Sens Lett 21:1\u20135. https:\/\/doi.org\/10.1109\/LGRS.2024.3403088","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1998_CR50","doi-asserted-by":"publisher","unstructured":"Liu Z, Hu H, Lin Y et al (2021) Swin transformer V2: scaling up capacity and resolution. https:\/\/doi.org\/10.48550\/ARXIV.2111.09883","DOI":"10.48550\/ARXIV.2111.09883"},{"key":"1998_CR51","doi-asserted-by":"crossref","unstructured":"Zamir SW, Arora A, Khan S et al (2021) Multi-Stage Progressive Image Restoration. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Nashville, TN, USA, pp 14816\u201314826","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"1998_CR52","unstructured":"Mnih V Machine Learning for Aerial Image Labeling"},{"key":"1998_CR53","doi-asserted-by":"crossref","unstructured":"Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2017) Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, Fort Worth, TX, pp 3226\u20133229","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"1998_CR54","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.3390\/rs10081195","volume":"10","author":"G Wu","year":"2018","unstructured":"Wu G, Guo Z, Shi X et al (2018) A boundary regulated network for accurate roof segmentation and outline extraction. Remote Sens 10:1195. https:\/\/doi.org\/10.3390\/rs10081195","journal-title":"Remote Sens"},{"key":"1998_CR55","doi-asserted-by":"crossref","unstructured":"Liu Y, Yang X, Li J et al (2022) Building sampling and labeling dataset of UAV images in rural China. China Scientific Data.","DOI":"10.11922\/noda.2021.0010.zh"},{"key":"1998_CR56","unstructured":"Gu A, Dao T, Mamba Linear-Time Sequence Modeling with Selective State Spaces"},{"key":"1998_CR57","unstructured":"Zhu L, Liao B, Zhang Q et al (2024) Vision Mamba. Efficient Visual Representation Learning with Bidirectional State Space Model"},{"key":"1998_CR58","unstructured":"Liu Y, Tian Y, Zhao Y et al (2024) VMamba: Visual State Space Model"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01998-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01998-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01998-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T01:46:05Z","timestamp":1757209565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01998-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"references-count":58,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["1998"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01998-3","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"7 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"377"}}