{"id":"https://openalex.org/W4376607424","doi":"https://doi.org/10.1109/tgrs.2023.3276591","title":"Segmentation Is Not the End of Road Extraction: An All-Visible Denoising Autoencoder for Connected and Smooth Road Reconstruction","display_name":"Segmentation Is Not the End of Road Extraction: An All-Visible Denoising Autoencoder for Connected and Smooth Road Reconstruction","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4376607424","doi":"https://doi.org/10.1109/tgrs.2023.3276591"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3276591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3276591","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/A5067368904","display_name":"Lingyi Han","orcid":"https://orcid.org/0000-0003-4202-6260"},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyi Han","raw_affiliation_strings":["Big Data Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources (AGRS), Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4202-6260","affiliations":[{"raw_affiliation_string":"Big Data Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources (AGRS), Beijing, China","institution_ids":["https://openalex.org/I2799486974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079270380","display_name":"Lu Hou","orcid":"https://orcid.org/0000-0003-3085-9353"},"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":"Lu Hou","raw_affiliation_strings":["Intelligent Computing and Communications (IC2) Laboratory, Wireless Signal Processing and Network (WSPN) Laboratory, Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-3085-9353","affiliations":[{"raw_affiliation_string":"Intelligent Computing and Communications (IC2) Laboratory, Wireless Signal Processing and Network (WSPN) Laboratory, Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024650870","display_name":"Xiangxiang Zheng","orcid":"https://orcid.org/0000-0002-9298-8532"},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangxiang Zheng","raw_affiliation_strings":["Big Data Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources (AGRS), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources (AGRS), Beijing, China","institution_ids":["https://openalex.org/I2799486974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112915965","display_name":"Ziyue Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziyue Ding","raw_affiliation_strings":["Big Data Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources (AGRS), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Big Data Center, China Aero Geophysical Survey and Remote Sensing Center for Natural Resources (AGRS), Beijing, China","institution_ids":["https://openalex.org/I2799486974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067295504","display_name":"Haojun Yang","orcid":"https://orcid.org/0000-0001-7404-5007"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Haojun Yang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":"https://orcid.org/0000-0001-7404-5007","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100412656","display_name":"Kan Zheng","orcid":"https://orcid.org/0000-0002-8531-6762"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kan Zheng","raw_affiliation_strings":["College of Electrical Engineering and Computer Sciences, Ningbo University, Ningbo, China"],"raw_orcid":"https://orcid.org/0000-0002-8531-6762","affiliations":[{"raw_affiliation_string":"College of Electrical Engineering and Computer Sciences, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7344,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.82554479,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"18"},"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":0.9952999949455261,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9509000182151794,"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/computer-science","display_name":"Computer science","score":0.8048676252365112},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6567696332931519},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6368322968482971},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.604020357131958},{"id":"https://openalex.org/keywords/smoothness","display_name":"Smoothness","score":0.5452977418899536},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5244324803352356},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5184400081634521},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4766422212123871},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4687606990337372},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4550344944000244},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.423734188079834},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4184165596961975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3461533188819885},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2973320484161377},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09112876653671265}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8048676252365112},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6567696332931519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6368322968482971},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.604020357131958},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.5452977418899536},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5244324803352356},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5184400081634521},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4766422212123871},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4687606990337372},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4550344944000244},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.423734188079834},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4184165596961975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3461533188819885},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2973320484161377},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09112876653671265},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3276591","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3276591","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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8399999737739563}],"awards":[{"id":"https://openalex.org/G4911792101","display_name":null,"funder_award_id":"62001052","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5042819708","display_name":null,"funder_award_id":"2022RC04","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G579693649","display_name":null,"funder_award_id":"DD20230645","funder_id":"https://openalex.org/F4320334926","funder_display_name":"China