{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T20:05:18Z","timestamp":1767989118014,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-022-01530-1","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T14:03:54Z","timestamp":1670940234000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Change Detection of Urban GIS Maps Using Multi-scale U-Net-Based Attention Neural Network Architecture"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3025-5528","authenticated-orcid":false,"given":"S.","family":"Vasavi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Bhanu","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. Jaya","family":"Sai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. Venu Gopala","family":"Rao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"1530_CR1","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1080\/014311689089039391989","volume":"10","author":"A Singh","year":"1989","unstructured":"Singh A. Digital change detection techniques using remotely-sensed data. Int J Remote Sens. 1989;10:989\u20131003. https:\/\/doi.org\/10.1080\/014311689089039391989.","journal-title":"Int J Remote Sens"},{"key":"1530_CR2","doi-asserted-by":"publisher","DOI":"10.1080\/01431160801950162","author":"JS Deng","year":"2008","unstructured":"Deng JS, Wang K, Deng YH, Qi GJ. PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. Int J Remote Sens. 2008. https:\/\/doi.org\/10.1080\/01431160801950162.","journal-title":"Int J Remote Sens"},{"key":"1530_CR3","doi-asserted-by":"publisher","DOI":"10.1080\/2150704X.2014.973998","author":"S Aiye","year":"2014","unstructured":"Aiye S, et al. Unsupervised change detection based on robust chi-squared transform for bitemporal remotely sensed images. Int J Remote Sens. 2014. https:\/\/doi.org\/10.1080\/2150704X.2014.973998.","journal-title":"Int J Remote Sens"},{"issue":"1","key":"1530_CR4","first-page":"23","volume":"19","author":"ME-H Mamdouh","year":"2016","unstructured":"Mamdouh ME-H. Applying post classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay). Egypt J Remote Sens Space Sci. 2016;19(1):23\u20136.","journal-title":"Egypt J Remote Sens Space Sci"},{"key":"1530_CR5","doi-asserted-by":"crossref","unstructured":"Jmour N, Zayen S, Abdelkrim A. Convolutional neural networks for image classification. In: 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), 2018, pp. 397\u2013402","DOI":"10.1109\/ASET.2018.8379889"},{"key":"1530_CR6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.4842","author":"C Szegedy","year":"2015","unstructured":"Szegedy C, Liu W, Jia SP, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabino-vich A. Going deeper with convolutions. Proc IEEE Conf Comput Vis Pattern Recognit. 2015. https:\/\/doi.org\/10.48550\/arXiv.1409.4842.","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"1530_CR7","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1512.03385","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proc IEEE Conf Comput Vis Pattern Recognit. 2016. https:\/\/doi.org\/10.48550\/arXiv.1512.03385.","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"1530_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/ACPR.2015.7486599","author":"S Liu","year":"2015","unstructured":"Liu S, Deng W. Very deep convolutional neural network based image classification using small training sample size. IAPR Asian Conf Pattern Recognit (ACPR). 2015. https:\/\/doi.org\/10.1109\/ACPR.2015.7486599.","journal-title":"IAPR Asian Conf Pattern Recognit (ACPR)"},{"key":"1530_CR9","unstructured":"Total and Urban Population, e-Handbook of Statistics, United Nations Conference on Trade and Development, 2020, pp 63\u20135."},{"key":"1530_CR10","unstructured":"Urban Population Set to Increase by Almost 700 million by 2030, https:\/\/www.destatis.de\/EN\/Themes\/Countries-Regions\/International-Statistics\/Data-Topic\/Population-Labour-Social-Issues\/DemographyMigration\/UrbanPopulation.html. Accessed 10 June 2021."},{"key":"1530_CR11","unstructured":"Varghese A, Jayavardhana G, Akshaya R, Balamuralidhar P. ChangeNet: a deep learning architecture for visual change detection. In: European Conference on Computer Vision, 2018, pp 1\u201311"},{"issue":"1109","key":"1530_CR12","first-page":"431","volume":"167","author":"N Ruhil","year":"2019","unstructured":"Ruhil N, Mohan S, Mitra D, Akansha K. Detection of changes from satellite images using fused difference images and hybrid Kohonen Fuzzy C-means sigma. Int Conf Comput Intell Data Sci. 2019;167(1109):431\u20139.","journal-title":"Int Conf Comput Intell Data Sci"},{"issue":"2","key":"1530_CR13","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3390\/environments6020025","volume":"6","author":"C Cao","year":"2019","unstructured":"Cao C, Dragi\u0107evi\u0107 S, Li S. Land use change detection with convolutional neural network methods. Environments. 2019;6(2):25.","journal-title":"Environments"},{"issue":"4","key":"1530_CR14","doi-asserted-by":"publisher","first-page":"2848","DOI":"10.1109\/TGRS.2019.2956756","volume":"58","author":"H Chen","year":"2020","unstructured":"Chen H, Wu C, Du B, Zhang L, Wang L. Change detection in multisource VHR images via deep Siamese convolutional multiple-layers recurrent neural network. IEEE Trans Geosci Remote Sens. 2020;58(4):2848\u201364. https:\/\/doi.org\/10.1109\/TGRS.2019.2956756.","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1530_CR15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1906.11479","author":"C Hongruixuan","year":"2019","unstructured":"Hongruixuan C, Chen W, Bo D, Liangpei Z. Change detection in multi-temporal VHR images based on deep Siamese multi-scale convolutional net-works. Arxiv. 