{"id":"https://openalex.org/W4313169851","doi":"https://doi.org/10.1109/igarss46834.2022.9884698","title":"Learning Uncertainty-Aware Label Transition for Weakly Supervised Solar Panel Mapping with Aerial Images","display_name":"Learning Uncertainty-Aware Label Transition for Weakly Supervised Solar Panel Mapping with Aerial Images","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4313169851","doi":"https://doi.org/10.1109/igarss46834.2022.9884698"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884698","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047572962","display_name":"Jue Zhang","orcid":"https://orcid.org/0000-0003-3427-3456"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jue Zhang","raw_affiliation_strings":["School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia","institution_ids":["https://openalex.org/I31746571","https://openalex.org/I188329596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024631382","display_name":"Xiuping Jia","orcid":"https://orcid.org/0000-0001-9916-6382"},"institutions":[{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]},{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiuping Jia","raw_affiliation_strings":["School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia","institution_ids":["https://openalex.org/I188329596","https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781212","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0001-5822-8233"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["School of Information and Communication Technology, Griffith University, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, Griffith University, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075234257","display_name":"Jiankun Hu","orcid":"https://orcid.org/0000-0003-0230-1432"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I188329596","display_name":"University of Canberra","ror":"https://ror.org/04s1nv328","country_code":"AU","type":"education","lineage":["https://openalex.org/I188329596"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jiankun Hu","raw_affiliation_strings":["School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia","institution_ids":["https://openalex.org/I31746571","https://openalex.org/I188329596"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":61},"biblio":{"volume":"30","issue":null,"first_page":"1181","last_page":"1184"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Learning with Noisy Labels in Machine Learning","score":0.9976,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Learning with Noisy Labels in Machine Learning","score":0.9976,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Deep Learning in Computer Vision and Image Recognition","score":0.9967,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10627","display_name":"Image Feature Retrieval and Recognition Techniques","score":0.984,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised Learning","score":0.574125},{"id":"https://openalex.org/keywords/noisy-labels","display_name":"Noisy Labels","score":0.571429},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-Supervised Learning","score":0.543051},{"id":"https://openalex.org/keywords/robust-learning","display_name":"Robust Learning","score":0.540318},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"Transfer Learning","score":0.532345},{"id":"https://openalex.org/keywords/stochastic-matrix","display_name":"Stochastic matrix","score":0.50686795}],"concepts":[{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.6712574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6683761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6255965},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5614431},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5526739},{"id":"https://openalex.org/C49555168","wikidata":"https://www.wikidata.org/wiki/Q176583","display_name":"Stochastic matrix","level":3,"score":0.50686795},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43417335},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42981172},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42163625},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22344324},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09568736},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.09391791},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884698","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.53}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":14,"referenced_works":["https://openalex.org/W2291934802","https://openalex.org/W2494424066","https://openalex.org/W2600383743","https://openalex.org/W2904145953","https://openalex.org/W2945231726","https://openalex.org/W2962858109","https://openalex.org/W2964292098","https://openalex.org/W2998375045","https://openalex.org/W3034453930","https://openalex.org/W3082600751","https://openalex.org/W3135738366","https://openalex.org/W3165676168","https://openalex.org/W3205631735","https://openalex.org/W4206571460"],"related_works":["https://openalex.org/W4254549582","https://openalex.org/W3148934225","https://openalex.org/W3124109721","https://openalex.org/W2916152223","https://openalex.org/W2356241365","https://openalex.org/W2351648145","https://openalex.org/W2148483050","https://openalex.org/W2052845382","https://openalex.org/W2038165226","https://openalex.org/W1676609285"],"abstract_inverted_index":{"Weakly":[0],"supervised":[1,36,154],"solar":[2,12,146],"panel":[3],"mapping":[4,80,112,140,171],"has":[5],"shown":[6],"its":[7],"advantages":[8],"in":[9,34,51,132,170],"automatically":[10],"detecting":[11],"panels":[13],"from":[14],"remote":[15],"sensing":[16],"images":[17],"with":[18,82,98,150],"low":[19],"annotation":[20],"costs.":[21],"Considering":[22],"the":[23,46,99,111,122,127,133,138,163,166],"noisy":[24],"nature":[25],"of":[26,48,68,165],"pseudo":[27],"labels":[28,119],"(PLs),":[29],"which":[30],"are":[31],"frequently":[32],"employed":[33,131],"weakly":[35,153],"methods,":[37],"we":[38],"propose":[39],"to":[40,44,109],"introduce":[41],"uncertainty":[42,71,89,100],"measure":[43],"guide":[45],"estimation":[47,72],"noise":[49],"levels":[50],"PLs":[52],"and":[53,78,116,120,173],"develop":[54],"a":[55,103],"novel":[56],"method":[57,66],"based":[58],"on":[59,156],"uncertainty-aware":[60,74],"label":[61,75,104],"transition":[62,76,105,124,128],"(UALT).":[63],"The":[64],"proposed":[65,167],"consists":[67],"three":[69],"parts:":[70],"network,":[73,77],"target":[79,139],"network":[81,106,141],"forward":[83,134],"correction.":[84],"We":[85],"first":[86],"generate":[87],"heteroscedastic":[88],"by":[90],"learning":[91],"an":[92,157],"estimator":[93],"under":[94],"Bayes":[95,117],"formalism.":[96],"Then,":[97],"as":[101],"guidance,":[102],"is":[107,130],"trained":[108],"learn":[110],"between":[113],"clean":[114,143],"labels,":[115],"optimal":[118],"predict":[121],"instance-dependent":[123],"matrix.":[125],"Finally,":[126],"matrix":[129],"correction":[135],"process,":[136],"where":[137],"produces":[142],"predictions":[144],"for":[145],"panels.":[147],"Comparative":[148],"experiments":[149],"six":[151],"state-of-the-art":[152],"methods":[155],"aerial":[158],"image":[159],"data":[160],"set":[161],"show":[162],"superiority":[164],"UALT,":[168],"especially":[169],"accuracy":[172],"dis-covering":[174],"small-scale":[175],"objects.":[176]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4313169851","counts_by_year":[],"updated_date":"2024-10-26T12:54:38.594843","created_date":"2023-01-06"}