{"id":"https://openalex.org/W4386275704","doi":"https://doi.org/10.1109/tgrs.2023.3309918","title":"Semisupervised Building Instance Extraction From High-Resolution Remote Sensing Imagery","display_name":"Semisupervised Building Instance Extraction From High-Resolution Remote Sensing Imagery","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386275704","doi":"https://doi.org/10.1109/tgrs.2023.3309918"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3309918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3309918","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5100386847","display_name":"Fang Fang","orcid":"https://orcid.org/0000-0001-8969-8879"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Fang","raw_affiliation_strings":["School of Computer Science and National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China","School of Computer Science, China University of Geosciences, Wuhan, China","Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-8969-8879","affiliations":[{"raw_affiliation_string":"School of Computer Science and National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103134479","display_name":"Xu Rui","orcid":"https://orcid.org/0009-0002-1226-0945"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Xu","raw_affiliation_strings":["Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0002-1226-0945","affiliations":[{"raw_affiliation_string":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069385514","display_name":"Shengwen Li","orcid":"https://orcid.org/0000-0002-1829-4006"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengwen Li","raw_affiliation_strings":["School of Computer Science and National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China","School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-1829-4006","affiliations":[{"raw_affiliation_string":"School of Computer Science and National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101654971","display_name":"Qingyi Hao","orcid":"https://orcid.org/0009-0003-0232-8077"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyi Hao","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029397690","display_name":"Kang Zheng","orcid":"https://orcid.org/0009-0000-4552-7760"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kang Zheng","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0009-0000-4552-7760","affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001188748","display_name":"Kaishun Wu","orcid":"https://orcid.org/0000-0003-2216-0737"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaishun Wu","raw_affiliation_strings":["School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062101784","display_name":"Bo Wan","orcid":"https://orcid.org/0000-0003-2387-5419"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Wan","raw_affiliation_strings":["School of Computer Science and National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China","School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-2387-5419","affiliations":[{"raw_affiliation_string":"School of Computer Science and National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":null,"apc_paid":null,"fwci":1.4378,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.8416662,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9976000189781189,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9962999820709229,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7919490337371826},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7742643356323242},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5656813979148865},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.48929688334465027},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.46586182713508606},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3742348849773407},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3731818199157715},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3570677638053894},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3503376245498657}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7919490337371826},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7742643356323242},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5656813979148865},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.48929688334465027},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.46586182713508606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3742348849773407},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3731818199157715},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3570677638053894},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3503376245498657},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3309918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3309918","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8199999928474426,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G6142257702","display_name":null,"funder_award_id":"42071382","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7329378683","display_name":null,"funder_award_id":"KLIGIP-2023-B11","funder_id":"https://openalex.org/F8678731271","funder_display_name":"Hubei Key Laboratory of Intelligent