{"id":"https://openalex.org/W4415707533","doi":"https://doi.org/10.1109/tgrs.2025.3626816","title":"GenP2C: A General Pipeline for Road Extraction From Partial Road Maps","display_name":"GenP2C: A General Pipeline for Road Extraction From Partial Road Maps","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4415707533","doi":"https://doi.org/10.1109/tgrs.2025.3626816"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3626816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3626816","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/A5003763398","display_name":"Haotian Yan","orcid":"https://orcid.org/0000-0002-6877-4366"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haotian Yan","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021615259","display_name":"Kaiwen Jing","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiwen Jing","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031910980","display_name":"Yihao Zuo","orcid":"https://orcid.org/0000-0003-0814-7325"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yihao Zuo","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077902436","display_name":"Ming Wu","orcid":"https://orcid.org/0000-0001-8390-5398"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Wu","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015730388","display_name":"Chuang Zhang","orcid":"https://orcid.org/0000-0002-1115-5580"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuang Zhang","raw_affiliation_strings":["Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"School of Artificial Intelligence, Pattern Recognition and Intelligent System Laboratory, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5003763398"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38511377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9934999942779541,"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":0.9934999942779541,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.0010999999940395355,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/discriminative-model","display_name":"Discriminative model","score":0.6427000164985657},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.640500009059906},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5595999956130981},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.5493000149726868},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.44440001249313354},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4318000078201294},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.42820000648498535},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4108000099658966},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.39739999175071716}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7228999733924866},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6427000164985657},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.640500009059906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5748999714851379},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5595999956130981},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.5493000149726868},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4514999985694885},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.44440001249313354},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4108000099658966},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3874000012874603},{"id":"https://openalex.org/C188048851","wikidata":"https://www.wikidata.org/wiki/Q2298569","display_name":"Road map","level":2,"score":0.3758000135421753},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2770000100135803},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2718000113964081},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2632000148296356},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.259799987077713},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3626816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3626816","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":[],"awards":[{"id":"https://openalex.org/G7398995703","display_name":null,"funder_award_id":"U24B20177","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":52,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2593886839","https://openalex.org/W2735039185","https://openalex.org/W2787091153","https://openalex.org/W2804199516","https://openalex.org/W2893801697","https://openalex.org/W2921476973","https://openalex.org/W2962858109","https://openalex.org/W2975194617","https://openalex.org/W2991488782","https://openalex.org/W3025400702","https://openalex.org/W3035012694","https://openalex.org/W3090630740","https://openalex.org/W3110395491","https://openalex.org/W3111241683","https://openalex.org/W3138516171","https://openalex.org/W3150573203","https://openalex.org/W3203699578","https://openalex.org/W3214248441","https://openalex.org/W4200512530","https://openalex.org/W4206046266","https://openalex.org/W4213147909","https://openalex.org/W4221145217","https://openalex