{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:41:36Z","timestamp":1776976896038,"version":"3.51.4"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T00:00:00Z","timestamp":1709596800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T00:00:00Z","timestamp":1709596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Road extraction from remote-sensing images is of great significance for vehicle navigation and emergency insurance. However, the road information extracted in the remote-sensing image is discontinuous because the road in the image is often obscured by the shadows of trees or buildings. Moreover, due to the scale difference of roads in remote-sensing images, it remains a computational challenge to extract small-size roads from remote-sensing images. To address those problems, we propose a road extraction method based on adaptive global feature fusion (AGF-Net). First, a dilated convolution strip attention (DCSA) module is designed from the encoder\u2013decoder structure. It consists of the dilated convolution and the strip attention module, which adaptively emphasizes relevant features in vertical and horizontal directions. Then, multiple global feature fusion modules (GFFM) in the skip connection are designed to supplement the decoder with road detail features, and we design a multi-scale strip convolution module (MSCM) to implement the GFFM module to obtain multi-scale road information. We compare AGF-Net to state-of-the-art methods and report their performance using standard evaluation metrics, including Intersection over Union (IoU), <jats:italic>F<\/jats:italic>1-score, precision, and recall. Our proposed AGF-Net achieves higher accuracy compared to other existing methods on the Massachusetts Road Dataset, DeepGlobe Road Dataset, CHN6-CUG Road Dataset, and BJRoad Dataset. The IoU obtained on these datasets are 0.679, 0.673, 0.567, and 0.637, respectively.<\/jats:p>","DOI":"10.1007\/s40747-024-01364-9","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T20:01:41Z","timestamp":1709668901000},"page":"4311-4328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["AGF-Net: adaptive global feature fusion network for road extraction from remote-sensing images"],"prefix":"10.1007","volume":"10","author":[{"given":"Yajuan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Lan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yunhe","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wenjia","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,5]]},"reference":[{"key":"1364_CR1","doi-asserted-by":"crossref","unstructured":"Kaack LH, Chen GH, Morgan MG (2019) Truck traffic monitoring with satellite images. In: Proceedings of the 2nd ACM SIGCAS conference on computing and sustainable societies, pp 155\u2013164","DOI":"10.1145\/3314344.3332480"},{"issue":"12","key":"1364_CR2","doi-asserted-by":"publisher","first-page":"4394","DOI":"10.3390\/s22124394","volume":"22","author":"AR Javed","year":"2022","unstructured":"Javed AR, Hassan MA, Shahzad F, Ahmed W, Singh S, Baker T, Gadekallu TR (2022) Integration of blockchain technology and federated learning in vehicular (iot) networks: a comprehensive survey. Sensors 22(12):4394","journal-title":"Sensors"},{"issue":"9","key":"1364_CR3","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.3390\/rs10091461","volume":"10","author":"Y Xu","year":"2018","unstructured":"Xu Y, Xie Z, Feng Y, Chen Z (2018) Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sens 10(9):1461","journal-title":"Remote Sens"},{"issue":"4","key":"1364_CR4","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/LGRS.2018.2878771","volume":"16","author":"Y Li","year":"2018","unstructured":"Li Y, Guo L, Rao J, Xu L, Jin S (2018) Road segmentation based on hybrid convolutional network for high-resolution visible remote sensing image. IEEE Geosci Remote Sens Lett 16(4):613\u2013617","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1364_CR5","doi-asserted-by":"crossref","unstructured":"Hassan MA, Javed R, Granelli F, Gen X, Rizwan M, Ali SH, Junaid H, Ullah S et\u00a0al (2023) Intelligent transportation systems in smart city: a systematic survey. In: 2023 international conference on robotics and automation in industry (ICRAI). IEEE, pp 1\u20139","DOI":"10.1109\/ICRAI57502.2023.10089543"},{"issue":"9","key":"1364_CR6","doi-asserted-by":"publisher","first-page":"0138071","DOI":"10.1371\/journal.pone.0138071","volume":"10","author":"B Liu","year":"2015","unstructured":"Liu B, Wu H, Wang Y, Liu W (2015) Main road extraction from zy-3 grayscale imagery based on directional mathematical morphology and vgi prior knowledge in urban areas. PLoS ONE 10(9):0138071","journal-title":"PLoS ONE"},{"issue":"9","key":"1364_CR7","doi-asserted-by":"publisher","first-page":"1444","DOI":"10.3390\/rs12091444","volume":"12","author":"A Abdollahi","year":"2020","unstructured":"Abdollahi A, Pradhan B, Shukla N, Chakraborty S, Alamri A (2020) Deep learning approaches applied to remote sensing datasets for road extraction: a state-of-the-art review. Remote Sens 12(9):1444","journal-title":"Remote Sens"},{"key":"1364_CR8","doi-asserted-by":"publisher","first-page":"5489","DOI":"10.1109\/JSTARS.2020.3023549","volume":"13","author":"R