{"id":"https://openalex.org/W7160868364","doi":"https://doi.org/10.3390/ijgi15050208","title":"Topology-Aware Road Extraction from Remote Sensing Images Using Deep Learning and Graph-Based Connectivity Refinement","display_name":"Topology-Aware Road Extraction from Remote Sensing Images Using Deep Learning and Graph-Based Connectivity Refinement","publication_year":2026,"publication_date":"2026-05-09","ids":{"openalex":"https://openalex.org/W7160868364","doi":"https://doi.org/10.3390/ijgi15050208"},"language":"en","primary_location":{"id":"doi:10.3390/ijgi15050208","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15050208","pdf_url":"https://www.mdpi.com/2220-9964/15/5/208/pdf?version=1778332861","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2220-9964/15/5/208/pdf?version=1778332861","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5119542484","display_name":"Zixuan Teng","orcid":"https://orcid.org/0009-0001-4377-5346"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zixuan Teng","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135863351","display_name":"Zezhong Zheng","orcid":"https://orcid.org/0000-0002-5615-5015"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I3018263800","display_name":"Huzhou University","ror":"https://ror.org/04mvpxy20","country_code":"CN","type":"education","lineage":["https://openalex.org/I3018263800"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zezhong Zheng","raw_affiliation_strings":["School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313000, China"],"raw_orcid":"https://orcid.org/0000-0002-5615-5015","affiliations":[{"raw_affiliation_string":"School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China","institution_ids":["https://openalex.org/I150229711"]},{"raw_affiliation_string":"The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313000, China","institution_ids":["https://openalex.org/I3018263800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083163801","display_name":"Xiangyang Sun","orcid":"https://orcid.org/0000-0003-4385-3479"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiangyang Sun","raw_affiliation_strings":["SHU-SUCG Research Centre for Building Industrialization, SILC Business School, Shanghai University, Shanghai 201800, China"],"raw_orcid":"https://orcid.org/0009-0001-6619-9097","affiliations":[{"raw_affiliation_string":"SHU-SUCG Research Centre for Building Industrialization, SILC Business School, Shanghai University, Shanghai 201800, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000564739","display_name":"Hao Xue","orcid":"https://orcid.org/0000-0003-1700-9215"},"institutions":[{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Hao Xue","raw_affiliation_strings":["School of Geographical & Earth Science, University of Glasgow, Glasgow G12 8QQ, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geographical & Earth Science, University of Glasgow, Glasgow G12 8QQ, UK","institution_ids":["https://openalex.org/I7882870"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083163801"],"corresponding_institution_ids":["https://openalex.org/I113940042"],"apc_list":{"value":1700,"currency":"CHF","value_usd":1893},"apc_paid":{"value":1700,"currency":"CHF","value_usd":1893},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.49702399,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"15","issue":"5","first_page":"208","last_page":"208"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9952999949455261,"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":0.9952999949455261,"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":0.0007999999797903001,"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/T12644","display_name":"Wildlife-Road Interactions and Conservation","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/segmentation","display_name":"Segmentation","score":0.6092000007629395},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.58160001039505},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5422000288963318},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5066999793052673},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.45879998803138733},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.40790000557899475},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.4011000096797943},{"id":"https://openalex.org/keywords/geographic-information-system","display_name":"Geographic information system","score":0.33079999685287476},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.3255000114440918},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.31310001015663147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7312999963760376},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6092000007629395},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.58160001039505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.576200008392334},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5422000288963318},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5066999793052673},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.45879998803138733},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.40790000557899475},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3889000117778778},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.38119998574256897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3718999922275543},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.3077999949455261},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2953000068664551},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.2831000089645386},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C138187205","wikidata":"https://www.wikidata.org/wiki/Q131251","display_name":"Tangent","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C143392562","wikidata":"https://www.wikidata.org/wiki/Q449111","display_name":"Subdivision","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C186594467","wikidata":"https://www.wikidata.org/wiki/Q1429176","display_name":"Flooding (psychology)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C191015642","wikidata":"https://www.wikidata.org/wiki/Q1132459","display_name":"Fragmentation (computing)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/ijgi15050208","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15050208","pdf_url":"https://www.mdpi.com/2220-9964/15/5/208/pdf?version=1778332861","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:d098ec9bc9b040c6b65148680ea1050c","is_oa":true,"landing_page_url":"https://doaj.org/article/d098ec9bc9b040c6b65148680ea1050c","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ISPRS International Journal of Geo-Information, Vol 15, Iss 5, p 208 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/ijgi15050208","is_oa":true,"landing_page_url":"https://doi.org/10.3390/ijgi15050208","pdf_url":"https://www.mdpi.com/2220-9964/15/5/208/pdf?version=1778332861","source":{"id":"https://openalex.org/S2764431341","display_name":"ISPRS International Journal of Geo-Information","issn_l":"2220-9964","issn":["2220-9964"],"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":"ISPRS International Journal of Geo-Information","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.5650439858436584}],"awards":[{"id":"https://openalex.org/G6190148888","display_name":null,"funder_award_id":"2024GZ54","funder_id":"https://openalex.org/F4320326169","funder_display_name":"Huzhou Municipal Science and Technology Bureau"},{"id":"https://openalex.org/G673731264","display_name":null,"funder_award_id":"2026NSFSC0219","funder_id":"https://openalex.org/F4320329861","funder_display_name":"Natural Science Foundation of Sichuan Province"}],"funders":[{"id":"https://openalex.org/F4320326169","display_name":"Huzhou Municipal Science and Technology Bureau","ror":null},{"id":"https://openalex.org/F4320329861","display_name":"Natural Science Foundation of Sichuan Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7160868364.pdf","grobid_xml":"https://content.openalex.org/works/W7160868364.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2560023338","https://openalex.org/W2798925380","https://openalex.org/W2804199516","https://openalex.org/W3024167159","https://openalex.org/W3086017879","https://openalex.org/W3093694820","https://openalex.org/W3110395491","https://openalex.org/W3130942710","https://openalex.org/W3138516171","https://openalex.org/W3150573203","https://openalex.org/W3158561381","https://openalex.org/W3159442990","https://openalex.org/W3196904463","https://openalex.org/W4210274385","https://openalex.org/W4224935869","https://openalex.org/W4283078106","https://openalex.org/W4292982208","https://openalex.org/W4296211548","https://openalex.org/W4312443924","https://openalex.org/W4327695706","https://openalex.org/W4382982167","https://openalex.org/W4385413647","https://openalex.org/W4389104824","https://openalex.org/W4389961031","https://openalex.org/W4393079009","https://openalex.org/W4399432762","https://openalex.org/W4404037509","https://openalex.org/W4404057535","https://openalex.org/W4407192196","https://openalex.org/W4409853020","https://openalex.org/W4413353303","https://openalex.org/W4413757834"],"related_works":[],"abstract_inverted_index":{"Road":[0],"networks":[1,37],"are":[2],"fundamental":[3],"components":[4],"of":[5,158,205],"transportation":[6],"infrastructure":[7],"and":[8,46,100,124,153,191,207,226,239],"play":[9],"a":[10,54,65,71,87,95,101,128,174],"crucial":[11],"role":[12],"in":[13,27,177],"various":[14],"geospatial":[15],"applications.":[16],"Although":[17],"deep":[18,61],"learning-based":[19,62],"semantic":[20],"segmentation":[21,63],"models":[22],"have":[23],"achieved":[24],"promising":[25],"results":[26,163],"extracting":[28],"roads":[29],"from":[30,40],"high-resolution":[31],"remote":[32],"sensing":[33],"imagery,":[34],"the":[35,114,156,159,166,181,188,194,214],"resulting":[36],"often":[38],"suffer":[39],"topological":[41,125],"fragmentation":[42],"due":[43],"to":[44,80,136],"occlusions":[45],"shadows.":[47],"To":[48],"address":[49],"this":[50],"issue,":[51],"we":[52],"propose":[53],"topology-aware":[55],"road":[56,83,130,143,171,223],"extraction":[57],"method":[58,215],"that":[59,165,213],"integrates":[60],"with":[64,173,201,221],"graph-based":[66],"connectivity":[67,172],"refinement":[68,167],"strategy.":[69],"Specifically,":[70,180],"Pyramid":[72],"Scene":[73],"Parsing":[74],"Network":[75],"(PSPNet)":[76],"is":[77,91,134],"first":[78],"employed":[79],"generate":[81],"initial":[82],"probability":[84],"maps.":[85],"Subsequently,":[86],"connectivity-oriented":[88],"post-processing":[89],"pipeline":[90],"introduced,":[92],"which":[93],"incorporates":[94],"multi-source":[96],"cost":[97],"function":[98],"strategy":[99,133],"direction-aware":[102],"Dijkstra":[103],"search":[104],"algorithm.":[105],"By":[106],"utilizing":[107],"endpoint":[108],"tangent":[109],"vectors":[110],"as":[111,236],"inertial":[112],"weights,":[113],"algorithm":[115],"effectively":[116,216],"reconstructs":[117],"fragmented":[118],"segments":[119],"while":[120,197],"ensuring":[121],"geometric":[122],"smoothness":[123],"consistency.":[126],"Furthermore,":[127],"dynamic":[129],"width":[131],"restoration":[132],"applied":[135],"transform":[137],"refined":[138],"skeletons":[139],"into":[140,229],"physically":[141],"consistent":[142],"entities.":[144],"Experiments":[145],"conducted":[146],"on":[147,187,193],"two":[148],"publicly":[149],"available":[150],"datasets,":[151],"CHN6-CUG":[152,189],"DeepGlobe,":[154],"demonstrate":[155],"effectiveness":[157],"proposed":[160],"method.":[161],"Quantitative":[162],"show":[164],"process":[168],"significantly":[169],"enhances":[170],"minimal":[175],"trade-off":[176],"pixel-level":[178],"accuracy.":[179],"Conn":[182],"metric":[183],"increases":[184],"by":[185],"0.1989":[186],"dataset":[190],"0.3055":[192],"DeepGlobe":[195],"dataset,":[196],"MIoU":[198],"remains":[199],"high":[200],"only":[202],"marginal":[203],"decreases":[204],"1.07%":[206],"0.45%,":[208],"respectively.":[209],"These":[210],"findings":[211],"indicate":[212],"restores":[217],"structural":[218],"continuity,":[219],"helping":[220],"reliable":[222],"network":[224],"generation":[225],"subsequent":[227],"integration":[228],"Geographic":[230],"Information":[231],"System":[232],"(GIS)-based":[233],"applications":[234],"such":[235],"urban":[237],"planning":[238],"autonomous":[240],"navigation.":[241]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2026-05-12T00:00:00"}
