{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T13:18:45Z","timestamp":1781097525979,"version":"3.54.1"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001656","name":"Helmholtz-Gemeinschaft","doi-asserted-by":"publisher","award":["MUDS"],"award-info":[{"award-number":["MUDS"]}],"id":[{"id":"10.13039\/501100001656","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["ERC-2016-StG-714087"],"award-info":[{"award-number":["ERC-2016-StG-714087"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f?r Bildung und Forschung","doi-asserted-by":"publisher","award":["01DD20001"],"award-info":[{"award-number":["01DD20001"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tgrs.2023.3336357","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T18:48:45Z","timestamp":1702406925000},"page":"1-11","source":"Crossref","is-referenced-by-count":9,"title":["Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance in Earth Observation?"],"prefix":"10.1109","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8214-4723","authenticated-orcid":false,"given":"Christoph","family":"Koller","sequence":"first","affiliation":[{"name":"Department of Data Science in Earth Observation, Technical University Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00f6ran","family":"Kauermann","sequence":"additional","affiliation":[{"name":"Department of Statistics, LMU Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8107-9096","authenticated-orcid":false,"given":"Xiao Xiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Data Science in Earth Observation, Technical University Munich, Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2292894"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2545658"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3054390"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363518"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2715223"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/rs13040755"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-11-00019.1"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2020.2964708"},{"key":"ref9","first-page":"4","article-title":"Towards computation of urban local climate zones (LCZ) from openstreetmap data","volume-title":"Proc. 14th Int. Conf. GeoComputation","author":"Samsonov"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.proenv.2014.09.002"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3390\/atmos5040755"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1002\/joc.3746"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/cli6010005"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2016.11.006"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3354\/cr01220"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2019.101487"},{"key":"ref17","first-page":"20","article-title":"An introduction to the WUDAPT project","volume-title":"Proc. 9th Int. Conf. Urban Climate","author":"Mills"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2539977"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi4010199"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2018.10.001"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2016.04.001"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.09.009"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2017.05.017"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi7090379"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2019.05.004"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.2995711"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2953497"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS39084.2020.9324234"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS39084.2020.9324427"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2021.112794"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/rs13132511"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.3047447"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2021.08.001"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10562-9"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-05946-3"},{"key":"ref36","first-page":"1","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lakshminarayanan"},{"key":"ref37","first-page":"7047","article-title":"Predictive uncertainty estimation via prior networks","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Malinin"},{"key":"ref38","first-page":"3183","article-title":"Evidential deep learning to quantify classification uncertainty","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Sensoy"},{"key":"ref39","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gal"},{"key":"ref40","first-page":"1613","article-title":"Weight uncertainty in neural network","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Blundell"},{"key":"ref41","first-page":"13153","article-title":"A simple baseline for Bayesian uncertainty in deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Maddox"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3140324"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/igarss39084.2020.9323890"},{"issue":"3","key":"ref44","first-page":"61","article-title":"Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods","volume":"10","author":"Platt","year":"1999","journal-title":"Adv. Large Margin Classifiers"},{"key":"ref45","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015","journal-title":"arXiv:1503.02531"},{"key":"ref46","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo"},{"key":"ref47","article-title":"Attended temperature scaling: A practical approach for calibrating deep neural networks","author":"Mozafari","year":"2018","journal-title":"arXiv:1810.11586"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref49","article-title":"When does label smoothing help?","author":"M\u00fcller","year":"2019","journal-title":"arXiv:1906.02629"},{"key":"ref50","first-page":"6448","article-title":"Does label smoothing mitigate label noise?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lukasik"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00396"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00971"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18946-z"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3089942"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1613\/jair.606"},{"key":"ref56","article-title":"Deep learning is robust to massive label noise","author":"Rolnick","year":"2017","journal-title":"arXiv:1705.10694"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3390\/rs12223836"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/igarss46834.2022.9884691"},{"key":"ref59","first-page":"12","article-title":"Local climate zones: Origins, development, and application to urban heat island studies","volume-title":"Proc. Annu. Meeting Amer. Assoc. Geographers","author":"Stewart"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1175\/BAMS-D-16-0236.1"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.2307\/2987588"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102430"},{"issue":"7","key":"ref63","first-page":"1","article-title":"Measuring calibration in deep learning","volume-title":"Proc. CVPR Workshops","volume":"2","author":"Nixon"},{"key":"ref64","volume-title":"Keras","author":"Chollet","year":"2015"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.3403\/30310507"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/10354519\/10354049.pdf?arnumber=10354049","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T23:43:55Z","timestamp":1705103035000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10354049\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":65,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2023.3336357","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}