Lian","year":"2020","unstructured":"Lian R, Wang W, Mustafa N, Huang L (2020) Road extraction methods in high-resolution remote sensing images: a comprehensive review. IEEE J Sel Top Appl Earth Obs Remote Sens 13:5489\u20135507","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"1364_CR9","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:1312.4400"},{"key":"1364_CR10","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, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1364_CR11","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2014MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1364_CR12","doi-asserted-by":"crossref","unstructured":"Chaurasia A, Culurciello E (2017) Linknet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE visual communications and image processing (VCIP). IEEE, pp 1\u20134","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"1364_CR13","doi-asserted-by":"crossref","unstructured":"Zhou L, Zhang C, Wu M (2018) D-linknet: linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 182\u2013186","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"1364_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/JSTARS.2020.3042816","volume":"14","author":"Q Wu","year":"2020","unstructured":"Wu Q, Luo F, Wu P, Wang B, Yang H, Wu Y (2020) Automatic road extraction from high-resolution remote sensing images using a method based on densely connected spatial feature-enhanced pyramid. IEEE J Sel Top Appl Earth Obs Remote Sens 14:3\u201317","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"12","key":"1364_CR15","doi-asserted-by":"publisher","first-page":"571","DOI":"10.3390\/ijgi8120571","volume":"8","author":"Y Xie","year":"2019","unstructured":"Xie Y, Miao F, Zhou K, Peng J (2019) Hsgnet: a road extraction network based on global perception of high-order spatial information. ISPRS Int J Geo Inf 8(12):571","journal-title":"ISPRS Int J Geo Inf"},{"issue":"4","key":"1364_CR16","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.3390\/rs15041049","volume":"15","author":"X Liu","year":"2023","unstructured":"Liu X, Wang Z, Wan J, Zhang J, Xi Y, Liu R, Miao Q (2023) Roadformer: road extraction using a swin transformer combined with a spatial and channel separable convolution. Remote Sens 15(4):1049","journal-title":"Remote Sens"},{"issue":"6","key":"1364_CR17","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.3390\/rs15061602","volume":"15","author":"J Tao","year":"2023","unstructured":"Tao J, Chen Z, Sun Z, Guo H, Leng B, Yu Z, Wang Y, He Z, Lei X, Yang J (2023) Seg-road: a segmentation network for road extraction based on transformer and cnn with connectivity structures. Remote Sens 15(6):1602","journal-title":"Remote Sens"},{"key":"1364_CR18","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1364_CR19","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, vol 30"},{"key":"1364_CR20","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117\u20132125","DOI":"10.1109\/CVPR.2017.106"},{"issue":"4","key":"1364_CR21","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, 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":"1364_CR22","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122"},{"key":"1364_CR23","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.ins.2020.05.062","volume":"535","author":"M Lan","year":"2020","unstructured":"Lan M, Zhang Y, Zhang L, Du B (2020) Global context based automatic road segmentation via dilated convolutional neural network. Inf Sci 535:156\u2013171","journal-title":"Inf Sci"},{"issue":"5","key":"1364_CR24","doi-asserted-by":"publisher","first-page":"552","DOI":"10.3390\/rs11050552","volume":"11","author":"L Gao","year":"2019","unstructured":"Gao L, Song W, Dai J, Chen Y (2019) Road extraction from high-resolution remote sensing imagery using refined deep residual convolutional neural network. Remote Sens 11(5):552","journal-title":"Remote Sens"},{"key":"1364_CR25","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062"},{"key":"1364_CR26","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587"},{"key":"1364_CR27","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"8","key":"1364_CR28","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.3390\/rs15081978","volume":"15","author":"Z Tong","year":"2023","unstructured":"Tong Z, Li Y, Zhang J, He L, Gong Y (2023) Msfanet: multiscale fusion attention network for road segmentation of multispectral remote sensing data. Remote Sens 15(8):1978","journal-title":"Remote Sens"},{"key":"1364_CR29","volume":"114","author":"X Chen","year":"2022","unstructured":"Chen X, Sun Q, Guo W, Qiu C, Yu A (2022) Ga-net: a geometry prior assisted neural network for road extraction. Int J Appl Earth Obs Geoinf 114:103004","journal-title":"Int J Appl Earth Obs Geoinf"},{"issue":"8","key":"1364_CR30","doi-asserted-by":"publisher","first-page":"4068","DOI":"10.3390\/app12084068","volume":"12","author":"S Qu","year":"2022","unstructured":"Qu S, Zhou H, Zhang B, Liang S (2022) Mspnet: multi-scale strip pooling network for road extraction from remote sensing images. Appl Sci 12(8):4068","journal-title":"Appl