{"id":"https://openalex.org/W7214720879","doi":"https://doi.org/10.54097/nvegp737","title":"TSNet: A Two-stage Segmentation Network Guided by Uncertainty for Remote Sensing Image Road Extraction","display_name":"TSNet: A Two-stage Segmentation Network Guided by Uncertainty for Remote Sensing Image Road Extraction","publication_year":2026,"publication_date":"2026-09-29","ids":{"openalex":"https://openalex.org/W7214720879","doi":"https://doi.org/10.54097/nvegp737"},"language":"en","primary_location":{"id":"doi:10.54097/nvegp737","doi":"https://doi.org/10.54097/nvegp737","is_oa":true,"landing_page_url":"https://doi.org/10.54097/nvegp737","pdf_url":"https://drpress.org/ojs/index.php/ajst/article/download/35988/35149","source":{"id":"https://openalex.org/S4220651530","display_name":"Academic Journal of Science and Technology","issn_l":"2771-3032","issn":["2771-3032"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Academic Journal of Science and Technology","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://drpress.org/ojs/index.php/ajst/article/download/35988/35149","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5153522280","display_name":"Jiajia Liu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I58995867","display_name":"Civil Aviation Flight University of China","ror":"https://ror.org/01xyb1v19","country_code":"CN","type":"education","lineage":["https://openalex.org/I58995867"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajia Liu","raw_affiliation_strings":["Civil Aviation Flight University of China, Center for Faculty Development and Teaching Evaluation, Guanghan 618307, China","Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Civil Aviation Flight University of China, Center for Faculty Development and Teaching Evaluation, Guanghan 618307, China","institution_ids":["https://openalex.org/I58995867"]},{"raw_affiliation_string":"Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China","institution_ids":["https://openalex.org/I58995867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126420483","display_name":"Xuan Zhao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I58995867","display_name":"Civil Aviation Flight University of China","ror":"https://ror.org/01xyb1v19","country_code":"CN","type":"education","lineage":["https://openalex.org/I58995867"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Zhao","raw_affiliation_strings":["Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China","institution_ids":["https://openalex.org/I58995867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126363104","display_name":"Wenxiang Dong","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I58995867","display_name":"Civil Aviation Flight University of China","ror":"https://ror.org/01xyb1v19","country_code":"CN","type":"education","lineage":["https://openalex.org/I58995867"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxiang Dong","raw_affiliation_strings":["Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China","institution_ids":["https://openalex.org/I58995867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115501026","display_name":"Jianhua Liu","orcid":"https://orcid.org/0000-0002-0113-2182","observed_orcids":["https://orcid.org/0000-0002-0113-2182"]},"institutions":[{"id":"https://openalex.org/I58995867","display_name":"Civil Aviation Flight University of China","ror":"https://ror.org/01xyb1v19","country_code":"CN","type":"education","lineage":["https://openalex.org/I58995867"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Liu","raw_affiliation_strings":["Civil Aviation Flight University of China, Center for Faculty Development and Teaching Evaluation, Guanghan 618307, China","Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Civil Aviation Flight University of China, Center for Faculty Development and Teaching Evaluation, Guanghan 618307, China","institution_ids":["https://openalex.org/I58995867"]},{"raw_affiliation_string":"Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China","institution_ids":["https://openalex.org/I58995867"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013979705","display_name":"Xiaoguang Tu","orcid":"https://orcid.org/0000-0002-1185-5229","observed_orcids":["https://orcid.org/0000-0002-1185-5229","https://orcid.org/0009-0001-6876-8783"]},"institutions":[{"id":"https://openalex.org/I58995867","display_name":"Civil Aviation Flight University