{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T03:53:20Z","timestamp":1771300400004,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T00:00:00Z","timestamp":1664841600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Fund of China","doi-asserted-by":"crossref","award":["62076027"],"award-info":[{"award-number":["62076027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs tremendous computational resources, which often becomes a huge burden to the remote sensing community. Undoubtedly cloud computing is one of the best choices for land cover classification, however, if not managed properly, the computation cost on the cloud could be surprisingly high. Recently, cutting the unnecessary computation <jats:italic>long tail<\/jats:italic> has become a promising solution for saving cost in the cloud. For land cover classification, it is generally not necessary to achieve the best accuracy and 85% can be regarded as a reliable land cover classification. Therefore, in this paper, we propose a framework for cost-effective remote sensing classification. Given the desired accuracy, the clustering algorithm can stop early for cost-saving whilst achieving sufficient accuracy for land cover image classification. Experimental results show that achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud computation cost for achieving a 100% accuracy. To put it into perspective, for the US land cover classification example, the proposed approach can save over $1,593,490.18 for the government in each single-use when the desired accuracy is 90%.<\/jats:p>","DOI":"10.1186\/s13677-022-00335-0","type":"journal-article","created":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T19:02:24Z","timestamp":1664910144000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Cost-effective land cover classification for remote sensing images"],"prefix":"10.1186","volume":"11","author":[{"given":"Dongwei","family":"Li","sequence":"first","affiliation":[]},{"given":"Shuliang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"He","sequence":"additional","affiliation":[]},{"given":"Yun","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,4]]},"reference":[{"key":"335_CR1","unstructured":"Buchhorn M, Smets B, Bertels L, De\u00a0Roo B, Lesiv M, Tsendbazar N-E, Li L, Tarko AJ (2021) Copernicus global land service.\u00a0https:\/\/land.copernicus.eu\/global\/products\/lc. Accessed 29 Sept 2022"},{"key":"335_CR2","doi-asserted-by":"crossref","unstructured":"Bechtel B, Conrad O, Tamminga M, Verdonck ML, Coillie V (2017) Beyond the urban mask. In: Joint Urban Remote Sensing Event (JURSE). IEEE,\u00a0Manhattan, p 1\u20134","DOI":"10.1109\/JURSE.2017.7924557"},{"key":"335_CR3","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.rse.2012.05.019","volume":"124","author":"C Alcantara","year":"2012","unstructured":"Alcantara C, Kuemmerle T, Prishchepov AV, Radeloff VC (2012) Mapping abandoned agriculture with multi-temporal modis satellite data. Remote Sens Environ 124:334\u2013347","journal-title":"Remote Sens Environ"},{"issue":"34","key":"335_CR4","doi-asserted-by":"publisher","first-page":"12947","DOI":"10.1073\/pnas.0604093103","volume":"103","author":"GP Asner","year":"2006","unstructured":"Asner GP, Broadbent EN, Oliveira PJ, Keller M, Knapp DE, Silva JN (2006) Condition and fate of logged forests in the brazilian amazon. Proc Natl Acad Sci 103(34):12947\u201312950","journal-title":"Proc Natl Acad Sci"},{"key":"335_CR5","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.landusepol.2018.09.032","volume":"80","author":"EA Glinskis","year":"2019","unstructured":"Glinskis EA, Guti\u00e9rrez-V\u00e9lez VH (2019) Quantifying and understanding land cover changes by large and small oil palm expansion regimes in the peruvian amazon. Land Use Policy 80:95\u2013106","journal-title":"Land Use Policy"},{"issue":"2","key":"335_CR6","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1109\/91.493905","volume":"4","author":"AM Bensaid","year":"1996","unstructured":"Bensaid AM, Hall LO, Bezdek JC, Clarke LP, Silbiger ML, Arrington JA, Murtagh RF (1996) Validity-guided (re) clustering with applications to image segmentation. IEEE Trans Fuzzy Syst 4(2):112\u2013123","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"6","key":"335_CR7","doi-asserted-by":"publisher","first-page":"3672","DOI":"10.1109\/TGRS.2016.2524557","volume":"54","author":"H Zhang","year":"2016","unstructured":"Zhang H, Zhai H, Zhang L, Li P (2016) Spectral-spatial sparse subspace clustering for hyperspectral remote sensing images. IEEE Trans Geosci Remote Sens 54(6):3672\u20133684","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"335_CR8","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1049\/cje.2016.05.001","volume":"25","author":"S Wang","year":"2016","unstructured":"Wang S, Wang D, Li C, Li Y, Ding G (2016) Clustering by fast search and find of density peaks with data field. Chin J Electron 25(3):397\u2013402","journal-title":"Chin J Electron"},{"issue":"5","key":"335_CR9","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1080\/01431160600746456","volume":"28","author":"D Lu","year":"2007","unstructured":"Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823\u2013870","journal-title":"Int J Remote Sens"},{"issue":"1","key":"335_CR10","doi-asserted-by":"publisher","first-page":"389","DOI":"10.5721\/EuJRS20144723","volume":"47","author":"M Li","year":"2014","unstructured":"Li M, Zang S, Zhang B, Li S, Wu C (2014) A review of remote sensing image classification techniques: the role of spatio-contextual information. Eur J Remote Sens 47(1):389\u2013411","journal-title":"Eur J Remote Sens"},{"issue":"4","key":"335_CR11","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","volume":"6","author":"T Celik","year":"2009","unstructured":"Celik T (2009) Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci Remote Sens Lett 6(4):772\u2013776","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"3","key":"335_CR12","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0031-3203(92)90114-X","volume":"25","author":"N Venkateswarlu","year":"1992","unstructured":"Venkateswarlu N, Raju P (1992) Fast isodata clustering algorithms. Pattern Recog 25(3):335\u2013342","journal-title":"Pattern Recog"},{"issue":"3","key":"335_CR13","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1109\/TGRS.2004.842108","volume":"43","author":"PR Kersten","year":"2005","unstructured":"Kersten PR, Lee JS, Ainsworth TL (2005) Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and em clustering. IEEE Trans Geosci Remote Sens 43(3):519\u2013527","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"335_CR14","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/LGRS.2012.2194770","volume":"10","author":"K Xu","year":"2013","unstructured":"Xu K, Yang W, Liu G, Sun H (2013) Unsupervised satellite image classification using markov field topic model. IEEE Geosci Remote Sens Lett 10(1):130\u2013134","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"10","key":"335_CR15","doi-asserted-by":"publisher","first-page":"1412","DOI":"10.1016\/j.cviu.2013.05.001","volume":"117","author":"Z Wang","year":"2013","unstructured":"Wang Z, Song Q, Soh YC, Sim K (2013) An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation. Comp Vision Image Underst 117(10):1412\u20131420","journal-title":"Comp Vision Image Underst"},{"issue":"2","key":"335_CR16","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s12046-011-0018-4","volume":"36","author":"B Sowmya","year":"2011","unstructured":"Sowmya B, Sheelarani B (2011) Land cover classification using reformed fuzzy c-means. Sadhana 36(2):153\u2013165","journal-title":"Sadhana"},{"issue":"2","key":"335_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3474838","volume":"13","author":"N Gao","year":"2022","unstructured":"Gao N, Xue H, Shao W, Zhao S, Qin KK, Prabowo A, Rahaman MS, Salim FD (2022) Generative adversarial networks for spatio-temporal data: A survey. ACM Trans Intell Syst Technol (TIST) 13(2):1\u201325","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"335_CR18","doi-asserted-by":"publisher","first-page":"106848","DOI":"10.1016\/j.buildenv.2020.106848","volume":"177","author":"MB