{"id":"https://openalex.org/W4403684564","doi":"https://doi.org/10.3390/rs16213945","title":"Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images","display_name":"Consistency Self-Training Semi-Supervised Method for Road Extraction from Remote Sensing Images","publication_year":2024,"publication_date":"2024-10-23","ids":{"openalex":"https://openalex.org/W4403684564","doi":"https://doi.org/10.3390/rs16213945"},"language":"en","primary_location":{"id":"doi:10.3390/rs16213945","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16213945","pdf_url":null,"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://doi.org/10.3390/rs16213945","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101628074","display_name":"Xingjian Gu","orcid":"https://orcid.org/0000-0002-2095-9505"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingjian Gu","raw_affiliation_strings":["College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China"],"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047066325","display_name":"Su\u2010Peng Yu","orcid":"https://orcid.org/0000-0003-1348-7447"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Supeng Yu","raw_affiliation_strings":["College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China"],"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102200806","display_name":"Fen Huang","orcid":"https://orcid.org/0009-0001-7864-8531"},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fen Huang","raw_affiliation_strings":["College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China"],"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041981044","display_name":"Shougang Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I119454577","display_name":"Nanjing Agricultural University","ror":"https://ror.org/05td3s095","country_code":"CN","type":"education","lineage":["https://openalex.org/I119454577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shougang Ren","raw_affiliation_strings":["College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China"],"affiliations":[{"raw_affiliation_string":"College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China","institution_ids":["https://openalex.org/I119454577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102720592","display_name":"Chengcheng Fan","orcid":"https://orcid.org/0000-0002-5402-6471"},"institutions":[{"id":"https://openalex.org/I4387154354","display_name":"Innovation Academy for Microsatellites of Chinese Academy of Sciences","ror":"https://ror.org/05f6mer85","country_code":null,"type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4387154354"]},{"id":"https://openalex.org/I4210088244","display_name":"Shanghai Micro Satellite Engineering Center","ror":"https://ror.org/003cp7918","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210088244"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengcheng Fan","raw_affiliation_strings":["Innovation Academy for Microsatellites of CAS, Shanghai 201210, China","Shanghai Engineering Center for Microsatellites, Shanghai 201210, China"],"affiliations":[{"raw_affiliation_string":"Innovation Academy for Microsatellites of CAS, Shanghai 201210, China","institution_ids":["https://openalex.org/I4210088244","https://openalex.org/I4387154354"]},{"raw_affiliation_string":"Shanghai Engineering Center for Microsatellites, Shanghai 201210, China","institution_ids":["https://openalex.org/I4210088244"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5102720592"],"corresponding_institution_ids":["https://openalex.org/I4210088244","https://openalex.org/I4387154354"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.9358,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.9061911,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"16","issue":"21","first_page":"3945","last_page":"3945"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":1.0,"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":1.0,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9979000091552734,"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":0.989300012588501,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6007241606712341},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5999903678894043},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5642018914222717},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.4757554233074188},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4671667814254761},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4271862208843231},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.332672119140625},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3235000967979431},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12803915143013},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11257833242416382},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.06673312187194824},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.047834962606430054}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6007241606712341},