of China","ror":"https://ror.org/01xyb1v19","country_code":"CN","type":"education","lineage":["https://openalex.org/I58995867"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoguang Tu","raw_affiliation_strings":["Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Civil Aviation Flight University of China, College of Aviation Electronic and Electrical Engineering, Guanghan 618307, China","institution_ids":["https://openalex.org/I58995867"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I58995867"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":"3","first_page":"24","last_page":"34"},"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/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"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/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.0,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/road-extraction","display_name":"road extraction","score":0.9879999756813049},{"id":"https://openalex.org/keywords/road-segmentation","display_name":"road segmentation","score":0.8889999985694885},{"id":"https://openalex.org/keywords/high-resolution-remote-sensing-images","display_name":"high-resolution remote sensing images","score":0.8429999947547913},{"id":"https://openalex.org/keywords/multiscale-feature-fusion","display_name":"multi-scale feature fusion","score":0.7400000095367432},{"id":"https://openalex.org/keywords/uncertainty-aware-learning","display_name":"uncertainty-aware learning","score":0.7350000143051147},{"id":"https://openalex.org/keywords/road-network-connectivity","display_name":"road connectivity","score":0.5920000076293945},{"id":"https://openalex.org/keywords/edge-refinement","display_name":"edge refinement","score":0.4580000042915344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354999780654907},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6815999746322632},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6660000085830688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5665000081062317},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5030999779701233},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4794999957084656},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47040000557899475},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4426000118255615},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4099000096321106},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39959999918937683},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.37529999017715454},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3752000033855438},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.289900004863739},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.54097/nvegp737","doi":"https://doi.org/10.54097/nvegp737","is_oa":true,"landing_page_url":"https://doi.org/10.54097/nvegp737","pdf_url":"https://drpress.org/ojs/index.php/ajst/article/download/35988/35149","source":{"id":"https://openalex.org/S4220651530","display_name":"Academic Journal of Science and Technology","issn_l":"2771-3032","issn":["2771-3032"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Academic Journal of Science and Technology","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.54097/nvegp737","doi":"https://doi.org/10.54097/nvegp737","is_oa":true,"landing_page_url":"https://doi.org/10.54097/nvegp737","pdf_url":"https://drpress.org/ojs/index.php/ajst/article/download/35988/35149","source":{"id":"https://openalex.org/S4220651530","display_name":"Academic Journal of Science and Technology","issn_l":"2771-3032","issn":["2771-3032"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Academic Journal of Science and Technology","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.8970999717712402,"display_name":"Industry, innovation and infrastructure"},{"id":"https://metadata.un.org/sdg/11","score":0.47999998927116394,"display_name":"Sustainable cities and