{"id":"https://openalex.org/W4386602361","doi":"https://doi.org/10.3390/rs15184432","title":"A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework","display_name":"A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework","publication_year":2023,"publication_date":"2023-09-08","ids":{"openalex":"https://openalex.org/W4386602361","doi":"https://doi.org/10.3390/rs15184432"},"language":"en","primary_location":{"id":"doi:10.3390/rs15184432","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15184432","pdf_url":"https://www.mdpi.com/2072-4292/15/18/4432/pdf?version=1694419161","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/18/4432/pdf?version=1694419161","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100327178","display_name":"Yanjun Wang","orcid":"https://orcid.org/0000-0002-3317-6518"},"institutions":[{"id":"https://openalex.org/I121296143","display_name":"Hunan University of Science and Technology","ror":"https://ror.org/02m9vrb24","country_code":"CN","type":"education","lineage":["https://openalex.org/I121296143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanjun Wang","raw_affiliation_strings":["National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China","School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"],"affiliations":[{"raw_affiliation_string":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China","institution_ids":["https://openalex.org/I121296143"]},{"raw_affiliation_string":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China","institution_ids":["https://openalex.org/I121296143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055961257","display_name":"Yunhao Lin","orcid":"https://orcid.org/0000-0002-6373-4576"},"institutions":[{"id":"https://openalex.org/I121296143","display_name":"Hunan University of Science and Technology","ror":"https://ror.org/02m9vrb24","country_code":"CN","type":"education","lineage":["https://openalex.org/I121296143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunhao Lin","raw_affiliation_strings":["National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China","School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"],"affiliations":[{"raw_affiliation_string":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China","institution_ids":["https://openalex.org/I121296143"]},{"raw_affiliation_string":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China","institution_ids":["https://openalex.org/I121296143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052548451","display_name":"Huiqing Huang","orcid":"https://orcid.org/0000-0001-5540-2523"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huiqing Huang","raw_affiliation_strings":["Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410118, China","The Third Surveying and Mapping Institute of Hunan Province, Changsha 410118, China"],"affiliations":[{"raw_affiliation_string":"Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410118, China","institution_ids":[]},{"raw_affiliation_string":"The Third Surveying and Mapping Institute of Hunan Province, Changsha 410118, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101762421","display_name":"Shuhan Wang","orcid":"https://orcid.org/0009-0009-4722-3889"},"institutions":[{"id":"https://openalex.org/I121296143","display_name":"Hunan University of Science and Technology","ror":"https://ror.org/02m9vrb24","country_code":"CN","type":"education","lineage":["https://openalex.org/I121296143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuhan Wang","raw_affiliation_strings":["National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China","School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"],"affiliations":[{"raw_affiliation_string":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China","institution_ids":["https://openalex.org/I121296143"]},{"raw_affiliation_string":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China","institution_ids":["https://openalex.org/I121296143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091578581","display_name":"Shicheng Wen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210132195","display_name":"Guangdong Province Environmental Monitoring Center","ror":"https://ror.org/03gfeke93","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210132195"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shicheng Wen","raw_affiliation_strings":["Hunan Provincial Natural Resources Survey and Monitoring Center, Changsha 410118, China","The Second Survey and Mapping Institute of Hunan Province, Changsha 410118, China"],"affiliations":[{"raw_affiliation_string":"Hunan Provincial Natural Resources Survey and Monitoring Center, Changsha 410118, China","institution_ids":["https://openalex.org/I4210132195"]},{"raw_affiliation_string":"The Second Survey and Mapping Institute of Hunan Province, Changsha 410118, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010831428","display_name":"Hengfan Cai","orcid":"https://orcid.org/0000-0001-9820-7533"},"institutions":[{"id":"https://openalex.org/I121296143","display_name":"Hunan University of Science and Technology","ror":"https://ror.org/02m9vrb24","country_code":"CN","type":"education","lineage":["https://openalex.org/I121296143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengfan Cai","raw_affiliation_strings":["National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China","School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China"],"affiliations":[{"raw_affiliation_string":"National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China","institution_ids":["https://openalex.org/I121296143"]},{"raw_affiliation_string":"School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China","institution_ids":["https://openalex.org/I121296143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100327178"],"corresponding_institution_ids":["https://openalex.org/I121296143"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.4814,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.60411609,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"15","issue":"18","first_page":"4432","last_page":"4432"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9922999739646912,"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"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9922999739646912,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9919999837875366,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9876999855041504,"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":0.7168048024177551},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6286674737930298},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5798646807670593},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5338588356971741},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5142964720726013},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47558701038360596},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4487782418727875},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42608627676963806},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3998102843761444}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7168048024177551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6286674737930298},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5798646807670593},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5338588356971741},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5142964720726013},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47558701038360596},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4487782418727875},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42608627676963806},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3998102843761444},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15184432","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15184432","pdf_url":"https://www.mdpi.com/2072-4292/15/18/4432/pdf?version=1694419161","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a53d2fa05bee4a3dab2a2ed550dca768","is_oa":true,"landing_page_url":"https://doaj.org/article/a53d2fa05bee4a3dab2a2ed550dca768","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 