Sci"},{"key":"1364_CR31","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman\u00a0Siddiquee MM, Tajbakhsh N, Liang J (2018) Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop, DLMIA 2018, and 8th international workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. Springer, pp 3\u201311","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"1364_CR32","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y-W, Wu J (2020) Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1055\u20131059","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"issue":"4","key":"1364_CR33","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/0924-2716(95)98233-P","volume":"50","author":"A Gruen","year":"1995","unstructured":"Gruen A, Li H (1995) Road extraction from aerial and satellite images by dynamic programming. ISPRS J Photogramm Remote Sens 50(4):11\u201320","journal-title":"ISPRS J Photogramm Remote Sens"},{"issue":"7","key":"1364_CR34","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1109\/34.506793","volume":"18","author":"M Barzohar","year":"1996","unstructured":"Barzohar M, Cooper DB (1996) Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. IEEE Trans Pattern Anal Mach Intell 18(7):707\u2013721","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1364_CR35","doi-asserted-by":"crossref","unstructured":"Anil P, Natarajan S (2010) A novel approach using active contour model for semi-automatic road extraction from high resolution satellite imagery. In: 2010 second international conference on machine learning and computing. IEEE, pp 263\u2013266","DOI":"10.1109\/ICMLC.2010.36"},{"issue":"1","key":"1364_CR36","doi-asserted-by":"publisher","first-page":"063610","DOI":"10.1117\/1.JRS.6.063610","volume":"6","author":"M Ronggui","year":"2012","unstructured":"Ronggui M, Weixing W, Sheng L (2012) Extracting roads based on retinex and improved canny operator with shape criteria in vague and unevenly illuminated aerial images. J Appl Remote Sens 6(1):063610","journal-title":"J Appl Remote Sens"},{"key":"1364_CR37","doi-asserted-by":"crossref","unstructured":"Mnih V, Hinton GE (2010) Learning to detect roads in high-resolution aerial images. In: Computer vision\u2014ECCV 2010: 11th European conference on computer vision, Heraklion, Crete, Greece, September 5\u201311, 2010, Proceedings, Part VI 11. Springer, pp 210\u2013223","DOI":"10.1007\/978-3-642-15567-3_16"},{"issue":"5","key":"1364_CR38","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett 15(5):749\u2013753","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1364_CR39","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, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"9","key":"1364_CR40","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.3390\/rs10091461","volume":"10","author":"Y Xu","year":"2018","unstructured":"Xu Y, Xie Z, Feng Y, Chen Z (2018) Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sens 10(9):1461","journal-title":"Remote Sens"},{"key":"1364_CR41","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1364_CR42","doi-asserted-by":"publisher","first-page":"8540","DOI":"10.1109\/TIP.2021.3117076","volume":"30","author":"J Mei","year":"2021","unstructured":"Mei J, Li R-J, Gao W, Cheng M-M (2021) Coanet: connectivity attention network for road extraction from satellite imagery. IEEE Trans Image Process 30:8540\u20138552","journal-title":"IEEE Trans Image Process"},{"key":"1364_CR43","first-page":"1","volume":"19","author":"S-B Chen","year":"2021","unstructured":"Chen S-B, Ji Y-X, Tang J, Luo B, Wang W-Q, Lv K (2021) Dbranet: road extraction by dual-branch encoder and regional attention decoder. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"12","key":"1364_CR44","doi-asserted-by":"publisher","first-page":"10243","DOI":"10.1109\/TGRS.2020.3034011","volume":"59","author":"L Ding","year":"2020","unstructured":"Ding L, Bruzzone L (2020) Diresnet: direction-aware residual network for road extraction in VHR remote sensing images. IEEE Trans Geosci Remote Sens 59(12):10243\u201310254","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1364_CR45","first-page":"1","volume":"19","author":"Y Wang","year":"2021","unstructured":"Wang Y, Seo J, Jeon T (2021) Nl-linknet: toward lighter but more accurate road extraction with nonlocal operations. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"1364_CR46","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.isprsjprs.2021.03.008","volume":"175","author":"X Lu","year":"2021","unstructured":"Lu X, Zhong Y, Zheng Z, Zhang L (2021) Gamsnet: globally aware road detection network with multi-scale residual learning. ISPRS J Photogramm Remote Sens 175:340\u2013352","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1364_CR47","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.isprsjprs.2021.03.016","volume":"175","author":"Q Zhu","year":"2021","unstructured":"Zhu Q, Zhang Y, Wang L, Zhong Y, Guan Q, Lu X, Zhang L, Li D (2021) A global context-aware and batch-independent network for road extraction from VHR satellite imagery. ISPRS J Photogramm Remote Sens 