Kj\u00e6rgaard","year":"2020","unstructured":"Kj\u00e6rgaard MB, Ardakanian O, Carlucci S, Dong B, Firth SK, Gao N, Huebner GM, Mahdavi A, Rahaman MS, Salim FD et al (2020) Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings. Building and Environment 177:106848","journal-title":"Building and Environment"},{"key":"335_CR19","doi-asserted-by":"crossref","unstructured":"Gao N, Marschall M, Burry J, Watkins S, Salim FD\u00a0(2022) Understanding occupants\u2019 behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables. Sci Data 9(1):1\u201316","DOI":"10.1038\/s41597-022-01347-w"},{"issue":"10","key":"335_CR20","doi-asserted-by":"publisher","first-page":"4519","DOI":"10.1109\/TII.2018.2793350","volume":"14","author":"JS Fu","year":"2018","unstructured":"Fu JS, Liu Y, Chao HC, Bhargava BK, Zhang ZJ (2018) Secure data storage and searching for industrial iot by integrating fog computing and cloud computing. IEEE Trans Ind Inform 14(10):4519\u20134528","journal-title":"IEEE Trans Ind Inform"},{"key":"335_CR21","unstructured":"(2022) Amazon web services: Ec2 instance pricing. https:\/\/aws.amazon.com\/ec2\/pricing\/on-demand\/. Accessed 27 Feb 2022"},{"key":"335_CR22","doi-asserted-by":"crossref","unstructured":"Li D, Wang S, Gao N, He Q, Yang Y (2019) Cutting the unnecessary long tail: cost-effective big data clustering in the cloud. IEEE Trans Cloud Comput\u00a010(1):292\u2013303","DOI":"10.1109\/TCC.2019.2947678"},{"key":"335_CR23","volume-title":"A land use and land cover classification system for use with remote sensor data","author":"JR Anderson","year":"1976","unstructured":"Anderson JR (1976) A land use and land cover classification system for use with remote sensor data, vol 964. US Government Printing Office, US"},{"key":"335_CR24","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel HP, Ng RT, Sander J (2000) Lof: identifying density-based local outliers. In: ACM Sigmod Record, vol\u00a029. ACM, p 93\u2013104","DOI":"10.1145\/335191.335388"},{"issue":"10","key":"335_CR25","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1016\/j.neunet.2004.07.002","volume":"17","author":"GC Cawley","year":"2004","unstructured":"Cawley GC, Talbot NL (2004) Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Netw 17(10):1467\u20131475","journal-title":"Neural Netw"},{"key":"335_CR26","volume-title":"Pattern recognition with fuzzy objective function Algorithms","author":"JC Bezdek","year":"2013","unstructured":"Bezdek JC (2013) Pattern recognition with fuzzy objective function Algorithms. Springer Science & Business Media, Logan"},{"issue":"14","key":"335_CR27","doi-asserted-by":"publisher","first-page":"2721","DOI":"10.1080\/014311698214479","volume":"19","author":"J Zhang","year":"1998","unstructured":"Zhang J, Foody G (1998) A fuzzy classification of sub-urban land cover from remotely sensed imagery. Int J Remote Sens 19(14):2721\u20132738","journal-title":"Int J Remote Sens"},{"issue":"9","key":"335_CR28","doi-asserted-by":"publisher","first-page":"3277","DOI":"10.1080\/01431161.2020.1871094","volume":"42","author":"W Zhang","year":"2021","unstructured":"Zhang W, Tang P, Zhao L (2021) Fast and accurate land-cover classification on medium-resolution remote-sensing images using segmentation models. Int J Remote Sens 42(9):3277\u20133301","journal-title":"Int J Remote Sens"},{"issue":"1","key":"335_CR29","doi-asserted-by":"publisher","first-page":"e0227438","DOI":"10.1371\/journal.pone.0227438","volume":"15","author":"C de Sousa","year":"2020","unstructured":"de Sousa C, Fatoyinbo L, Neigh C, Boucka F, Angoue V, Larsen T (2020) Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in liberia and gabon. PLoS ONE 15(1):e0227438","journal-title":"PLoS ONE"},{"key":"335_CR30","doi-asserted-by":"crossref","unstructured":"Nguyen NV, Trinh THT, Pham HT, Tran TTT, Pham LT, Nguyen CT (2020) Land cover classification based on cloud computing platform. J Southwest Jiaotong Univ 55(2)","DOI":"10.35741\/issn.0258-2724.55.2.61"},{"issue":"4","key":"335_CR31","doi-asserted-by":"publisher","first-page":"1612","DOI":"10.1109\/TNET.2019.2926142","volume":"27","author":"Y