18, p 4432 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/18/4432/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15184432","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15184432","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15184432","pdf_url":"https://www.mdpi.com/2072-4292/15/18/4432/pdf?version=1694419161","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1356514951","display_name":null,"funder_award_id":"41971","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4827696944","display_name":null,"funder_award_id":"41971423","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7727344685","display_name":null,"funder_award_id":"31972951","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"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4386602361.pdf"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W1964335437","https://openalex.org/W2010731097","https://openalex.org/W2113464037","https://openalex.org/W2119363183","https://openalex.org/W2134670479","https://openalex.org/W2333549353","https://openalex.org/W2527802235","https://openalex.org/W2600061660","https://openalex.org/W2617536493","https://openalex.org/W2620773907","https://openalex.org/W2737129951","https://openalex.org/W2746791238","https://openalex.org/W2759832987","https://openalex.org/W2770429219","https://openalex.org/W2791969626","https://openalex.org/W2884585870","https://openalex.org/W2893801697","https://openalex.org/W2908159312","https://openalex.org/W2964309882","https://openalex.org/W3000627240","https://openalex.org/W3003341045","https://openalex.org/W3035524453","https://openalex.org/W3040988483","https://openalex.org/W3049655825","https://openalex.org/W3122028341","https://openalex.org/W3126696000","https://openalex.org/W3132612813","https://openalex.org/W3138481289","https://openalex.org/W3157994709","https://openalex.org/W3177541758","https://openalex.org/W3207809295","https://openalex.org/W3209582755","https://openalex.org/W3213181250","https://openalex.org/W4200142374","https://openalex.org/W4206007731","https://openalex.org/W4206841460","https://openalex.org/W4210697906","https://openalex.org/W4224269597","https://openalex.org/W4224882649","https://openalex.org/W4229002315","https://openalex.org/W4281633835","https://openalex.org/W4286384837","https://openalex.org/W4293215018","https://openalex.org/W4313260933","https://openalex.org/W4313627848","https://openalex.org/W4316664561","https://openalex.org/W4324144346","https://openalex.org/W4382519818","https://openalex.org/W6677065643","https://openalex.org/W6679792166","https://openalex.org/W6840448520","https://openalex.org/W7039198002"],"related_works":["https://openalex.org/W3016928466","https://openalex.org/W4389574804","https://openalex.org/W4375867731","https://openalex.org/W2936725271","https://openalex.org/W3150655618","https://openalex.org/W2295788148","https://openalex.org/W1578717197","https://openalex.org/W3108295644","https://openalex.org/W2114282491","https://openalex.org/W4315434538"],"abstract_inverted_index":{"Deep":[0],"learning":[1,314],"has":[2],"gained":[3],"widespread":[4],"interest":[5],"in":[6,43,51,115,159,253,273,312],"the":[7,35,44,67,73,81,84,90,94,101,107,129,133,140,163,166,169,177,186,190,195,199,203,206,210,217,243,254,260,268,281,293],"task":[8],"of":[9,26,80,83,89,103,109,132,144,156,165,168,176,221,230,288,322],"building":[10,123,240],"semantic":[11,218],"segmentation":[12,137,219],"modelling":[13],"using":[14],"remote":[15,134],"sensing":[16,135],"images;":[17],"however,":[18],"neural":[19],"network":[20],"models":[21,36],"require":[22,318],"a":[23,121,285,319],"large":[24,286,320],"number":[25,287,321],"training":[27,45,48,113,323],"samples":[28,49,70,92,111,155,179,188,192,197,213,246,290],"to":[29,40,99,161,182,259,279,295,307],"achieve":[30],"better":[31],"classification":[32,53,64,124,142,270,310],"performance,":[33],"and":[34,86,105,139,184,193,209,226,242,250,299],"are":[37,214],"more":[38],"sensitive":[39],"error":[41],"patches":[42],"samples.":[46,201,263,324],"The":[47,228,264],"obtained":[50],"semi-supervised":[52,63,122,269],"methods":[54,311],"need":[55],"less":[56,78],"reliable":[57],"weakly":[58],"labelled":[59],"samples,":[60,171,205,208,298],"but":[61],"current":[62],"research":[65],"puts":[66],"generated":[68],"weak":[69,91,110,153,170,178,187,200,207,212,245,262,297],"directly":[71],"into":[72,216],"model":[74,96,138,220,315],"for":[75,223,284],"applications,":[76],"with":[77,189],"consideration":[79],"impact":[82],"accuracy":[85,224,256],"quality":[87,108,164],"improvement":[88],"on":[93,128,237],"subsequent":[95],"classification.":[97],"Therefore,":[98],"address":[100],"problem":[102],"generating":[104],"optimising":[106],"from":[112,198],"data":[114],"deep":[116,313],"learning,":[117],"this":[118,149,231,274],"paper":[119,150,275],"proposes":[120],"framework.":[125],"Firstly,":[126],"based":[127],"test":[130,255],"results":[131,143,265],"image":[136,154],"unsupervised":[141],"LiDAR":[145],"point":[146],"cloud":[147],"data,":[148],"quickly":[151],"generates":[152],"buildings.":[157],"Secondly,":[158],"order":[160],"improve":[162],"spots":[167],"an":[172,305],"iterative":[173],"optimisation":[174],"strategy":[175],"is":[180],"proposed":[181,272],"compare":[183],"analyse":[185],"real":[191,204],"extract":[194],"accurate":[196],"Finally,":[202],"optimised":[211,244],"input":[215],"buildings":[222],"evaluation":[225],"analysis.":[227],"effectiveness":[229],"paper\u2019s":[232],"approach":[233],"was":[234],"experimentally":[235],"verified":[236],"two":[238],"different":[239],"datasets,":[241],"improved":[247],"by":[248],"1.9%":[249],"0.6%,":[251],"respectively,":[252],"mIoU":[257],"compared":[258],"initial":[261],"demonstrate":[266],"that":[267,317],"framework":[271],"can":[276,301],"be":[277,302],"used":[278,303],"alleviate":[280],"model\u2019s":[282],"demand":[283],"real-labelled":[289],"while":[291],"improving":[292],"ability":[294],"utilise":[296],"it":[300],"as":[304],"alternative":[306],"fully":[308],"supervised":[309],"applications":[316]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
