{"id":"https://openalex.org/W2569842528","doi":"https://doi.org/10.1109/icsai.2016.7811092","title":"Automatic extraction of road regions of interest(ROI) from very high resolution remote sensing images","display_name":"Automatic extraction of road regions of interest(ROI) from very high resolution remote sensing images","publication_year":2016,"publication_date":"2016-11-01","ids":{"openalex":"https://openalex.org/W2569842528","doi":"https://doi.org/10.1109/icsai.2016.7811092","mag":"2569842528"},"language":"en","primary_location":{"id":"doi:10.1109/icsai.2016.7811092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2016.7811092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 3rd International Conference on Systems and Informatics (ICSAI)","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/A5101781487","display_name":"Lv Ye","orcid":"https://orcid.org/0000-0001-7059-1211"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Lv","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, P.R. China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100389615","display_name":"Guofeng Wang","orcid":"https://orcid.org/0000-0001-5751-6364"},"institutions":[{"id":"https://openalex.org/I4210103140","display_name":"CCCC Highway Consultants (China)","ror":"https://ror.org/01bs84e19","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210103140"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guofeng Wang","raw_affiliation_strings":["China Highway Engineering Consulting Corporation, Beijing, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Highway Engineering Consulting Corporation, Beijing, P. R. China","institution_ids":["https://openalex.org/I4210103140"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004788238","display_name":"Xiangyun Hu","orcid":"https://orcid.org/0000-0003-3623-8304"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyun Hu","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, P.R. China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":null,"first_page":"976","last_page":"981"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9998999834060669,"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":0.9998999834060669,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9861000180244446,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9848999977111816,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.832034170627594},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7543617486953735},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6263710260391235},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6248179078102112},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5995471477508545},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.5247783064842224},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34448695182800293}],"concepts":[{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.832034170627594},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7543617486953735},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6263710260391235},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6248179078102112},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5995471477508545},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.5247783064842224},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34448695182800293},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsai.2016.7811092","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2016.7811092","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 3rd International Conference on Systems and Informatics (ICSAI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W73112891","https://openalex.org/W1517818437","https://openalex.org/W1555849598","https://openalex.org/W1978164061","https://openalex.org/W1979363725","https://openalex.org/W2015785930","https://openalex.org/W2020321370","https://openalex.org/W2049769803","https://openalex.org/W2076938548","https://openalex.org/W2089970463","https://openalex.org/W2106652846","https://openalex.org/W2107634464","https://openalex.org/W2121120345","https://openalex.org/W2155226776","https://openalex.org/W2157738829","https://openalex.org/W2160137407","https://openalex.org/W2161877343","https://openalex.org/W2164598857","https://openalex.org/W2168014483","https://openalex.org/W6602970364","https://openalex.org/W6682859550","https://openalex.org/W6684905197"],"related_works":["https://openalex.org/W2601157893","https://openalex.org/W2131735617","https://openalex.org/W2373006798","https://openalex.org/W2056912418","https://openalex.org/W2123759770","https://openalex.org/W2033213769","https://openalex.org/W2811390910","https://openalex.org/W4312376745","https://openalex.org/W2082269393","https://openalex.org/W2043960970"],"abstract_inverted_index":{"Road":[0],"region":[1,50],"of":[2,6,51,72,123],"interest(ROI)":[3],"extraction":[4,24,43,118],"is":[5,30,45,108,132],"great":[7],"use":[8,76],"when":[9,112],"dealing":[10,113],"with":[11,114],"related":[12],"CV":[13],"tasks,":[14],"such":[15],"as":[16],"car":[17],"extraction,":[18,21],"road":[19,49,65,84,88,93,116],"facilities":[20],"traffic":[22],"volume":[23],"and":[25,79],"so":[26],"on.":[27],"However,":[28],"there":[29],"little":[31],"exploration":[32],"in":[33,98,104],"this":[34,37],"area.":[35],"In":[36],"paper,":[38],"an":[39,109],"effective":[40],"automatic":[41],"ROI":[42,94,117],"method":[44,100],"proposed":[46],"to":[47,68,86],"extract":[48],"interest":[52],"(ROI)":[53],"from":[54],"high":[55],"resolution":[56],"remote":[57],"sensing":[58],"images.":[59],"First,":[60],"we":[61,75],"search":[62,80],"for":[63,81,92],"possible":[64],"area":[66,89],"according":[67],"directional":[69],"saliency":[70],"feature":[71,111],"road.":[73],"Next,":[74],"haar":[77],"filters":[78],"locally":[82],"identical":[83],"features":[85],"detect":[87],"more":[90],"precisely":[91],"extraction.":[95],"Both":[96],"parts":[97],"our":[99,130],"can":[101],"be":[102],"implemented":[103],"parallelized":[105],"pattern":[106],"which":[107],"attractive":[110],"massive":[115],"task":[119],"given":[120],"a":[121,133],"lot":[122],"computational":[124],"resources.":[125],"And":[126],"results":[127],"prove":[128],"that":[129],"approach":[131],"promising":[134],"research":[135],"direction.":[136]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}