2019. https:\/\/doi.org\/10.48550\/arXiv.1906.11479.","journal-title":"Arxiv"},{"key":"1530_CR16","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.08041","author":"W Junzheng","year":"2021","unstructured":"Junzheng W, Biao L, Yao Q, Weiping N, Han Z, Yuli S. A multiscale graph convolutional network for change detection in homogeneous and heterogeneous remote sensing images. ArXiv. 2021. https:\/\/doi.org\/10.48550\/arXiv.2102.08041.","journal-title":"ArXiv"},{"issue":"10","key":"1530_CR17","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.3390\/rs12101662","volume":"12","author":"H Chen","year":"2020","unstructured":"Chen H, Shi Z. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens. 2020;12(10):1662.","journal-title":"Remote Sens"},{"key":"1530_CR18","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6430627","author":"C Long","year":"2020","unstructured":"Long C, Dezheng Z, Peng L, Peng L. Change detection of remote sensing images based on attention mechanism. Comput Intellig Neurosci. 2020. https:\/\/doi.org\/10.1155\/2020\/6430627.","journal-title":"Comput Intellig Neurosci"},{"issue":"5","key":"1530_CR19","first-page":"905","volume":"13","author":"C Wu","year":"2021","unstructured":"Wu C, Zhang F, Xia J, Xu Y, Li G, Xie J, Du Z, Liu R. Building damage detection using u-net with attention mechanism from pre- and post-disaster. Remote Sens Datasets Remote Sens. 2021;13(5):905.","journal-title":"Remote Sens Datasets Remote Sens"},{"issue":"5","key":"1530_CR20","doi-asserted-by":"publisher","first-page":"789","DOI":"10.3390\/rs12050789","volume":"12","author":"K Li","year":"2020","unstructured":"Li K, Hu X, Jiang H, Shu Z, Zhang M. Attention-guided multi-scale segmentation neural network for interactive extraction of region objects from high-resolution satellite imagery. Remote Sens. 2020;12(5):789.","journal-title":"Remote Sens"},{"issue":"10","key":"1530_CR21","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.3390\/rs12101688","volume":"12","author":"W Shi","year":"2020","unstructured":"Shi W, Zhang M, Zhang R, Chen S, Zhan Z. Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sens. 2020;12(10):1688.","journal-title":"Remote Sens"},{"key":"1530_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/ICOEI51242.2021.9452927","author":"MB Prasad","year":"2021","unstructured":"Prasad MB, Sai PJ, Vasavi S, Joha P, Pooja P, Rao KVG. Change detection of urban GIS maps with high resolution satellite data using deep learning techniques. Int Conf Trends Electr Inform (ICOEI). 2021. https:\/\/doi.org\/10.1109\/ICOEI51242.2021.9452927.","journal-title":"Int Conf Trends Electr Inform (ICOEI)"},{"key":"1530_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2017.00-19","author":"E Rezende","year":"2017","unstructured":"Rezende E, Ruppert G, Carvalho T, Ramos F, de Geus P. Malicious software classification using transfer learning of ResNet-50 deep neural network. IEEE Int Conf Mach Learn Appl (ICMLA). 2017. https:\/\/doi.org\/10.1109\/ICMLA.2017.00-19.","journal-title":"IEEE Int Conf Mach Learn Appl (ICMLA)"},{"key":"1530_CR24","first-page":"5998","volume-title":"Advances in neural information processing systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention is all you need. In: Advances in neural information processing systems. Cham: Springer; 2017. p. 5998\u20136008."},{"key":"1530_CR25","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1023\/A:1008655427845","volume":"12","author":"AY Alfakih","year":"1999","unstructured":"Alfakih AY, Khandani A, Wolkowicz H. Solving Euclidean distance matrix completion problems via semidefinite programming. Comput Optim Appl. 1999;12:13\u201330. https:\/\/doi.org\/10.1023\/A:1008655427845.","journal-title":"Comput Optim Appl"},{"issue":"10","key":"1530_CR26","doi-asserted-by":"publisher","first-page":"11417","DOI":"10.1109\/JSEN.2020.3007883","volume":"21","author":"S Vasavi","year":"2021","unstructured":"Vasavi S, Priyadarshini NK, Harshavaradhan K. Invariant feature-based darknet architecture for moving object classification. IEEE Sens J. 2021;21(10):11417\u201326. https:\/\/doi.org\/10.1109\/JSEN.2020.3007883.","journal-title":"IEEE Sens J"},{"key":"1530_CR27","unstructured":"Murphy K. Machine learning: a probabilistic perspective. MIT; 2021."},{"key":"1530_CR28","unstructured":"Hao C. Levir change detection dataset. 2021. https:\/\/justchenhao.github.io\/LEVIR\/. Accessed 03 Jan 2021."},{"key":"1530_CR29","unstructured":"Hao L, Pratik C, Hao Y, Michael L, Avinash R, Rahul B, Stefano S. Rethinking The Hyper parameters For Fine-Tuning. In: Proceedings of ICLR, 2020, pp 1\u201320. arXiv:2002.11770"},{"key":"1530_CR30","unstructured":"xBD dataset. 2021. https:\/\/datasets.bifrost.ai\/info\/86. 03 Accessed 03 May 2021."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01530-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-022-01530-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-022-01530-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T22:31:30Z","timestamp":1673130690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-022-01530-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["1530"],"URL":"https:\/\/doi.org\/10.1007\/s42979-022-01530-1","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]},"assertion":[{"value":"3 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2022","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"90"}}