.org/W4226445372","https://openalex.org/W4292656663","https://openalex.org/W4312314757","https://openalex.org/W4317603812","https://openalex.org/W4320919181","https://openalex.org/W4360994023","https://openalex.org/W4378697132","https://openalex.org/W4380032327","https://openalex.org/W4382138638","https://openalex.org/W4385819918","https://openalex.org/W4386076493","https://openalex.org/W4387515118","https://openalex.org/W4387682131","https://openalex.org/W4389104824","https://openalex.org/W4390873988","https://openalex.org/W4390874575","https://openalex.org/W4391582411","https://openalex.org/W4391791466","https://openalex.org/W4395447356","https://openalex.org/W4398788587","https://openalex.org/W4400448280","https://openalex.org/W4401476753","https://openalex.org/W4406333380","https://openalex.org/W4408057884","https://openalex.org/W4408468245","https://openalex.org/W4408564691","https://openalex.org/W4408642921","https://openalex.org/W4410808562","https://openalex.org/W4413145547"],"related_works":[],"abstract_inverted_index":{"We":[0],"propose":[1,88],"a":[2,89,107,116],"pipeline":[3,14],"for":[4,26,190],"extracting":[5],"complete":[6,255],"road":[7,11,28,71,76,123,128,171,176,202,216,248,266,273,278,284],"networks":[8],"from":[9,47,257],"partial":[10,56,65,258,303],"maps.":[12],"The":[13,130],"is":[15,155,198],"architecturally":[16],"simpler,":[17],"more":[18,20],"powerful,":[19],"computationally":[21],"efficient,":[22],"and":[23,114,194,225,238,299,305],"better":[24,310],"suited":[25],"general":[27,201,265],"extraction":[29,72,124,203,249,267,279,285],"models":[30,46,250,280],"than":[31,312],"the":[32,39,70,80,83,96,100,122,143,158,184,191,282],"existing":[33],"method":[34],"like":[35],"P2CNet.":[36,313],"Inspired":[37],"by":[38,212,307],"philosophy":[40],"of":[41,110,231],"conditional":[42],"control":[43],"in":[44,74,272],"diffusion":[45],"AIGC":[48],"research,":[49],"we":[50,87],"introduce":[51],"an":[52,213],"<italic":[53,57,144,167,172],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[54,58,145,147,149,168,173],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">E</i>fficient":[55],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">M</i>ap":[59],"Encoder":[60],"(EME)":[61],"that":[62,251],"extracts":[63],"multi-level":[64,132],"Map":[66,97],"embeddings":[67,134,189],"to":[68,94,119,180,200,246,253,295],"guide":[69],"model":[73,125],"learning":[75,196],"topology.":[77],"To":[78],"interact":[79],"EME":[81,182,237],"with":[82,99,126,183,206,264,286],"main":[84],"(image)":[85],"encoder,":[86,186],"Self-Activation":[90],"Addition":[91],"(SAA)":[92],"module":[93],"fuse":[95],"embedding":[98],"Image":[101],"embedding,":[102],"where":[103],"SAA":[104,179,239],"only":[105,229],"occupies":[106],"few":[108],"portion":[109],"whole":[111],"computational":[112,288],"budget":[113],"conducts":[115],"level-to-level":[117],"fusion":[118],"effectively":[120],"prompt":[121],"discriminative":[127],"information.":[129],"resulting":[131],"Image-Map":[133,188],"are":[135],"fed":[136],"into":[137],"our":[138,164],"newly":[139],"developed":[140],"hierarchical":[141],"decoder,":[142],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">M</i>ulti-<italic":[146],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">R</i>oad-<italic":[148],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">S</i>cale":[150],"Attention":[151],"Network":[152],"(Mrs.Former),":[153,193],"which":[154],"based":[156],"on":[157,218,281],"varying-window":[159],"multi-scale":[160],"attention":[161],"mechanism.":[162],"Overall,":[163],"pipeline,":[165],"termed":[166],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Gen</i>eral":[169],"Partial":[170],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">to</i>":[174],"Complete":[175],"(GenP2C),":[177],"uses":[178],"connect":[181],"image":[185],"producing":[187],"decoder":[192],"its":[195],"objective":[197],"identical":[199],"models.":[204],"Compared":[205],"P2CNet,":[207],"GenP2C":[208,260,290],"consistently":[209],"improves":[210],"performance":[211,311],"average":[214],"1.7%":[215],"IoU":[217,274],"challenging":[219],"datasets":[220],"such":[221],"as":[222,242],"DeepGlobe,":[223],"SpaceNet,":[224],"OSM,":[226],"while":[227],"using":[228],"72%":[230],"P2CNet\u2019s":[232],"computation":[233],"cost.":[234],"More":[235],"prominently,":[236],"can":[240],"serve":[241],"plug-and-play":[243],"modules":[244],"applicable":[245],"other":[247],"aim":[252],"extract":[254],"roads":[256],"ones.":[259],"demonstrates":[261,292],"strong":[262,293],"compatibility":[263],"models,":[268],"offering":[269],"0.8%-1.2%":[270],"improvements":[271],"across":[275],"five":[276],"prevalent":[277],"P2C":[283],"cheap":[287],"overhead.":[289],"also":[291],"generalization":[294],"multi-spectral":[296],"satellite":[297],"images":[298],"spatial":[300],"misalignments":[301],"between":[302],"maps":[304],"imagery":[306],"continuously":[308],"achieving":[309]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-10-30T00:00:00"}