communities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214720879.pdf","grobid_xml":"https://content.openalex.org/works/W7214720879.grobid-xml"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2734349601","https://openalex.org/W2893801697","https://openalex.org/W2964309882","https://openalex.org/W3015788359","https://openalex.org/W3084798277","https://openalex.org/W3138516171","https://openalex.org/W4214532801","https://openalex.org/W4283450732","https://openalex.org/W4290715747","https://openalex.org/W4292787077","https://openalex.org/W4327620107","https://openalex.org/W4399432762","https://openalex.org/W4404035907","https://openalex.org/W4404952356","https://openalex.org/W4405900858","https://openalex.org/W4411406779"],"related_works":["https://openalex.org/W7129110371","https://openalex.org/W7134946618","https://openalex.org/W3081791696","https://openalex.org/W4407663381","https://openalex.org/W4206690280","https://openalex.org/W3167787268","https://openalex.org/W4410770531","https://openalex.org/W4413462722","https://openalex.org/W7160964647","https://openalex.org/W4402593558"],"abstract_inverted_index":{"Road":[0],"extraction":[1,271],"from":[2,29],"high\u2011resolution":[3],"remote\u2011sensing":[4],"images":[5],"plays":[6],"a":[7],"vital":[8],"practical":[9],"role":[10],"in":[11,177,251],"multiple":[12],"application":[13],"scenarios":[14],"including":[15,36],"urban":[16],"layout":[17],"planning":[18],"and":[19,43,77,92,112,155,186,196,219,256,266],"autonomous":[20],"driving":[21],"systems.":[22],"Nevertheless,":[23],"current":[24],"road\u2011extraction":[25],"approaches":[26],"still":[27],"suffer":[28],"several":[30],"bottlenecks":[31],"under":[32],"complex":[33,273],"ground":[34],"environments,":[35],"indistinct":[37],"object":[38],"boundaries,":[39],"missing":[40],"fine\u2011grained":[41],"features":[42],"fragmented":[44],"road":[45,68,160,179,254,270],"outputs.":[46],"Aiming":[47],"to":[48,90,120,146,152],"mitigate":[49],"the":[50,98,104,122,130,134,140,166,171,202,246,264],"above":[51],"defects,":[52],"this":[53],"paper":[54],"puts":[55],"forward":[56],"an":[57,113,206],"uncertainty\u2011guided":[58],"two\u2011stage":[59],"segmentation":[60,69,106],"network":[61],"termed":[62],"TSNet.":[63],"The":[64],"proposed":[65],"framework":[66],"divides":[67],"into":[70],"two":[71],"successive":[72],"subtasks:":[73],"global\u2011level":[74],"semantic":[75],"understanding":[76],"local":[78,135],"boundary":[79],"optimization.":[80],"A":[81],"cross\u2011layer":[82],"residual":[83],"feature":[84,95],"enhancement":[85],"module":[86,116,137],"(CRM)":[87],"is":[88,118],"adopted":[89],"extract":[91],"aggregate":[93],"multi\u2011scale":[94,101],"information.":[96],"On":[97],"basis":[99],"of":[100,191,208,211,214,217,221,269],"fused":[102],"features,":[103],"first":[105],"stage":[107],"produces":[108],"preliminary":[109],"probability":[110],"maps,":[111],"uncertainty\u2011aware":[114],"learning":[115],"(UALM)":[117],"introduced":[119],"measure":[121],"confidence":[123],"degree":[124],"for":[125,159,234,272],"each":[126],"pixel\u2011wise":[127],"prediction.":[128],"In":[129,239],"subsequent":[131],"second":[132],"stage,":[133],"refinement":[136],"(LRM)":[138],"takes":[139],"generated":[141],"uncertainty":[142],"map":[143],"as":[144],"guidance":[145],"dynamically":[147],"concentrate":[148],"on":[149,165,201],"regions":[150],"prone":[151],"prediction":[153,267],"errors,":[154],"implements":[156],"adaptive":[157],"optimization":[158],"edge":[161,259],"areas.":[162],"Quantitative":[163],"experiments":[164],"DeepGlobe":[167],"dataset":[168,204],"prove":[169],"that":[170,245],"presented":[172],"model":[173,247],"achieves":[174,231],"competitive":[175],"performance":[176],"maintaining":[178],"connectivity,":[180],"yielding":[181],"Accuracy,":[182],"Precision,":[183],"Recall,":[184],"F1\u2011score":[185,216],"Intersection":[187],"over":[188],"Union":[189],"(IoU)":[190],"98.68%,":[192],"83.72%,":[193],"82.36%,":[194],"82.85%":[195],"71.07%,":[197],"respectively.":[198],"Further":[199],"validation":[200],"CHN6\u2011CUG":[203],"obtains":[205],"Accuracy":[207],"96.10%,":[209],"Precision":[210],"80.48%,":[212],"Recall":[213],"79.56%,":[215],"79.37%":[218],"IoU":[220],"66.20%.":[222],"When":[223],"compared":[224],"with":[225],"state\u2011of\u2011the\u2011art":[226],"competing":[227],"algorithms,":[228],"our":[229],"method":[230],"optimal":[232],"results":[233,243],"all":[235],"five":[236],"evaluation":[237],"metrics.":[238],"addition,":[240],"qualitative":[241],"visualization":[242],"reveal":[244],"exhibits":[248],"prominent":[249],"strengths":[250],"retaining":[252],"connected":[253],"topology":[255],"recovering":[257],"intricate":[258],"contours,":[260],"which":[261],"effectively":[262],"enhances":[263],"robustness":[265],"accuracy":[268],"real\u2011world":[274],"scenes.":[275]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-30T00:00:00"}