{"id":"https://openalex.org/W2987325095","doi":"https://doi.org/10.1109/igarss.2019.8899115","title":"Multi-Scale Enhanced Deep Network for Road Detection","display_name":"Multi-Scale Enhanced Deep Network for Road Detection","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2987325095","doi":"https://doi.org/10.1109/igarss.2019.8899115","mag":"2987325095"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8899115","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899115","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5011042925","display_name":"Xiaoyan Lu","orcid":"https://orcid.org/0000-0002-9026-8195"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyan Lu","raw_affiliation_strings":["Wuhan University,State Key Laboratory of Information Engineering in Surveying,Wuhan,China,430079","State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,State Key Laboratory of Information Engineering in Surveying,Wuhan,China,430079","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]},{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100754351","display_name":"Yanfei Zhong","orcid":"https://orcid.org/0000-0001-9446-5850"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanfei Zhong","raw_affiliation_strings":["Wuhan University,State Key Laboratory of Information Engineering in Surveying,Wuhan,China,430079","State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan University,State Key Laboratory of Information Engineering in Surveying,Wuhan,China,430079","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]},{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041081010","display_name":"Ji Zhao","orcid":"https://orcid.org/0000-0001-9039-2789"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Zhao","raw_affiliation_strings":["China University of Geosciences,School of Computer Science,Wuhan,China,430074","School of Computer Science, China University of Geosciences, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China University of Geosciences,School of Computer Science,Wuhan,China,430074","institution_ids":["https://openalex.org/I3124059619"]},{"raw_affiliation_string":"School of Computer Science, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7038,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.90570957,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3947","last_page":"3950"},"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":0.9986000061035156,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9965000152587891,"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":0.7951383590698242},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.7507193088531494},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.6429576277732849},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6312560439109802},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5664717555046082},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.544120192527771},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5314167141914368},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4958074986934662},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.42421141266822815},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3990514278411865},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3721528649330139},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.09274956583976746},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08263850212097168}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7951383590698242},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7507193088531494},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.6429576277732849},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6312560439109802},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5664717555046082},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.544120192527771},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5314167141914368},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4958074986934662},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.42421141266822815},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3990514278411865},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3721528649330139},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09274956583976746},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08263850212097168},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8899115","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899115","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2032850818","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2244429148","https://openalex.org/W2593886839","https://openalex.org/W2623331213","https://openalex.org/W2630837129","https://openalex.org/W2899771611","https://openalex.org/W2962978395","https://openalex.org/W2963446712","https://openalex.org/W6639824700","https://openalex.org/W6684191040","https://openalex.org/W6739696289","https://openalex.org/W6756040250","https://openalex.org/W7020680850"],"related_works":["https://openalex.org/W4401096132","https://openalex.org/W2022849497","https://openalex.org/W3081299480","https://openalex.org/W2407190427","https://openalex.org/W2919210741","https://openalex.org/W2907584218","https://openalex.org/W4226493464","https://openalex.org/W3002446410","https://openalex.org/W3133861977","https://openalex.org/W4390224712"],"abstract_inverted_index":{"Road":[0],"detection":[1,45],"is":[2,48,90],"a":[3,41,101],"hot":[4],"research":[5],"topic":[6],"in":[7,20],"the":[8,53,76,81,109,121],"very":[9],"high":[10],"resolution":[11],"(VHR)":[12],"remote":[13],"sensing":[14],"field":[15],"and":[16,34,59,75],"has":[17,63],"been":[18,29],"applied":[19],"various":[21],"practical":[22],"applications.":[23],"Many":[24],"deep-learning":[25],"based":[26,51],"methods":[27],"have":[28],"used":[30,106],"to":[31,71,92,107],"detect":[32],"roads":[33],"achieved":[35],"good":[36],"performance.":[37],"In":[38],"this":[39],"paper,":[40],"multi-scale":[42,95],"enhanced":[43],"road":[44,98,103],"framework":[46],"(DenseUNet)":[47],"proposed":[49,110,118],"which":[50,124],"on":[52],"densely":[54],"connected":[55],"convolutional":[56],"networks":[57],"(DenseNet)":[58],"U-Net.":[60],"The":[61,117],"U-Net":[62],"strong":[64],"capabilities":[65],"of":[66],"preserving":[67],"spatial":[68,86],"details":[69],"due":[70],"its":[72,126],"skip":[73],"connections,":[74],"DenseNet":[77],"can":[78],"better":[79],"optimize":[80],"deep":[82],"network.":[83],"Meanwhile,":[84],"atrous":[85],"pyramid":[87],"pooling":[88],"(ASPP)":[89],"employed":[91],"effectively":[93],"capture":[94],"features":[96],"for":[97],"detection.":[99],"Finally,":[100],"public":[102],"dataset":[104],"was":[105],"verify":[108],"approach,":[111],"compared":[112],"with":[113],"other":[114],"state-of-the-art":[115],"methods.":[116],"method":[119],"achieve":[120],"best":[122],"performance,":[123],"illustrates":[125],"superiority.":[127]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