175:353\u2013365","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1364_CR48","doi-asserted-by":"publisher","first-page":"6302","DOI":"10.1109\/JSTARS.2021.3083055","volume":"14","author":"J Wan","year":"2021","unstructured":"Wan J, Xie Z, Xu Y, Chen S, Qiu Q (2021) Da-roadnet: a dual-attention network for road extraction from high resolution satellite imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 14:6302\u20136315","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"7","key":"1364_CR49","doi-asserted-by":"publisher","first-page":"3511","DOI":"10.3390\/app12073511","volume":"12","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Miao C, Liu C, Tian Q (2022) Dcs-transupernet: road segmentation network based on cswin transformer with dual resolution. Appl Sci 12(7):3511","journal-title":"Appl Sci"},{"key":"1364_CR50","first-page":"1","volume":"19","author":"L Luo","year":"2022","unstructured":"Luo L, Wang J-X, Chen S-B, Tang J, Luo B (2022) Bdtnet: road extraction by bi-direction transformer from remote sensing images. IEEE Geosci Remote Sens Lett 19:1\u20135","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"21","key":"1364_CR51","doi-asserted-by":"publisher","first-page":"5476","DOI":"10.3390\/rs14215476","volume":"14","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Sun X, Liu Y (2022) Gmr-net: road-extraction network based on fusion of local and global information. Remote Sens 14(21):5476","journal-title":"Remote Sens"},{"issue":"21","key":"1364_CR52","doi-asserted-by":"publisher","first-page":"5342","DOI":"10.3390\/rs14215342","volume":"14","author":"Y Jie","year":"2022","unstructured":"Jie Y, He H, Xing K, Yue A, Tan W, Yue C, Jiang C, Chen X (2022) Meca-net: a multiscale feature encoding and long-range context-aware network for road extraction from remote sensing images. Remote Sens 14(21):5342","journal-title":"Remote Sens"},{"key":"1364_CR53","first-page":"1","volume":"60","author":"Y Wang","year":"2022","unstructured":"Wang Y, Peng Y, Li W, Alexandropoulos GC, Yu J, Ge D, Xiang W (2022) Ddu-net: dual-decoder-u-net for road extraction using high-resolution remote sensing images. IEEE Trans Geosci Remote Sens 60:1\u201312","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"1364_CR54","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1007\/s13369-022-07082-z","volume":"48","author":"SD Khan","year":"2023","unstructured":"Khan SD, Alarabi L, Basalamah S (2023) Dsmsa-net: deep spatial and multi-scale attention network for road extraction in high spatial resolution satellite images. Arab J Sci Eng 48(2):1907\u20131920","journal-title":"Arab J Sci Eng"},{"key":"1364_CR55","first-page":"1","volume":"61","author":"L Dai","year":"2023","unstructured":"Dai L, Zhang G, Zhang R (2023) Radanet: road augmented deformable attention network for road extraction from complex high-resolution remote-sensing images. IEEE Trans Geosci Remote Sens 61:1\u201313","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1364_CR56","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder\u2013decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"1364_CR57","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Garcia-Rodriguez J (2017) A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"1364_CR58","doi-asserted-by":"crossref","unstructured":"Yuan H, Wang S, Bao Z, Wang S (2023) Automatic road extraction with multi-source data revisited: completeness, smoothness and discrimination. Proc VLDB Endow 16(11):3004\u20133017","DOI":"10.14778\/3611479.3611504"},{"issue":"4","key":"1364_CR59","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1109\/TKDE.2015.2507581","volume":"28","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Hsueh Y-L, Lee W-C, Jhang Y-H (2015) Efficient cache-supported path planning on roads. IEEE Trans Knowl Data Eng 28(4):951\u2013964","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1364_CR60","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TNNLS.2020.3015660","volume":"32","author":"T Wang","year":"2020","unstructured":"Wang T, Zhao Y, Wang J, Somani AK, Sun C (2020) Attention-based road registration for gps-denied uas navigation. IEEE Trans Neural Netw Learn Syst 32(4):1788\u20131800","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"1364_CR61","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1109\/TNNLS.2020.3039675","volume":"33","author":"Q Wang","year":"2020","unstructured":"Wang Q, Han T, Qin Z, Gao J, Li X (2020) Multitask attention network for lane detection and fitting. IEEE Trans Neural Netw Learn Syst 33(3):1066\u20131078","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01364-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01364-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01364-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T18:28:07Z","timestamp":1715884087000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01364-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,5]]},"references-count":61,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1364"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01364-9","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,5]]},"assertion":[{"value":"28 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}