{"id":"https://openalex.org/W3095216905","doi":"https://doi.org/10.3390/rs12213612","title":"An OSM Data-Driven Method for Road-Positive Sample Creation","display_name":"An OSM Data-Driven Method for Road-Positive Sample Creation","publication_year":2020,"publication_date":"2020-11-03","ids":{"openalex":"https://openalex.org/W3095216905","doi":"https://doi.org/10.3390/rs12213612","mag":"3095216905"},"language":"en","primary_location":{"id":"doi:10.3390/rs12213612","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12213612","pdf_url":"https://www.mdpi.com/2072-4292/12/21/3612/pdf?version=1604505275","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/12/21/3612/pdf?version=1604505275","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075721700","display_name":"Jiguang Dai","orcid":"https://orcid.org/0000-0003-1539-0673"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]},{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4210104879","display_name":"China Land Surveying and Planning Institute","ror":"https://ror.org/01dqnej95","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104879"]},{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiguang Dai","raw_affiliation_strings":["Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China","China Land Surveying and Planning Institute, Beijing 100035, China","Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 123000, China","Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources, Beijing 100036, China","School of Geomatics, Liaoning Technical University, Fuxin 123000, China","The Chinese Academy of Surveying and Mapping, Beijing 100036, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China","institution_ids":[]},{"raw_affiliation_string":"China Land Surveying and Planning Institute, Beijing 100035, China","institution_ids":["https://openalex.org/I4210104879"]},{"raw_affiliation_string":"Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 123000, China","institution_ids":["https://openalex.org/I176808543"]},{"raw_affiliation_string":"Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources, Beijing 100036, China","institution_ids":["https://openalex.org/I4210114963","https://openalex.org/I211433327"]},{"raw_affiliation_string":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China","institution_ids":["https://openalex.org/I176808543"]},{"raw_affiliation_string":"The Chinese Academy of Surveying and Mapping, Beijing 100036, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324617","display_name":"Chengcheng Li","orcid":"https://orcid.org/0000-0002-9298-7265"},"institutions":[{"id":"https://openalex.org/I176808543","display_name":"Liaoning Technical University","ror":"https://ror.org/01n2bd587","country_code":"CN","type":"education","lineage":["https://openalex.org/I176808543"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengcheng Li","raw_affiliation_strings":["Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 123000, China","School of Geomatics, Liaoning Technical University, Fuxin 123000, China"],"raw_orcid":"https://orcid.org/0000-0002-9298-7265","affiliations":[{"raw_affiliation_string":"Institute of Spatiotemporal Transportation Data, Liaoning Technical University, Fuxin 123000, China","institution_ids":["https://openalex.org/I176808543"]},{"raw_affiliation_string":"School of Geomatics, Liaoning Technical University, Fuxin 123000, China","institution_ids":["https://openalex.org/I176808543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017983472","display_name":"Yuqiang Zuo","orcid":"https://orcid.org/0000-0003-1317-562X"},"institutions":[{"id":"https://openalex.org/I4210104879","display_name":"China Land Surveying and Planning Institute","ror":"https://ror.org/01dqnej95","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210104879"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqiang Zuo","raw_affiliation_strings":["China Land Surveying and Planning Institute, Beijing 100035, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Land Surveying and Planning Institute, Beijing 100035, China","institution_ids":["https://openalex.org/I4210104879"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002755226","display_name":"Haibin Ai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114963","display_name":"Chinese Academy of Surveying and Mapping","ror":"https://ror.org/02j693n47","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114963"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibin Ai","raw_affiliation_strings":["The Chinese Academy of Surveying and Mapping, Beijing 100036, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Chinese Academy of Surveying and Mapping, Beijing 100036, China","institution_ids":["https://openalex.org/I4210114963"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100324617"],"corresponding_institution_ids":["https://openalex.org/I176808543"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.181,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.53378155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"12","issue":"21","first_page":"3612","last_page":"3612"},"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":0.9984999895095825,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9811999797821045,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7034100294113159},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5289295315742493},{"id":"https://openalex.org/keywords/ransac","display_name":"RANSAC","score":0.5096521377563477},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4989173412322998},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4820757508277893},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4339383840560913},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.41386300325393677},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36756110191345215},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3552347421646118},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2055233120918274},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18298375606536865}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7034100294113159},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5289295315742493},{"id":"https://openalex.org/C114744707","wikidata":"https://www.wikidata.org/wiki/Q218533","display_name":"RANSAC","level":3,"score":0.5096521377563477},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4989173412322998},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4820757508277893},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4339383840560913},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.41386300325393677},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36756110191345215},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3552347421646118},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2055233120918274},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18298375606536865},{"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},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs12213612","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12213612","pdf_url":"https://www.mdpi.com/2072-4292/12/21/3612/pdf?version=1604505275","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:da481f2ec5b84877889adc0552b01e9c","is_oa":true,"landing_page_url":"https://doaj.org/article/da481f2ec5b84877889adc0552b01e9c","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":"Remote