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5999903678894043},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5642018914222717},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.4757554233074188},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4671667814254761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4271862208843231},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.332672119140625},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3235000967979431},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12803915143013},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11257833242416382},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.06673312187194824},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.047834962606430054},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs16213945","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16213945","pdf_url":null,"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:ffd07efe1e404cfeaa9a04a97a5a0022","is_oa":true,"landing_page_url":"https://doaj.org/article/ffd07efe1e404cfeaa9a04a97a5a0022","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 16, Iss 21, p 3945 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16213945","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16213945","pdf_url":null,"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W611457968","https://openalex.org/W1495267108","https://openalex.org/W1528110553","https://openalex.org/W1901129140","https://openalex.org/W2022902702","https://openalex.org/W2401246392","https://openalex.org/W2552414813","https://openalex.org/W2737129951","https://openalex.org/W2747638294","https://openalex.org/W2893801697","https://openalex.org/W2942939191","https://openalex.org/W2962758679","https://openalex.org/W2981329832","https://openalex.org/W2982628450","https://openalex.org/W2983228571","https://openalex.org/W3034453930","https://openalex.org/W3118997087","https://openalex.org/W3121754326","https://openalex.org/W3132505321","https://openalex.org/W3134927667","https://openalex.org/W3150573203","https://openalex.org/W3170104301","https://openalex.org/W3175430845","https://openalex.org/W3206841217","https://openalex.org/W4206422044","https://openalex.org/W4213264000","https://openalex.org/W4282553729","https://openalex.org/W4282967568","https://openalex.org/W4285118380","https://openalex.org/W4312310512","https://openalex.org/W4393205855","https://openalex.org/W6631792978","https://openalex.org/W6806992076","https://openalex.org/W6838417955","https://openalex.org/W6863156558"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4366179611","https://openalex.org/W2996078371"],"abstract_inverted_index":{"Road":[0],"extraction":[1],"techniques":[2],"based":[3,18],"on":[4,19,126,146,172],"remote":[5,20,179],"sensing":[6,21,180],"image":[7],"have":[8,46],"significantly":[9,136],"advanced.":[10],"Currently,":[11],"fully":[12,200],"supervised":[13,201],"road":[14,30,78,175],"segmentation":[15,117,123],"neural":[16],"networks":[17],"images":[22,182],"require":[23],"a":[24,54,61,87,98,106,162],"significant":[25],"number":[26,100],"of":[27,57,64,101,109,165],"densely":[28],"labeled":[29,44,58,102,166],"samples,":[31],"limiting":[32],"their":[33],"applicability":[34],"in":[35,76],"large-scale":[36],"scenarios.":[37],"Consequently,":[38],"semi-supervised":[39,77,91,116,119,122,142,195],"methods":[40,196],"that":[41,189],"utilize":[42],"fewer":[43],"data":[45,59,66,103],"gained":[47],"increasing":[48],"attention.":[49],"However,":[50],"the":[51],"imbalance":[52],"between":[53],"small":[55],"quantity":[56],"and":[60,72,105,183,197],"large":[62,107],"volume":[63],"unlabeled":[65,110],"leads":[67],"to":[68,151],"local":[69,138],"detail":[70,139],"errors":[71],"overall":[73,153],"cognitive":[74,154],"mistakes":[75],"extraction.":[79],"To":[80],"address":[81],"this":[82,84],"challenge,":[83],"paper":[85],"proposes":[86],"novel":[88],"consistency":[89],"self-training":[90,115],"method":[92,113,157,191],"(CSSnet),":[93],"which":[94,135],"effectively":[95],"learns":[96],"from":[97],"limited":[99],"samples":[104],"amount":[108,164],"data.":[111,167],"This":[112,168],"integrates":[114],"with":[118,161],"classification.":[120],"The":[121,141],"component":[124,144],"relies":[125,145],"an":[127,147],"enhanced":[128],"generative":[129],"adversarial":[130],"network":[131,150],"for":[132],"semantic":[133],"segmentation,":[134],"reduces":[137],"errors.":[140,155],"classification":[143],"upgraded":[148],"mean-teacher":[149],"handle":[152],"Our":[156],"exhibits":[158],"excellent":[159],"performance":[160],"modest":[163],"study":[169],"was":[170],"validated":[171],"three":[173],"separate":[174],"datasets":[176],"comprising":[177],"high-resolution":[178],"satellite":[181],"UAV":[184],"photographs.":[185],"Experimental":[186],"findings":[187],"showed":[188],"our":[190],"consistently":[192],"outperformed":[193],"state-of-the-art":[194],"several":[198],"classic":[199],"methods.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}