{"id":"https://openalex.org/W7115701768","doi":"https://doi.org/10.1109/lgrs.2025.3645558","title":"MSA-GAN: Multistructure Adaptive Generative Adversarial Network for Semi-Supervised Remote Sensing Road Extraction","display_name":"MSA-GAN: Multistructure Adaptive Generative Adversarial Network for Semi-Supervised Remote Sensing Road Extraction","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7115701768","doi":"https://doi.org/10.1109/lgrs.2025.3645558"},"language":null,"primary_location":{"id":"doi:10.1109/lgrs.2025.3645558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3645558","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":null,"display_name":"Yuanyuan Dang","orcid":null},"institutions":[{"id":"https://openalex.org/I4385474403","display_name":"Changchun University of Technology","ror":"https://ror.org/052pakb34","country_code":"CN","type":"education","lineage":["https://openalex.org/I4385474403"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Dang","raw_affiliation_strings":["College of Computer Science and Engineering, Changchun University of Technology, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Changchun University of Technology, Changchun, China","institution_ids":["https://openalex.org/I4385474403"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenhao Liu","orcid":"https://orcid.org/0009-0005-1254-9185"},"institutions":[{"id":"https://openalex.org/I4385474403","display_name":"Changchun University of Technology","ror":"https://ror.org/052pakb34","country_code":"CN","type":"education","lineage":["https://openalex.org/I4385474403"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhao Liu","raw_affiliation_strings":["College of Computer Science and Engineering, Changchun University of Technology, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0005-1254-9185","affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Changchun University of Technology, Changchun, China","institution_ids":["https://openalex.org/I4385474403"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bing Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I4385474403","display_name":"Changchun University of Technology","ror":"https://ror.org/052pakb34","country_code":"CN","type":"education","lineage":["https://openalex.org/I4385474403"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bing Liu","raw_affiliation_strings":["College of Computer Science and Engineering, Changchun University of Technology, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Changchun University of Technology, Changchun, China","institution_ids":["https://openalex.org/I4385474403"]}]},{"author_position":"last","author":{"id":null,"display_name":"Hao Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4385474403","display_name":"Changchun University of Technology","ror":"https://ror.org/052pakb34","country_code":"CN","type":"education","lineage":["https://openalex.org/I4385474403"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Li","raw_affiliation_strings":["College of Computer Science and Engineering, Changchun University of Technology, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Engineering, Changchun University of Technology, Changchun, China","institution_ids":["https://openalex.org/I4385474403"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4385474403"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45654899,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"23","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9911999702453613,"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":0.9911999702453613,"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":0.0015999999595806003,"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":0.0008999999845400453,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5705000162124634},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.5651999711990356},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5408999919891357},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.517799973487854},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5066999793052673},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4885999858379364},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.48809999227523804},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.475600004196167},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47279998660087585},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42309999465942383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8206999897956848},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.613099992275238},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5705000162124634},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.5651999711990356},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.517799973487854},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5066999793052673},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4885999858379364},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.475600004196167},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44279998540878296},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42309999465942383},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.3804999887943268},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3612000048160553},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3546000123023987},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.3515999913215637},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.3476000130176544},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.30399999022483826},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2025.3645558","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3645558","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":[{"id":"https://metadata.un.org/sdg/10","score":0.704441487789154,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2804199516","https://openalex.org/W2893801697","https://openalex.org/W2982628450","https://openalex.org/W2995808743","https://openalex.org/W3034502973","https://openalex.org/W3034552520","https://openalex.org/W3040304705","https://openalex.org/W4312310512","https://openalex.org/W4327695706","https://openalex.org/W4391559729","https://openalex.org/W4392499125","https://openalex.org/W4399268501","https://openalex.org/W4399432762","https://openalex.org/W4400579361","https://openalex.org/W4401261302","https://openalex.org/W4401900946","https://openalex.org/W4403791406","https://openalex.org/W4409331503","https://openalex.org/W4409852203"],"related_works":[],"abstract_inverted_index":{"To":[0],"address":[1],"the":[2,147],"challenges":[3],"of":[4,149],"incomplete":[5],"pseudo-labels":[6],"and":[7,36,40,50,58,70,76,92,103,109,123,129,140,152],"complex":[8],"background":[9],"interference":[10],"in":[11,138,156],"semi-supervised":[12,157],"remote":[13],"sensing":[14],"road":[15,52,158],"extraction,":[16],"we":[17],"propose":[18],"a":[19,29,37,83,93,115],"Multi-Structure":[20],"Adaptive":[21],"Generative":[22],"Adversarial":[23],"Network":[24],"(MSA-GAN).":[25],"The":[26,80],"generator":[27],"integrates":[28],"Wavelet":[30],"Attention":[31],"Feature":[32,95],"Fusion":[33],"Module":[34,43,86,97],"(WAFFM)":[35],"Multi-scale":[38],"Context":[39],"Detail":[41],"Enhancement":[42],"(MCDEM)":[44],"to":[45,61,88,99],"extract":[46],"hierarchical":[47],"structural":[48,102],"cues":[49],"preserve":[51],"continuity.":[53],"WAFFM":[54],"combines":[55],"wavelet-based":[56],"decomposition":[57],"directional":[59,90],"attention":[60],"enhance":[62],"edge":[63],"connectivity,":[64],"while":[65],"MCDEM":[66],"aggregates":[67],"contextual":[68],"semantics":[69],"local":[71],"details":[72],"via":[73],"strip":[74],"pooling":[75],"enhanced":[77],"atrous":[78],"convolutions.":[79],"discriminator":[81],"incorporates":[82],"Rectangular":[84],"Self-Calibration":[85],"(RCM)":[87],"capture":[89],"dependencies,":[91],"Dynamic":[94],"Adaptation":[96],"(DFAM)":[98],"adaptively":[100],"suppress":[101],"semantic":[104],"noise":[105],"through":[106],"deformable":[107],"convolutions":[108],"dynamic":[110],"fusion.":[111],"These":[112],"modules":[113],"establish":[114],"structure-aware":[116],"adversarial":[117,154],"framework,":[118],"enhancing":[119],"both":[120],"pseudo-label":[121],"quality":[122],"segmentation":[124],"consistency.":[125],"Experiments":[126],"on":[127],"DeepGlobe":[128],"Massachusetts":[130],"datasets":[131],"demonstrate":[132],"that":[133],"MSA-GAN":[134],"achieves":[135],"consistent":[136],"improvements":[137],"F1":[139],"IoU":[141],"(0.89%\u20136.0%)":[142],"over":[143],"state-of-the-art":[144],"methods,":[145],"validating":[146],"effectiveness":[148],"multi-structure":[150],"enhancements":[151],"adaptive":[153],"learning":[155],"extraction.":[159]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-12-17T00:00:00"}