{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:38:42Z","timestamp":1783186722563,"version":"3.54.6"},"reference-count":73,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"European Research Council (ERC) through the ERC Synergy Grant Understanding and Modelling the Earth System with Machine Learning","award":["855187"],"award-info":[{"award-number":["855187"]}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Program through the Project \u201cXAIDA: Extreme Events\u2014 Artificial Intelligence for Detection and Attribution,\u201d","award":["101003469"],"award-info":[{"award-number":["101003469"]}]},{"name":"European Space Agency (ESA) AI4Science Project \u201cMulti- Hazards, Compounds and Cascade Events: DeepExtremes,\u201d","award":["2022\u20132024;"],"award-info":[{"award-number":["2022\u20132024;"]}]},{"name":"Computer Resources Provided by the J\u00fclich Supercomputing Centre","award":["PRACE-DEV-2022D01-048"],"award-info":[{"award-number":["PRACE-DEV-2022D01-048"]}]},{"name":"Computer Resources Provided by Artemisa (funded by the European Union European Regional Development Fund"},{"name":"Horizon Europe through the Project \u201cMeDiTwin: Mediterranean Digital Twin Network for Understanding Climate Extremes,\u201d"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tgrs.2024.3504340","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T19:10:33Z","timestamp":1732216233000},"page":"1-13","source":"Crossref","is-referenced-by-count":8,"title":["Deep Learning With Noisy Labels for Spatiotemporal Drought Detection"],"prefix":"10.1109","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9344-8191","authenticated-orcid":false,"given":"Jordi","family":"Cort\u00e9s-Andr\u00e9s","sequence":"first","affiliation":[{"name":"Image Processing Laboratory (IPL), Universitat de València (UV), Valencia, Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0801-199X","authenticated-orcid":false,"given":"Miguel-\u00c1ngel","family":"Fern\u00e1ndez-Torres","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Universitat de València (UV), Valencia, Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1683-2138","authenticated-orcid":false,"given":"Gustau","family":"Camps-Valls","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Universitat de València (UV), Valencia, Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change","author":"Masson-Delmotte","year":"2021"},{"key":"ref2","volume-title":"Natural Hazards","author":"Bryant","year":"2005"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1029\/2019RG000683"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/wat2.1407"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1139\/a11-013"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-019-4237-z"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1111\/nyas.13912"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaz5492"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2010.07.012"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1029\/2020JD033005"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1080\/19475705.2022.2131471"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1002\/9781119646181"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2020.3043504"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2021.3136100"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-023-04571-5"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116689"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2292894"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2023.108626"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2024.131361"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2022.105327"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3074305"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-022-03701-7"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-023-01009-4"},{"key":"ref24","article-title":"Training deep neural-networks using a noise adaptation layer","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Goldberger"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00524"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref27","first-page":"950","article-title":"A simple weight decay can improve generalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"4","author":"Krogh"},{"issue":"1","key":"ref28","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"ref31","article-title":"A survey of label-noise representation learning: Past, present and future","author":"Han","year":"2020","journal-title":"arXiv:2011.04406"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101759"},{"issue":"1","key":"ref33","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume-title":"Proc. ICML","volume":"30","author":"Maas"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86517-7_7"},{"key":"ref36","article-title":"Mind the pad\u2014CNNs can develop blind spots","author":"Alsallakh","year":"2020","journal-title":"arXiv:2010.02178"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00793"},{"key":"ref38","article-title":"Simplifying neural network training under class imbalance","author":"Shwartz-Ziv","year":"2023","journal-title":"arXiv:2312.02517"},{"key":"ref39","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref40","volume-title":"Dive Into Deep Learning","author":"Zhang","year":"2023"},{"key":"ref41","article-title":"A theoretical analysis of learning with noisily labeled data","author":"Xu","year":"2021","journal-title":"arXiv:2104.04114"},{"key":"ref42","first-page":"4313","article-title":"Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Li"},{"key":"ref43","first-page":"6543","article-title":"Normalized loss functions for deep learning with noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ma"},{"key":"ref44","article-title":"ProSelfLC: Progressive self label correction towards a low-temperature entropy state","author":"Wang","year":"2022","journal-title":"arXiv:2207.00118"},{"key":"ref45","first-page":"5907","article-title":"SELFIE: Refurbishing unclean samples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Song"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/978-3-642-35289-8_19","article-title":"Neural network classification and prior class probabilities","volume-title":"Neural Networks: Tricks of the Trade","author":"Lawrence","year":"2012"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-021-00846-6"},{"key":"ref50","first-page":"4694","article-title":"When does label smoothing help?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"M\u00fcller"},{"key":"ref51","article-title":"Regularizing neural networks by penalizing confident output distributions","author":"Pereyra","year":"2017","journal-title":"arXiv:1701.06548"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17213"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.5194\/esd-11-201-2020"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1002\/qj.3803"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-10-1903-2017"},{"key":"ref57","volume-title":"CRED\/UCLouvain","year":"2023"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1088\/1748-9326\/acfd42"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30316-5"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2013.03.004"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143874"},{"key":"ref64","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo"},{"key":"ref65","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.5194\/hess-21-6329-2017"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.5194\/nhess-12-3519-2012"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.5194\/essd-6-285-2014"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s12145-023-01215-0"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.06.031"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.04.006"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119858"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/36\/10354519\/10759777.pdf?arnumber=10759777","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T05:41:51Z","timestamp":1733550111000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10759777\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":73,"URL":"https:\/\/doi.org\/10.1109\/tgrs.2024.3504340","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"value":"0196-2892","type":"print"},{"value":"1558-0644","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}