{"id":"https://openalex.org/W4401072727","doi":"https://doi.org/10.1109/lgrs.2024.3430900","title":"Road-SAM: Adapting the Segment Anything Model to Road Extraction From Large Very-High-Resolution Optical Remote Sensing Images","display_name":"Road-SAM: Adapting the Segment Anything Model to Road Extraction From Large Very-High-Resolution Optical Remote Sensing Images","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401072727","doi":"https://doi.org/10.1109/lgrs.2024.3430900"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2024.3430900","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3430900","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/A5036964506","display_name":"Wenqing Feng","orcid":"https://orcid.org/0000-0001-7763-5870"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqing Feng","raw_affiliation_strings":["Computer and Software School, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7763-5870","affiliations":[{"raw_affiliation_string":"Computer and Software School, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012288206","display_name":"Fangli Guan","orcid":"https://orcid.org/0000-0001-7409-2129"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangli Guan","raw_affiliation_strings":["Computer and Software School, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer and Software School, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075111057","display_name":"Chenhao Sun","orcid":"https://orcid.org/0000-0002-1661-4555"},"institutions":[{"id":"https://openalex.org/I56934997","display_name":"Changsha University of Science and Technology","ror":"https://ror.org/03yph8055","country_code":"CN","type":"education","lineage":["https://openalex.org/I56934997"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenhao Sun","raw_affiliation_strings":["Electrical and Information Engineering School, Changsha University of Science and Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Information Engineering School, Changsha University of Science and Technology, Changsha, China","institution_ids":["https://openalex.org/I56934997"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101970468","display_name":"Wei Xu","orcid":"https://orcid.org/0000-0003-4356-6832"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Xu","raw_affiliation_strings":["Information System and Management College, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Information System and Management College, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.5129,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.96399535,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"21","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":0.9998000264167786,"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":0.9998000264167786,"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":0.9894999861717224,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9804999828338623,"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/remote-sensing","display_name":"Remote sensing","score":0.5995123982429504},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5929462909698486},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49976325035095215},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4931761622428894},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.49101924896240234},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4396681785583496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43935173749923706},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.22427457571029663}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5995123982429504},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5929462909698486},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49976325035095215},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4931761622428894},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.49101924896240234},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4396681785583496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43935173749923706},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.22427457571029663},{"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":1,"locations":[{"id":"doi:10.1109/lgrs.2024.3430900","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3430900","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":[],"awards":[{"id":"https://openalex.org/G2341050865","display_name":null,"funder_award_id":"42101358","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2165643387","https://openalex.org/W2228908729","https://openalex.org/W2735039185","https://openalex.org/W2774320778","https://openalex.org/W2783572327","https://openalex.org/W2893801697","https://openalex.org/W2982363097","https://openalex.org/W3105636206","https://openalex.org/W4282967568","https://openalex.org/W4307631399","https://openalex.org/W4367189325","https://openalex.org/W4386065718","https://openalex.org/W4386076151","https://openalex.org/W4387197175","https://openalex.org/W4390190100","https://openalex.org/W4390632618","https://openalex.org/W4390874575","https://openalex.org/W4391462890","https://openalex.org/W6838701581","https://openalex.org/W6851980744","https://openalex.org/W6860844317","https://openalex.org/W6861113529"],"related_works":["https://openalex.org/W2121524756","https://openalex.org/W782553550","https://openalex.org/W1987967678","https://openalex.org/W2633218168","https://openalex.org/W4235897794","https://openalex.org/W2059707233","https://openalex.org/W2095126257","https://openalex.org/W2085738998","https://openalex.org/W2031511989","https://openalex.org/W2369060955"],"abstract_inverted_index":{"We":[0],"propose":[1],"road-segment":[2],"anything":[3],"model":[4,8,69],"(SAM),":[5],"a":[6,32,38,71,96],"universal":[7],"for":[9,41,55],"extracting":[10],"roads":[11],"from":[12],"large,":[13],"very-high-resolution":[14],"(VHR),":[15],"optical,":[16],"remote":[17],"sensing":[18],"(RS)":[19],"images.":[20],"Unlike":[21],"previous":[22],"methods,":[23],"Road-SAM":[24,93],"builds":[25],"upon":[26],"the":[27,30,47,64,68,115,138,141,165],"foundation":[28],"of":[29,58,75,125,133,140,164],"SAM,":[31],"large-scale":[33],"image-segmentation":[34],"model,":[35,117],"to":[36,109,161],"explore":[37],"new":[39],"paradigm":[40],"customizable":[42],"road":[43],"extraction":[44],"(RE).":[45],"Within":[46],"framework,":[48],"we":[49],"introduce":[50],"three":[51],"variants":[52],"that":[53,82],"allow":[54],"flexible":[56],"insertion":[57],"adapters":[59],"at":[60],"different":[61],"positions":[62],"within":[63],"transformer":[65],"block.":[66],"Additionally,":[67],"employs":[70],"task-specific":[72],"input":[73],"module":[74],"explicit":[76],"visual":[77],"prompting":[78],"(EVP)":[79],"during":[80],"training":[81],"uses":[83],"embedded":[84],"features":[85],"and":[86,121],"high-frequency":[87],"component":[88],"(HFC)":[89],"information":[90],"as":[91],"prompts.":[92],"also":[94],"utilizes":[95],"carefully":[97],"designed":[98],"frequency":[99],"adapter":[100],"fine-tuning":[101,107],"mechanism,":[102],"leveraging":[103],"lightweight":[104],"yet":[105],"effective":[106],"techniques":[108],"integrate":[110],"domain-specific":[111],"RS":[112,155],"knowledge":[113],"into":[114],"RE":[116,134,156],"enhancing":[118],"segmentation":[119],"performance":[120],"making":[122],"efficient":[123],"use":[124],"computational":[126],"resources.":[127],"Comprehensive":[128],"experiments":[129,146],"on":[130],"two":[131],"sets":[132],"benchmark":[135],"datasets":[136],"demonstrate":[137],"effectiveness":[139],"proposed":[142],"method.":[143],"Extensive":[144],"ablation":[145],"further":[147],"validate":[148],"its":[149],"superiority":[150],"over":[151],"multiple":[152],"state-of-the-art":[153],"(SOTA)":[154],"algorithms,":[157],"with":[158],"updates":[159],"applied":[160],"only":[162],"10%":[163],"parameters.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":15}],"updated_date":"2026-06-20T22:02:38.213706","created_date":"2025-10-10T00:00:00"}