Geological Survey"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334926","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":84,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1964038289","https://openalex.org/W1965176928","https://openalex.org/W1968879477","https://openalex.org/W1974097572","https://openalex.org/W2067197015","https://openalex.org/W2101993754","https://openalex.org/W2113654450","https://openalex.org/W2121120345","https://openalex.org/W2125930537","https://openalex.org/W2128906900","https://openalex.org/W2132267679","https://openalex.org/W2143972956","https://openalex.org/W2156438061","https://openalex.org/W2163200524","https://openalex.org/W2383800018","https://openalex.org/W2387730615","https://openalex.org/W2395811491","https://openalex.org/W2465439865","https://openalex.org/W2547880720","https://openalex.org/W2560719700","https://openalex.org/W2567758704","https://openalex.org/W2593886839","https://openalex.org/W2609077090","https://openalex.org/W2618083869","https://openalex.org/W2622445050","https://openalex.org/W2623331213","https://openalex.org/W2735039185","https://openalex.org/W2743136947","https://openalex.org/W2774320778","https://openalex.org/W2801201464","https://openalex.org/W2804199516","https://openalex.org/W2804860796","https://openalex.org/W2811199523","https://openalex.org/W2890554434","https://openalex.org/W2893801697","https://openalex.org/W2896457183","https://openalex.org/W2898504016","https://openalex.org/W2901082355","https://openalex.org/W2919115771","https://openalex.org/W2921476973","https://openalex.org/W2934268922","https://openalex.org/W2943295486","https://openalex.org/W2945957599","https://openalex.org/W2960912029","https://openalex.org/W2962978395","https://openalex.org/W2963270775","https://openalex.org/W2963420272","https://openalex.org/W2967073193","https://openalex.org/W2969786836","https://openalex.org/W2991391304","https://openalex.org/W3003394660","https://openalex.org/W3009268227","https://openalex.org/W3015281476","https://openalex.org/W3018699024","https://openalex.org/W3024167159","https://openalex.org/W3035665735","https://openalex.org/W3047725879","https://openalex.org/W3081260473","https://openalex.org/W3083219173","https://openalex.org/W3090240307","https://openalex.org/W3095867871","https://openalex.org/W3105636206","https://openalex.org/W3111241683","https://openalex.org/W3121754326","https://openalex.org/W3124067401","https://openalex.org/W3150573203","https://openalex.org/W3158827178","https://openalex.org/W3162672913","https://openalex.org/W3195858154","https://openalex.org/W3203699578","https://openalex.org/W3214248441","https://openalex.org/W4205921125","https://openalex.org/W4221145217","https://openalex.org/W4226445372","https://openalex.org/W4313156423","https://openalex.org/W4385245566","https://openalex.org/W6639824700","https://openalex.org/W6678818196","https://openalex.org/W6739901393","https://openalex.org/W6755207826","https://openalex.org/W6803870738","https://openalex.org/W7020680850"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W4256596923","https://openalex.org/W3037110488"],"abstract_inverted_index":{"With":[0],"a":[1,46,71,76,88,222],"plethora":[2],"of":[3,216],"remote":[4],"sensing":[5],"(RS)":[6],"images,":[7,86],"deep":[8],"neural":[9],"network-based":[10],"semantic":[11],"segmentation":[12],"models":[13],"(SegModels)":[14],"achieve":[15],"commendable":[16],"road":[17,35,51,57,82,94,133,139,156,190,224],"extraction":[18,58,225],"performance.":[19],"However,":[20],"the":[21,80,93,97,107,146,163,170,176,181,189,194,205,211,217,234],"occlusions":[22],"caused":[23],"by":[24,111],"vehicles,":[25],"roadside":[26],"objects":[27],"and":[28,53,87,137,161,196,213,227],"shadows":[29],"cannot":[30],"be":[31,100,126],"directly":[32],"identified":[33],"as":[34,116,135,141,231],"pixels,":[36],"especially":[37],"on":[38,45],"high-resolution":[39],"RS":[40,85],"images.":[41],"Therefore,":[42],"relying":[43],"only":[44],"single":[47],"SegModel":[48,77],"to":[49,78,91,125,154,165,175,187,233],"guarantee":[50],"connectivity":[52,195,212],"boundary":[54,197,214],"smoothness":[55,215],"in":[56],"tasks":[59],"is":[60,109,123],"extremely":[61],"difficult.":[62],"To":[63,143],"address":[64],"this":[65,67],"issue,":[66],"paper":[68],"puts":[69],"forward":[70],"\u201cSegmentation-with-Reconstruction\u201d":[72],"framework,":[73],"which":[74,184],"comprises":[75],"generate":[79],"binary":[81],"labels":[83,134,140,157],"from":[84],"reconstruction":[89],"model":[90,114],"refine":[92],"labels.":[95],"Specifically,":[96],"former":[98],"can":[99,208],"compatible":[101],"with":[102],"arbitrary":[103],"existing":[104],"SegModels,":[105],"while":[106,220],"latter":[108],"built":[110],"our":[112],"proposed":[113,206],"named":[115],"all-visible":[117],"denoising":[118],"auto-encoder":[119],"(AV-DAE).":[120],"The":[121],"AV-DAE":[122,164,171],"designed":[124],"an":[127],"encoder-decoder":[128],"architecture":[129],"that":[130,204],"takes":[131],"topology-corruption":[132],"inputs":[136],"true":[138],"outputs.":[142],"better":[144],"train":[145,162],"AV-DAE,":[147],"we":[148],"further":[149],"present":[150],"three":[151],"noise-adding":[152],"strategies":[153],"corrupt":[155],"for":[158],"diverse":[159],"patterns,":[160],"reconstruct":[166],"them.":[167],"Being":[168],"RS-image-agnostic,":[169],"pays":[172],"more":[173],"attention":[174],"spatial":[177],"features":[178],"rather":[179],"than":[180],"spectral":[182],"features,":[183],"enables":[185],"it":[186],"recover":[188],"topology":[191],"through":[192],"improving":[193],"smoothness.":[198],"Finally,":[199],"elaborate":[200],"simulation":[201],"results":[202],"demonstrate":[203],"framework":[207],"significantly":[209],"improve":[210],"extracted":[218],"roads,":[219],"achieving":[221],"competitive":[223],"performance":[226],"high":[228],"generalization":[229],"ability,":[230],"compared":[232],"benchmarks.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}