{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T16:26:38Z","timestamp":1781972798884,"version":"3.54.5"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T00:00:00Z","timestamp":1551916800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T00:00:00Z","timestamp":1551916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-16-1-2543 (PSU no. 171570)"],"award-info":[{"award-number":["N00014-16-1-2543 (PSU no. 171570)"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007065","name":"Nvidia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s00500-019-03878-8","type":"journal-article","created":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T06:27:06Z","timestamp":1551940026000},"page":"13393-13408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event"],"prefix":"10.1007","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9240-5501","authenticated-orcid":false,"given":"Liping","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guido","family":"Cervone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,3,7]]},"reference":[{"key":"3878_CR1","first-page":"265","volume":"16","author":"M Abadi","year":"2016","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M et al (2016) Tensorflow: a system for large-scale machine learning. OSDI 16:265\u2013283","journal-title":"OSDI"},{"issue":"3","key":"3878_CR2","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175\u2013185","journal-title":"Am Stat"},{"issue":"4","key":"3878_CR3","doi-asserted-by":"publisher","first-page":"e94","DOI":"10.1371\/journal.pone.0094137","volume":"9","author":"DR Amancio","year":"2014","unstructured":"Amancio DR, Comin CH, Casanova D, Travieso G, Bruno OM, Rodrigues FA, da\u00a0Fontoura\u00a0Costa L (2014) A systematic comparison of supervised classifiers. PLoS ONE 9(4):e94\u2013137","journal-title":"PLoS ONE"},{"issue":"Dec","key":"3878_CR4","first-page":"125","volume":"2","author":"A Ben-Hur","year":"2001","unstructured":"Ben-Hur A, Horn D, Siegelmann HT, Vapnik V (2001) Support vector clustering. J Mach Learn Res 2(Dec):125\u2013137","journal-title":"J Mach Learn Res"},{"key":"3878_CR5","volume-title":"Natural language processing with Python: analyzing text with the natural language toolkit","author":"S Bird","year":"2009","unstructured":"Bird S, Klein E, Loper E (2009) Natural language processing with Python: analyzing text with the natural language toolkit. O\u2019Reilly Media, Inc., Newton"},{"key":"3878_CR6","volume-title":"Pattern recognition and machine learning","author":"MC Bishop","year":"2006","unstructured":"Bishop MC (2006) Pattern recognition and machine learning. Springer, New York"},{"issue":"2","key":"3878_CR7","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"issue":"1","key":"3878_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"3878_CR9","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511543241","volume-title":"Radial basis functions: theory and implementations","author":"MD Buhmann","year":"2003","unstructured":"Buhmann MD (2003) Radial basis functions: theory and implementations, vol 12. Cambridge University Press, Cambridge"},{"issue":"2","key":"3878_CR10","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1023\/A:1009715923555","volume":"2","author":"CJ Burges","year":"1998","unstructured":"Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121\u2013167","journal-title":"Data Min Knowl Discov"},{"key":"3878_CR11","doi-asserted-by":"crossref","unstructured":"Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 161\u2013168","DOI":"10.1145\/1143844.1143865"},{"issue":"1","key":"3878_CR12","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1080\/01431161.2015.1117684","volume":"37","author":"G Cervone","year":"2016","unstructured":"Cervone G, Sava E, Huang Q, Schnebele E, Harrison J, Waters N (2016) Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study. Int J Remote Sens 37(1):100\u2013124","journal-title":"Int J Remote Sens"},{"key":"3878_CR13","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511486579","volume-title":"Memory-based language processing","author":"W Daelemans","year":"2005","unstructured":"Daelemans W, Van den Bosch A (2005) Memory-based language processing. Cambridge University Press, Cambridge"},{"key":"3878_CR14","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. CVPR 2009, IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"10","key":"3878_CR15","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2347736.2347755","volume":"55","author":"P Domingos","year":"2012","unstructured":"Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78\u201387","journal-title":"Commun ACM"},{"key":"3878_CR16","volume-title":"The master algorithm: how the quest for the ultimate learning machine will remake our world","author":"P