Geo-Information Processing"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F8678731271","display_name":"Hubei Key Laboratory of Intelligent Geo-Information Processing","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1994616650","https://openalex.org/W2032227103","https://openalex.org/W2071356085","https://openalex.org/W2139577851","https://openalex.org/W2194775991","https://openalex.org/W2561726557","https://openalex.org/W2565639579","https://openalex.org/W2794187036","https://openalex.org/W2795635230","https://openalex.org/W2806581075","https://openalex.org/W2897593716","https://openalex.org/W2903078082","https://openalex.org/W2908320224","https://openalex.org/W2910489404","https://openalex.org/W2951970475","https://openalex.org/W2953070460","https://openalex.org/W2962369866","https://openalex.org/W2963150697","https://openalex.org/W2963455537","https://openalex.org/W2963849369","https://openalex.org/W2963857746","https://openalex.org/W2964159205","https://openalex.org/W2978426779","https://openalex.org/W2979805229","https://openalex.org/W2989676862","https://openalex.org/W2993182889","https://openalex.org/W3001197829","https://openalex.org/W3024506755","https://openalex.org/W3035160371","https://openalex.org/W3035358681","https://openalex.org/W3035682985","https://openalex.org/W3088431851","https://openalex.org/W3091002423","https://openalex.org/W3093600664","https://openalex.org/W3113410735","https://openalex.org/W3159162471","https://openalex.org/W3172435473","https://openalex.org/W3172507542","https://openalex.org/W3177388720","https://openalex.org/W3180799764","https://openalex.org/W3198533054","https://openalex.org/W3203892710","https://openalex.org/W4288020585","https://openalex.org/W4288325606","https://openalex.org/W4295312788","https://openalex.org/W4303980708","https://openalex.org/W4310553620","https://openalex.org/W4312548937","https://openalex.org/W4312813877","https://openalex.org/W4312984783","https://openalex.org/W4318825795","https://openalex.org/W4377022413","https://openalex.org/W4403737996","https://openalex.org/W6733814495","https://openalex.org/W6757619437","https://openalex.org/W6762913911","https://openalex.org/W6764051988","https://openalex.org/W6764322716","https://openalex.org/W6765939562","https://openalex.org/W6766978945","https://openalex.org/W6770578729","https://openalex.org/W6771378132","https://openalex.org/W6773005947","https://openalex.org/W6784930956","https://openalex.org/W6873079881"],"related_works":["https://openalex.org/W2770593030","https://openalex.org/W4281727072","https://openalex.org/W2560201613","https://openalex.org/W3154990682","https://openalex.org/W2171975302","https://openalex.org/W2022352247","https://openalex.org/W1589637664","https://openalex.org/W1532073221","https://openalex.org/W2377538627","https://openalex.org/W2107220315"],"abstract_inverted_index":{"Automatic":[0],"building":[1,55,68,172,182],"instance":[2,56,69],"extraction":[3,57,70],"from":[4,71,123,184],"high-resolution":[5],"(HR)":[6],"remote":[7],"sensing":[8],"imagery":[9],"(RSI)":[10],"is":[11,41,103,137],"crucial":[12],"for":[13,180,197],"urban":[14],"planning":[15],"and":[16,44,63,94,126,160,170,193],"mapping.":[17],"The":[18,100,117],"dominant":[19],"approaches":[20],"are":[21],"based":[22],"on":[23,147,202],"the":[24,67,75,82,88,95,107,114,134,141,154],"full-supervised":[25],"learning":[26,62],"paradigm":[27],"that":[28,59,153],"requires":[29],"a":[30,53,177,194],"large":[31],"number":[32],"of":[33,79,109,143,167,200],"labeled":[34,110,168,191],"samples":[35,111],"to":[36,65,105,112,127,139],"train":[37,128],"their":[38],"models,":[39],"which":[40],"very":[42],"time-consuming":[43],"labor-intensive.":[45],"To":[46],"alleviate":[47],"this":[48,50],"problem,":[49],"study":[51,175],"proposes":[52],"semi-supervised":[54,201],"method":[58,77,156],"integrates":[60],"teacher-student":[61],"pseudo-labeling":[64],"improve":[66],"HR":[72,185],"RSI.":[73],"Specifically,":[74],"proposed":[76,155],"consists":[78],"three":[80,148],"modules,":[81],"hybrid":[83],"data":[84,169],"augmentation":[85],"(HDA)":[86],"module,":[87],"pseudo":[89,121],"label":[90],"generation":[91],"(PLG)":[92],"module":[93,102,119,136],"contour":[96],"refinement":[97],"(CR)":[98],"module.":[99],"HDA":[101],"designed":[104,138],"enrich":[106],"diversity":[108],"optimize":[113],"teacher":[115],"model.":[116],"PLG":[118],"generates":[120],"labels":[122],"unlabeled":[124],"data,":[125],"student":[129],"model":[130],"with":[131,189],"pseudo-labels.":[132],"Finally,":[133],"CR":[135],"refine":[140],"contours":[142],"buildings.":[144],"Experimental":[145],"results":[146],"challenging":[149],"public":[150],"datasets":[151],"demonstrate":[152],"achieves":[157],"superior":[158],"performance":[159],"exhibits":[161],"great":[162],"robustness":[163],"at":[164],"different":[165,171],"proportions":[166],"scenarios.":[173],"This":[174],"provides":[176],"new":[178],"approach":[179],"extracting":[181],"instances":[183],"RSI":[186],"in":[187],"scenarios":[188],"insufficient":[190],"samples,":[192],"methodological":[195],"reference":[196],"various":[198],"applications":[199],"RSIs.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
