{"id":"https://openalex.org/W2594796036","doi":"https://doi.org/10.3390/rs9030236","title":"Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas","display_name":"Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas","publication_year":2017,"publication_date":"2017-03-04","ids":{"openalex":"https://openalex.org/W2594796036","doi":"https://doi.org/10.3390/rs9030236","mag":"2594796036"},"language":"en","primary_location":{"id":"doi:10.3390/rs9030236","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs9030236","pdf_url":"https://www.mdpi.com/2072-4292/9/3/236/pdf?version=1488615386","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/2072-4292/9/3/236/pdf?version=1488615386","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100747582","display_name":"Xiaolong Ma","orcid":"https://orcid.org/0000-0002-5616-9767"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]},{"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":"Xiaolong Ma","raw_affiliation_strings":["College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China","Institute of Cartography and Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, China"],"raw_orcid":"https://orcid.org/0000-0002-5616-9767","affiliations":[{"raw_affiliation_string":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China","institution_ids":["https://openalex.org/I116953780"]},{"raw_affiliation_string":"Institute of Cartography and Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039348214","display_name":"Xiaohua Tong","orcid":"https://orcid.org/0000-0002-1045-3797"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaohua Tong","raw_affiliation_strings":["College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100684827","display_name":"Sicong Liu","orcid":"https://orcid.org/0000-0003-1612-4844"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sicong Liu","raw_affiliation_strings":["College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"],"raw_orcid":"https://orcid.org/0000-0003-1612-4844","affiliations":[{"raw_affiliation_string":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101921924","display_name":"Xin Luo","orcid":"https://orcid.org/0000-0003-4907-162X"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Luo","raw_affiliation_strings":["College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008163978","display_name":"Huan Xie","orcid":"https://orcid.org/0000-0003-3272-7848"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huan Xie","raw_affiliation_strings":["College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101808664","display_name":"Chengming Li","orcid":"https://orcid.org/0000-0002-9438-4871"},"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":true,"raw_author_name":"Chengming Li","raw_affiliation_strings":["Institute of Cartography and Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Cartography and Geographic Information System, Chinese Academy of Surveying and Mapping, Beijing 100830, China","institution_ids":["https://openalex.org/I4210114963"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5039348214","https://openalex.org/A5101808664"],"corresponding_institution_ids":["https://openalex.org/I116953780","https://openalex.org/I4210114963"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":4.8119,"has_fulltext":false,"cited_by_count":64,"citation_normalized_percentile":{"value":0.95257269,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"9","issue":"3","first_page":"236","last_page":"236"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11963","display_name":"Impact of Light on Environment and Health","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9878000020980835,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.6624636650085449},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6098053455352783},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5722482204437256},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.5471422672271729},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5445776581764221},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4975011646747589},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.46076324582099915},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.36834049224853516},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3553529381752014},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.270735502243042},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2538328468799591},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.22610357403755188},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.0933375358581543}],"concepts":[{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.6624636650085449},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6098053455352783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5722482204437256},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.5471422672271729},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5445776581764221},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4975011646747589},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.46076324582099915},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.36834049224853516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3553529381752014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.270735502243042},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2538328468799591},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.22610357403755188},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.0933375358581543},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs9030236","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs9030236","pdf_url":"https://www.mdpi.com/2072-4292/9/3/236/pdf?version=1488615386","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:4e98b57cdeb64f4584ec9b167038a055","is_oa":true,"landing_page_url":"https://doaj.org/article/4e98b57cdeb64f4584ec9b167038a055","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 9, Iss 3, p 236 (2017)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/9/3/236/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs9030236","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; Volume 9; Issue 3; Pages: 