Domingos","year":"2015","unstructured":"Domingos P (2015) The master algorithm: how the quest for the ultimate learning machine will remake our world. Basic Books, New York"},{"issue":"2\u20133","key":"3878_CR17","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1023\/A:1007413511361","volume":"29","author":"P Domingos","year":"1997","unstructured":"Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29(2\u20133):103\u2013130","journal-title":"Mach Learn"},{"key":"3878_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-47509-7","volume-title":"Fundamentals of data mining in genomics and proteomics","author":"W Dubitzky","year":"2007","unstructured":"Dubitzky W, Granzow M, Berrar DP (2007) Fundamentals of data mining in genomics and proteomics. Springer, Berlin"},{"issue":"2","key":"3878_CR19","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL (1990) Finding structure in time. Cognit Sci 14(2):179\u2013211","journal-title":"Cognit Sci"},{"issue":"1","key":"3878_CR20","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"3878_CR21","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189\u20131232","journal-title":"Ann Stat"},{"issue":"4","key":"3878_CR22","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","volume":"27","author":"PO Gislason","year":"2006","unstructured":"Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recognit Lett 27(4):294\u2013300","journal-title":"Pattern Recognit Lett"},{"key":"3878_CR23","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","volume":"77","author":"J Gu","year":"2017","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J et al (2017) Recent advances in convolutional neural networks. Pattern Recognit 77:354","journal-title":"Pattern Recognit"},{"key":"3878_CR24","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam"},{"key":"3878_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New York","edition":"2"},{"issue":"1","key":"3878_CR26","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s10100-017-0479-6","volume":"26","author":"B Kami\u0144ski","year":"2018","unstructured":"Kami\u0144ski B, Jakubczyk M, Szufel P (2018) A framework for sensitivity analysis of decision trees. Cent Eur J Oper Res 26(1):135\u2013159","journal-title":"Cent Eur J Oper Res"},{"key":"3878_CR27","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"11","key":"3878_CR28","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"7553","key":"3878_CR29","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"2","key":"3878_CR30","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1080\/15230406.2016.1271356","volume":"45","author":"Z Li","year":"2018","unstructured":"Li Z, Wang C, Emrich CT, Guo D (2018) A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods. Cartogr Geogr Inf Sci 45(2):97\u2013110","journal-title":"Cartogr Geogr Inf Sci"},{"key":"3878_CR31","unstructured":"Liong CY, Foo SF (2013) Comparison of linear discriminant analysis and logistic regression for data classification. In: AIP conference proceedings, AIP, vol 1522, pp 1159\u20131165"},{"issue":"4","key":"3878_CR32","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1023\/A:1009744630224","volume":"2","author":"SK Murthy","year":"1998","unstructured":"Murthy SK (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Mining Knowl Discov 2(4):345\u2013389","journal-title":"Data Mining Knowl Discov"},{"key":"3878_CR33","unstructured":"Ng AY, Jordan MI (2002) On discriminative vs. generative classifiers: a comparison of logistic regression and Naive Bayes. In: Advances in neural information processing systems, pp 841\u2013848"},{"key":"3878_CR34","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1613\/jair.614","volume":"11","author":"DW Opitz","year":"1999","unstructured":"Opitz DW, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res (JAIR) 11:169\u2013198","journal-title":"J Artif Intell Res (JAIR)"},{"issue":"5","key":"3878_CR35","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1080\/01431161.2017.1400193","volume":"39","author":"G Panteras","year":"2018","unstructured":"Panteras G, Cervone G (2018) Enhancing the temporal resolution of satellite-based flood extent generation using crowdsourced data for disaster monitoring. Int J Remote Sens 39(5):1459\u20131474","journal-title":"Int J Remote Sens"},{"issue":"Oct","key":"3878_CR36","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12(Oct):2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"3","key":"3878_CR37","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","volume":"6","author":"R Polikar","year":"2006","unstructured":"Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21\u201345","journal-title":"IEEE Circuits Syst Mag"},{"issue":"364","key":"3878_CR38","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1080\/01621459.1978.10480080","volume":"73","author":"SJ Press","year":"1978","unstructured":"Press SJ, Wilson S (1978) Choosing between logistic regression and discriminant analysis. J Am Stat Assoc 73(364):699\u2013705","journal-title":"J Am Stat Assoc"},{"issue":"2\u20133","key":"3878_CR39","first-page":"271","volume":"30","author":"F Provost","year":"1998","unstructured":"Provost F, Kohavi R (1998) Glossary of terms. J Mach Learn 30(2\u20133):271\u2013274","journal-title":"J Mach Learn"},{"issue":"1","key":"3878_CR40","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81\u2013106","journal-title":"Mach Learn"},{"issue":"1\u20132","key":"3878_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","volume":"33","author":"L Rokach","year":"2010","unstructured":"Rokach L (2010) Ensemble-based classifiers. Artif Intell Rev 33(1\u20132):1\u201339","journal-title":"Artif Intell Rev"},{"key":"3878_CR42","volume-title":"Artificial intelligence: a modern approach","author":"SJ Russell","year":"2003","unstructured":"Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD (2003) Artificial intelligence: a modern approach, vol 2. Prentice Hall, Upper Saddle River"},{"issue":"3","key":"3878_CR43","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1023\/A:1009752403260","volume":"1","author":"SL Salzberg","year":"1997","unstructured":"Salzberg SL (1997) On comparing classifiers: pitfalls to avoid and a recommended approach. Data Mining Knowl Discov 1(3):317\u2013328","journal-title":"Data Mining Knowl Discov"},{"key":"3878_CR44","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809682","volume-title":"Kernel methods for pattern analysis","author":"J Shawe-Taylor","year":"2004","unstructured":"Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, Cambridge"},{"key":"3878_CR45","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"issue":"4","key":"3878_CR46","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","volume":"45","author":"M Sokolova","year":"2009","unstructured":"Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427\u2013437","journal-title":"Inf Process Manag"},{"key":"3878_CR47","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A, et\u00a0al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"3878_CR48","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"3878_CR49","unstructured":"Wainer J (2016) Comparison of 14 different families of classification algorithms on 115 binary datasets. arXiv preprint arXiv:1606.00930"},{"key":"3878_CR50","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1613\/jair.1199","volume":"19","author":"GM Weiss","year":"2003","unstructured":"Weiss GM, Provost F (2003) Learning when training data are costly: the effect of class distribution on tree induction. J Artif Intell Res 19:315\u2013354","journal-title":"J Artif Intell Res"},{"key":"3878_CR51","volume-title":"Data mining: practical machine learning tools and techniques","author":"IH Witten","year":"2011","unstructured":"Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Burlington","edition":"3"},{"issue":"7","key":"3878_CR52","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1162\/neco.1996.8.7.1341","volume":"8","author":"DH Wolpert","year":"1996","unstructured":"Wolpert DH (1996) The lack of a priori distinctions between learning algorithms. Neural Comput 8(7):1341\u20131390","journal-title":"Neural Comput"},{"key":"3878_CR53","unstructured":"Xiao T, Xia T, Yang Y, Huang C, Wang X (2015) Learning from massive noisy labeled data for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2691\u20132699"},{"issue":"2","key":"3878_CR54","doi-asserted-by":"publisher","first-page":"65","DOI":"10.3390\/ijgi7020065","volume":"7","author":"L Yang","year":"2018","unstructured":"Yang L, MacEachren AM, Mitra P, Onorati T (2018) Visually-enabled active deep learning for (geo) text and image classification: a review. ISPRS Int J Geo-Inf 7(2):65","journal-title":"ISPRS Int J Geo-Inf"},{"key":"3878_CR55","unstructured":"Zhu XX, Tuia D, Mou L, Xia GS, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: a review. arXiv preprint arXiv:1710.03959"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-019-03878-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-019-03878-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-019-03878-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T12:56:06Z","timestamp":1721048166000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-019-03878-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,7]]},"references-count":55,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["3878"],"URL":"https:\/\/doi.org\/10.1007\/s00500-019-03878-8","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,7]]},"assertion":[{"value":"7 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}