Cui","year":"2019","unstructured":"Cui Y, Dai N, Lai Z, Li M, Li Z, Hu Y, Ren K, Chen Y (2019) Tailcutter: wisely cutting tail latency in cloud cdns under cost constraints. IEEE\/ACM Trans Netw 27(4):1612\u20131628","journal-title":"IEEE\/ACM Trans Netw"},{"key":"335_CR32","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.jpdc.2018.08.011","volume":"123","author":"MGR Alam","year":"2019","unstructured":"Alam MGR, Munir MS, Uddin MZ, Alam MS, Dang TN, Hong CS (2019) Edge-of-things computing framework for cost-effective provisioning of healthcare data. J Parallel Distrib Comput 123:54\u201360","journal-title":"J Parallel Distrib Comput"},{"issue":"5","key":"335_CR33","doi-asserted-by":"publisher","first-page":"04019016","DOI":"10.1061\/(ASCE)EY.1943-7897.0000611","volume":"145","author":"W Li","year":"2019","unstructured":"Li W, Liao K, He Q, Xia Y (2019) Performance-aware cost-effective resource provisioning for future grid iot-cloud system. J Energy Eng 145(5):04019016","journal-title":"J Energy Eng"},{"issue":"7","key":"335_CR34","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1109\/TPDS.2015.2476459","volume":"27","author":"S Niu","year":"2016","unstructured":"Niu S, Zhai J, Ma X, Tang X, Chen W, Zheng W (2016) Building semi-elastic virtual clusters for cost-effective hpc cloud resource provisioning. IEEE Trans Parallel Distrib Syst 27(7):1915\u20131928","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"3","key":"335_CR35","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1109\/TPDS.2017.2773504","volume":"29","author":"Z Hu","year":"2018","unstructured":"Hu Z, Li B, Luo J (2018) Time- and cost-efficient task scheduling cross geo-distributed data centers. IEEE Trans Parallel Distrib Syst 29(3):705\u2013718","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"335_CR36","doi-asserted-by":"crossref","unstructured":"Berriman GB, Juve G, Deelman E, Regelson M, Plavchan P (2010) The application of cloud computing to astronomy: a study of cost and performance. In: 6th IEEE International Conference on E-Science Workshops. IEEE, p 1\u20137","DOI":"10.1109\/eScienceW.2010.10"},{"key":"335_CR37","doi-asserted-by":"crossref","unstructured":"Carlyle AG, Harrell SL, Smith PM (2010) Cost-effective hpc: The community or the cloud? In: IEEE 2nd International Conference on Cloud Computing Technology and Science (CloudCom). IEEE, p 169\u2013176","DOI":"10.1109\/CloudCom.2010.115"},{"issue":"6","key":"335_CR38","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1109\/TPDS.2016.2628370","volume":"28","author":"Z Wang","year":"2017","unstructured":"Wang Z, Hayat MM, Ghani N, Shaban KB (2017) Optimizing cloud-service performance: Efficient resource provisioning via optimal workload allocation. IEEE Trans Parallel Distrib Syst 28(6):1689\u20131702","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"1","key":"335_CR39","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/TPDS.2015.2398438","volume":"27","author":"K Hwang","year":"2016","unstructured":"Hwang K, Bai X, Shi Y, Li M, Chen WG, Wu Y (2016) Cloud performance modeling with benchmark evaluation of elastic scaling strategies. IEEE Trans Parallel Distrib Syst 27(1):130\u2013143","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"2","key":"335_CR40","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1109\/TCC.2015.2491920","volume":"6","author":"D Yuan","year":"2018","unstructured":"Yuan D, Cui L, Li W, Liu X, Yang Y (2018) An algorithm for finding the minimum cost of storing and regenerating datasets in multiple clouds. IEEE Trans Cloud Comput 6(2):519\u2013531","journal-title":"IEEE Trans Cloud Comput"},{"key":"335_CR41","unstructured":"Jawad M, Qureshi MB, Khan U, Ali SM, Mehmood A, Khan B, Wang X, Khan SU (2018) A robust optimization technique for energy cost minimization of cloud data centers. IEEE Trans Cloud Comput"},{"issue":"2","key":"335_CR42","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/TII.2017.2738841","volume":"14","author":"GS Aujla","year":"2017","unstructured":"Aujla GS, Kumar N, Zomaya