{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T20:46:51Z","timestamp":1768423611833,"version":"3.49.0"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T00:00:00Z","timestamp":1551398400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Klaus Tschira Foundation, Heidelberg"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2019,3]]},"DOI":"10.1109\/tgrs.2018.2868748","type":"journal-article","created":{"date-parts":[[2018,10,3]],"date-time":"2018-10-03T18:16:43Z","timestamp":1538590603000},"page":"1713-1722","source":"Crossref","is-referenced-by-count":32,"title":["Deep Learning From Multiple Crowds: A Case Study of Humanitarian Mapping"],"prefix":"10.1109","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8423-555X","authenticated-orcid":false,"given":"Jiaoyan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Zipf","sequence":"additional","affiliation":[]},{"given":"Hongchao","family":"Fan","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7952579"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2540798"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2011.2139193"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2008.2010404"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-06498-7_19"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/COMGEO.2013.4"},{"key":"ref36","author":"xie","year":"2015","journal-title":"Transfer learning from deep features for remote sensing and poverty mapping"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2017.2703920"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749964"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/BF00993277"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.04.017"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2013.867495"},{"key":"ref12","author":"gal","year":"2017","journal-title":"Deep bayesian active learning with image data"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.spasta.2012.03.002"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MPRV.2008.80"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1968.sp008455"},{"key":"ref17","first-page":"162","article-title":"Erkennung von Fu&#x00DF;g&#x00E4;ngerstreifen aus orthophotos","volume":"2","author":"keller","year":"2016","journal-title":"The Journal of AGI"},{"key":"ref18","author":"kingma","year":"2014","journal-title":"Adam A method for stochastic optimization"},{"key":"ref19","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","first-page":"441","article-title":"Toward optimal active learning through sampling estimation of error reduction","author":"roy","year":"2001","journal-title":"Proc ICML"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2700322"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702294"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116828"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2014.2309695"},{"key":"ref29","author":"simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition"},{"key":"ref5","article-title":"Deep learning with satellite images and volunteered geographic information","author":"chen","year":"2017","journal-title":"Geospatial Data Science Techniques and Applications"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7729193"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2731997"},{"key":"ref2","first-page":"3812","article-title":"Deep learning quadcopter control via risk-aware active learning","author":"andersson","year":"2017","journal-title":"Proc AAAI"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2601622"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/rs8100859"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1553\/giscience2017_02_s173"},{"key":"ref22","first-page":"261","article-title":"Learning algorithms for classification: A comparison on handwritten digit recognition","author":"lecun","year":"1995","journal-title":"Neural Networks The Statistical Mechanics Perspective"},{"key":"ref21","author":"lecun","year":"2015","journal-title":"Lenet-5 convolutional neural networks"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/rs10030471"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2598859"},{"key":"ref26","first-page":"567","article-title":"Learning to label aerial images from noisy data","author":"mnih","year":"2012","journal-title":"Proc 29th Int Conf Mach Learn (ICML)"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2612821"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/8651575\/08480860.pdf?arnumber=8480860","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T20:58:46Z","timestamp":1657745926000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8480860\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3]]},"references-count":41,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tgrs.2018.2868748","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3]]}}}