Sensing, Vol 12, Iss 21, p 3612 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/12/21/3612/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs12213612","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs12213612","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12213612","pdf_url":"https://www.mdpi.com/2072-4292/12/21/3612/pdf?version=1604505275","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"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":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.75,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1833945193","display_name":null,"funder_award_id":"KLSMNR-202004","funder_id":"https://openalex.org/F4320316090","funder_display_name":"Ministry of Natural Resources of the People's Republic of China"},{"id":"https://openalex.org/G2507855583","display_name":null,"funder_award_id":"202004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3373275838","display_name":null,"funder_award_id":"42071428","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8501556841","display_name":null,"funder_award_id":"420713743","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320316090","display_name":"Ministry of Natural Resources of the People's Republic of China","ror":null},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3095216905.pdf","grobid_xml":"https://content.openalex.org/works/W3095216905.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W22745672","https://openalex.org/W116962720","https://openalex.org/W1479807131","https://openalex.org/W1575925448","https://openalex.org/W1982574519","https://openalex.org/W1987577389","https://openalex.org/W1989750313","https://openalex.org/W1992154601","https://openalex.org/W2048679005","https://openalex.org/W2058207201","https://openalex.org/W2085261163","https://openalex.org/W2085443648","https://openalex.org/W2094455438","https://openalex.org/W2097089247","https://openalex.org/W2103079830","https://openalex.org/W2104095591","https://openalex.org/W2104290444","https://openalex.org/W2119082135","https://openalex.org/W2119991813","https://openalex.org/W2121924526","https://openalex.org/W2136439324","https://openalex.org/W2138317821","https://openalex.org/W2139823104","https://openalex.org/W2154455818","https://openalex.org/W2156044350","https://openalex.org/W2342699585","https://openalex.org/W2390127744","https://openalex.org/W2461725797","https://openalex.org/W2594203750","https://openalex.org/W2595964094","https://openalex.org/W2620899671","https://openalex.org/W2744013902","https://openalex.org/W2766504121","https://openalex.org/W2774320778","https://openalex.org/W2810321647","https://openalex.org/W2890554434","https://openalex.org/W2896551720","https://openalex.org/W2910123878","https://openalex.org/W2918288062","https://openalex.org/W2994345572","https://openalex.org/W2995914831","https://openalex.org/W2997701990","https://openalex.org/W3003703043","https://openalex.org/W4403783248","https://openalex.org/W6675747103","https://openalex.org/W6682494755","https://openalex.org/W6746704917","https://openalex.org/W6771335894"],"related_works":["https://openalex.org/W2131378265","https://openalex.org/W2984240274","https://openalex.org/W2981196697","https://openalex.org/W3012182724","https://openalex.org/W2115876589","https://openalex.org/W1988708904","https://openalex.org/W2091196744","https://openalex.org/W3083084699","https://openalex.org/W2039208430","https://openalex.org/W3145778961"],"abstract_inverted_index":{"Determining":[0],"samples":[1,17,212],"is":[2,55,100,114,139],"considered":[3],"to":[4,57,77,102,116,141,159,172,191],"be":[5],"a":[6,48,64,96,133],"precondition":[7],"in":[8,216],"deep":[9,27],"network":[10],"training":[11],"and":[12,69,83,146,151,167,186,196,220],"learning,":[13],"but":[14],"at":[15],"present,":[16],"are":[18,75,157,170,181],"usually":[19],"created":[20],"manually,":[21],"which":[22],"limits":[23],"the":[24,45,59,79,85,88,93,104,118,126,129,143,147,161,164,174,177,193,206],"application":[25],"of":[26,87,125,176,189,218],"networks.":[28],"Therefore,":[29],"this":[30],"article":[31],"proposes":[32],"an":[33,110],"OpenStreetMap":[34],"(OSM)":[35],"data-driven":[36],"method":[37,195,208],"for":[38,209],"creating":[39,210],"road-positive":[40,178,211],"samples.":[41,179],"First,":[42],"based":[43],"on":[44,84,122,183],"OSM":[46],"data,":[47],"line":[49,107],"segment":[50],"orientation":[51],"histogram":[52],"(LSOH)":[53],"model":[54,74,138,150],"constructed":[56,76],"determine":[58,103,142],"local":[60,80,89,105,119,134],"road":[61,65,70,81,90,106,120,130,144,162,165,168],"direction.":[62],"Secondly,":[63],"homogeneity":[66],"constraint":[67,98],"rule":[68,99],"texture":[71,135],"feature":[72],"statistical":[73],"extract":[78,160],"line,":[82],"basis":[86],"lines":[91,121],"with":[92,199],"same":[94],"direction,":[95],"polar":[97],"proposed":[101,194,207],"set.":[108],"Then,":[109],"iterative":[111],"interpolation":[112],"algorithm":[113,156],"used":[115,158,171],"connect":[117],"both":[123],"sides":[124],"gaps":[127],"between":[128],"lines.":[131],"Finally,":[132],"self-similarity":[136],"(LTSS)":[137],"implemented":[140],"width,":[145],"centerpoint":[148],"autocorrection":[149],"random":[152],"sample":[153],"consensus":[154],"(RANSAC)":[155],"centerline;":[163],"width":[166],"centerline":[169],"complete":[173],"creation":[175],"Experiments":[180],"conducted":[182],"different":[184,187],"scenes":[185],"types":[188],"images":[190],"demonstrate":[192,204],"compare":[197],"it":[198],"other":[200],"approaches.":[201],"The":[202],"results":[203],"that":[205],"has":[213],"great":[214],"advantages":[215],"terms":[217],"accuracy":[219],"integrity.":[221]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