AY, Ranjan R (2017) Optimal decision making for big data processing at edge-cloud environment: An sdn perspective. IEEE Trans Ind Inform 14(2):778\u2013789","journal-title":"IEEE Trans Ind Inform"},{"key":"335_CR43","doi-asserted-by":"crossref","unstructured":"Teerapittayanon S, McDanel B, Kung HT (2016) Branchynet: Fast inference via early exiting from deep neural networks. In: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, pp 2464\u20132469","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"335_CR44","doi-asserted-by":"publisher","first-page":"102214","DOI":"10.1016\/j.sysarc.2021.102214","volume":"118","author":"DR Torres","year":"2021","unstructured":"Torres DR, Mart\u00edn C, Rubio B, D\u00edaz M (2021) An open source framework based on kafka-ml for distributed dnn inference over the cloud-to-things continuum. J Syst Architect 118:102214","journal-title":"J Syst Architect"},{"key":"335_CR45","doi-asserted-by":"publisher","first-page":"107346","DOI":"10.1016\/j.patcog.2020.107346","volume":"105","author":"N Passalis","year":"2020","unstructured":"Passalis N, Raitoharju J, Tefas A, Gabbouj M (2020) Efficient adaptive inference for deep convolutional neural networks using hierarchical early exits. Pattern Recog 105:107346","journal-title":"Pattern Recog"},{"issue":"2\u20133","key":"335_CR46","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek JC, Ehrlich R, Full W (1984) Fcm: The fuzzy c-means clustering algorithm. Comput Geosci 10(2\u20133):191\u2013203","journal-title":"Comput Geosci"},{"issue":"1","key":"335_CR47","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. J R Stat Soc Ser C (Appl Stat) 28(1):100\u2013108","journal-title":"J R Stat Soc Ser C (Appl Stat)"},{"issue":"336","key":"335_CR48","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1080\/01621459.1971.10482356","volume":"66","author":"WM Rand","year":"1971","unstructured":"Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846\u2013850","journal-title":"J Am Stat Assoc"},{"key":"335_CR49","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.sbspro.2013.12.027","volume":"106","author":"GK Uyan\u0131k","year":"2013","unstructured":"Uyan\u0131k GK, G\u00fcler N (2013) A study on multiple linear regression analysis. Procedia-Soc Behav Sci 106:234\u2013240","journal-title":"Procedia-Soc Behav Sci"},{"key":"335_CR50","doi-asserted-by":"crossref","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat\u00a029(5):1189\u20131232","DOI":"10.1214\/aos\/1013203451"},{"key":"335_CR51","unstructured":"Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Research 1(Jun):211\u2013244"},{"issue":"3","key":"335_CR52","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw A, Wiener M et al (2002) Classification and regression by randomforest. R News 2(3):18\u201322","journal-title":"R News"},{"key":"335_CR53","unstructured":"Spacenet on amazon web services (aws) datasets (2019) https:\/\/spacenetchallenge.github.io\/datasets\/datasetHomePage.html. Accessed 28 July 2019"},{"key":"335_CR54","doi-asserted-by":"crossref","unstructured":"Van\u00a0Etten A, Hogan D, Manso JM, Shermeyer J, Weir N, Lewis R (2021) The multi-temporal urban development spacenet dataset. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.\u00a0IEEE, Manhattan, p 6398\u20136407","DOI":"10.1109\/CVPR46437.2021.00633"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-022-00335-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-022-00335-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-022-00335-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T19:11:37Z","timestamp":1664910697000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-022-00335-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,4]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["335"],"URL":"https:\/\/doi.org\/10.1186\/s13677-022-00335-0","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,4]]},"assertion":[{"value":"31 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 October 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":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"62"}}