236","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs9030236","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs9030236","pdf_url":"https://www.mdpi.com/2072-4292/9/3/236/pdf?version=1488615386","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":0.8199999928474426,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G5713511748","display_name":null,"funder_award_id":"41631178, 41325005 and 41601354","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W2594796036.pdf"},"referenced_works_count":55,"referenced_works":["https://openalex.org/W252376446","https://openalex.org/W1005112471","https://openalex.org/W1619090422","https://openalex.org/W1969223470","https://openalex.org/W1974062086","https://openalex.org/W1977180815","https://openalex.org/W1978638151","https://openalex.org/W1983564473","https://openalex.org/W1986197640","https://openalex.org/W1988204220","https://openalex.org/W1991871367","https://openalex.org/W1991995284","https://openalex.org/W2001075407","https://openalex.org/W2004126227","https://openalex.org/W2006929658","https://openalex.org/W2007555690","https://openalex.org/W2015780109","https://openalex.org/W2017856447","https://openalex.org/W2029342456","https://openalex.org/W2030922582","https://openalex.org/W2033388392","https://openalex.org/W2034906331","https://openalex.org/W2043009944","https://openalex.org/W2047615348","https://openalex.org/W2049353036","https://openalex.org/W2050166462","https://openalex.org/W2050571985","https://openalex.org/W2067781444","https://openalex.org/W2067874135","https://openalex.org/W2069461688","https://openalex.org/W2087556827","https://openalex.org/W2088001257","https://openalex.org/W2091793895","https://openalex.org/W2108640453","https://openalex.org/W2122061073","https://openalex.org/W2131651374","https://openalex.org/W2134451879","https://openalex.org/W2135803256","https://openalex.org/W2145874974","https://openalex.org/W2147280166","https://openalex.org/W2154028886","https://openalex.org/W2226798190","https://openalex.org/W2230486561","https://openalex.org/W2269396833","https://openalex.org/W2319892480","https://openalex.org/W2336496425","https://openalex.org/W2347036094","https://openalex.org/W2390624765","https://openalex.org/W2390982033","https://openalex.org/W2404325781","https://openalex.org/W2462332352","https://openalex.org/W2497487572","https://openalex.org/W3150806421","https://openalex.org/W6642288624","https://openalex.org/W7015897505"],"related_works":["https://openalex.org/W3207046288","https://openalex.org/W3023446922","https://openalex.org/W4324030030","https://openalex.org/W1980260791","https://openalex.org/W4385533602","https://openalex.org/W3189212133","https://openalex.org/W4382239404","https://openalex.org/W4382519838","https://openalex.org/W2021379394","https://openalex.org/W2088899772"],"abstract_inverted_index":{"The":[0,78],"accuracy":[1,32,249,280,288],"of":[2,25,33,52,82,106,124,165,189,229,272,310,339],"training":[3,171,238],"samples":[4],"used":[5],"for":[6,44,61,176,186,256,291,330],"data":[7,48,76,91,156,166,207,211],"classification":[8,92,151],"methods,":[9,213],"such":[10,214],"as":[11,146,215,276],"support":[12],"vector":[13],"machines":[14],"(SVMs),":[15],"has":[16],"had":[17,268],"a":[18,41,50,71,269,327,336],"considerable":[19],"positive":[20],"impact":[21],"on":[22,100,128],"the":[23,31,53,101,110,121,131,143,147,159,170,187,216,221,230,236,243,247,258,265,283,298,304,308,318],"results":[24,232,320],"urban":[26,34,46,153,196,332],"area":[27,36,47,155],"extractions.":[28],"To":[29],"improve":[30],"built-up":[35,94,97,154,190,333],"extractions,":[37],"this":[38,183],"paper":[39],"presents":[40],"sample-optimized":[42],"approach":[43,80,185,325],"classifying":[45],"using":[49,130,158,182,195,242],"combination":[51],"Defense":[54],"Meteorological":[55],"Satellite":[56],"Program-Operational":[57],"Linescan":[58],"System":[59],"(DMSP-OLS)":[60],"nighttime":[62],"light":[63],"data,":[64,112],"Landsat":[65,201],"images,":[66],"and":[67,90,95,103,118,138,152,200,203,220,246,260,285,296,303],"GlobeLand30,":[68],"which":[69,192],"is":[70,326],"30-m":[72],"global":[73],"land":[74],"cover":[75],"product.":[77],"proposed":[79,184,244,266,324],"consists":[81],"three":[83,163],"main":[84],"components:":[85],"(1)":[86,235],"initial":[87],"sample":[88,116,237,262],"generation":[89],"into":[93],"non-urban":[96],"areas":[98,334],"based":[99,127],"maximum":[102],"minimum":[104],"intervals":[105],"digital":[107],"numbers":[108],"from":[109,142,209,252,313],"DMSP-OLS":[111],"respectively;":[113],"(2)":[114,264],"refined":[115],"selection":[117],"optimization":[119],"by":[120,278],"probability":[122],"threshold":[123,217],"each":[125],"pixel":[126],"vegetation-cover,":[129],"Landsat-derived":[132],"normalized":[133],"differential":[134],"vegetation":[135],"index":[136],"(NDVI)":[137],"artificial":[139,292],"surfaces":[140,293],"extracted":[141],"GlobeLand30":[144],"product":[145],"constraints;":[148],"(3)":[149,282],"iterative":[150],"extraction":[157,188],"relationship":[160],"between":[161],"these":[162],"aspects":[164],"collection":[167,212],"together":[168],"with":[169,206,335],"sets.":[172],"Experiments":[173],"were":[174,193,204,289],"conducted":[175],"several":[177],"cities":[178],"in":[179,294,307],"western":[180],"China":[181],"areas,":[191],"classified":[194],"construction":[197],"statistical":[198],"yearbooks":[199],"images":[202],"compared":[205],"obtained":[208],"traditional":[210],"dichotomy":[218],"method":[219,267],"improved":[222,241,302],"neighborhood":[223],"focal":[224],"statistics":[225],"method.":[226],"An":[227],"analysis":[228],"empirical":[231],"indicated":[233],"that":[234,322],"process":[239],"was":[240],"method,":[245],"overall":[248,284],"(OA)":[250],"increased":[251,312],"89%":[253],"to":[254,315],"96%":[255],"both":[257],"optimized":[259],"non-optimized":[261],"selection;":[263],"relative":[270],"error":[271],"less":[273],"than":[274],"10%,":[275],"calculated":[277],"an":[279],"assessment;":[281],"individual":[286],"class":[287],"higher":[290],"GlobeLand30;":[295],"(4)":[297],"average":[299],"OA":[300],"obviously":[301],"Kappa":[305],"coefficient":[306],"case":[309],"Chengdu":[311],"0.54":[314],"0.80.":[316],"Therefore,":[317],"experimental":[319],"demonstrated":[321],"our":[323],"reliable":[328],"solution":[329],"extracting":[331],"high":[337],"degree":[338],"accuracy.":[340]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":10},{"year":2017,"cited_by_count":4}],"updated_date":"2026-05-22T06:13:13.366637","created_date":"2017-03-16T00:00:00"}