{"id":"https://openalex.org/W3003696569","doi":"https://doi.org/10.1109/lgrs.2019.2963065","title":"GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network","display_name":"GIS-Supervised Building Extraction With Label Noise-Adaptive Fully Convolutional Neural Network","publication_year":2020,"publication_date":"2020-01-30","ids":{"openalex":"https://openalex.org/W3003696569","doi":"https://doi.org/10.1109/lgrs.2019.2963065","mag":"3003696569"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2019.2963065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2963065","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"},"type":"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/A5059094652","display_name":"Zenghui Zhang","orcid":"https://orcid.org/0000-0002-1238-8538"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zenghui Zhang","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-1238-8538","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027639981","display_name":"Weiwei Guo","orcid":"https://orcid.org/0000-0001-5037-0972"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Guo","raw_affiliation_strings":["Tongji-MIT City Science Lab, Center of Digital Innovation, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5037-0972","affiliations":[{"raw_affiliation_string":"Tongji-MIT City Science Lab, Center of Digital Innovation, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100416259","display_name":"Mingjie Li","orcid":"https://orcid.org/0000-0002-1588-2654"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingjie Li","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100445293","display_name":"Wenxian Yu","orcid":"https://orcid.org/0000-0002-8741-776X"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxian Yu","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-8741-776X","affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Sensing and Recognition, Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.758,"has_fulltext":false,"cited_by_count":50,"citation_normalized_percentile":{"value":0.97293412,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"17","issue":"12","first_page":"2135","last_page":"2139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9972000122070312,"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/computer-science","display_name":"Computer science","score":0.8476797342300415},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7358357310295105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6494324803352356},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5923580527305603},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5154584050178528},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.49877405166625977},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4936422109603882},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4757518470287323},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.43412086367607117},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3895680904388428},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34528958797454834},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11617380380630493}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8476797342300415},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7358357310295105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6494324803352356},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5923580527305603},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5154584050178528},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.49877405166625977},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4936422109603882},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4757518470287323},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.43412086367607117},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3895680904388428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34528958797454834},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11617380380630493}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2019.2963065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2963065","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.5199999809265137}],"awards":[{"id":"https://openalex.org/G329494662","display_name":null,"funder_award_id":"2015M581618","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G4959341059","display_name":null,"funder_award_id":"61331015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1866072925","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1921293667","https://openalex.org/W2028104478","https://openalex.org/W2079798851","https://openalex.org/W2167460663","https://openalex.org/W2252268321","https://openalex.org/W2271840356","https://openalex.org/W2308318555","https://openalex.org/W2538244214","https://openalex.org/W2609402060","https://openalex.org/W2752971446","https://openalex.org/W2755226765","https://openalex.org/W2790741584","https://openalex.org/W2919115771","https://openalex.org/W2939647427","https://openalex.org/W2973562770","https://openalex.org/W6637373629","https://openalex.org/W6639331287","https://openalex.org/W6639824700","https://openalex.org/W6640298173","https://openalex.org/W6691441656","https://openalex.org/W6694517276","https://openalex.org/W6743885473"],"related_works":["https://openalex.org/W4239286941","https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1966421350","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Automatic":[0],"building":[1,33,49,64,104,208],"extraction":[2,238],"from":[3,98,240],"aerial":[4],"or":[5],"satellite":[6],"images":[7,96],"is":[8,37,133],"a":[9,19,28,59,90,111,121,226],"dense":[10],"pixel":[11],"prediction":[12],"task":[13,87],"for":[14,32,103,207,221,234],"many":[15],"applications.":[16],"It":[17],"demands":[18],"large":[20],"number":[21],"of":[22,52,62,88,120,149,157,161,192],"clean":[23],"label":[24,65,95,101,113,142,201],"data":[25,45,51,102,175,202],"to":[26,40,73,94,135,177,229],"train":[27],"deep":[29,91],"neural":[30,92,116,194],"network":[31,93,117,123,164],"extraction.":[34,105,209],"But":[35],"it":[36,224],"labor":[38],"expensive":[39],"collect":[41],"such":[42,99],"pixel-wise":[43,97],"annotated":[44],"manually.":[46],"Fortunately,":[47],"the":[48,76,86,137,140,144,150,158,162,166,190,199,235],"footprint":[50],"geographic":[53],"information":[54],"system":[55],"(GIS)":[56],"maps":[57,78,206],"provide":[58],"cheap":[60],"way":[61],"generating":[63],"data,":[66],"but":[67],"these":[68],"labels":[69,187],"are":[70],"imperfect":[71],"due":[72],"misalignment":[74],"between":[75,139],"GIS":[77,205],"and":[79,143,188],"images.":[80,243],"In":[81],"this":[82,107],"letter,":[83],"we":[84,109],"consider":[85],"learning":[89],"noisy":[100,145,186,200],"To":[106],"end,":[108],"propose":[110],"general":[112],"noise-adaptive":[114],"(NA)":[115],"framework":[118],"consisting":[119],"base":[122],"followed":[124],"by":[125,165,204],"an":[126],"additional":[127],"probability":[128],"transition":[129],"modular":[130],"(PTM)":[131],"which":[132],"introduced":[134],"capture":[136],"relationship":[138],"true":[141],"label.":[146],"The":[147,210],"parameters":[148],"PTM":[151,182,220],"can":[152,183],"be":[153],"estimated":[154],"as":[155],"part":[156],"training":[159],"process":[160],"whole":[163],"off-the-shelf":[167],"backpropagation":[168],"algorithm.":[169],"We":[170],"conduct":[171],"experiments":[172],"on":[173,198],"real-world":[174],"set":[176],"demonstrate":[178],"that":[179,214],"our":[180,218],"proposed":[181,219],"better":[184],"handle":[185],"improve":[189],"performance":[191],"convolutional":[193],"networks":[195],"(CNNs)":[196],"trained":[197],"generated":[203],"experimental":[211],"results":[212],"indicate":[213],"being":[215],"armed":[216],"with":[217],"fully":[222],"CNN,":[223],"provides":[225],"promising":[227],"solution":[228],"reduce":[230],"manual":[231],"annotation":[232],"effort":[233],"labor-expensive":[236],"object":[237],"tasks":[239],"remote":[241],"sensing":[242]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
