{"id":"https://openalex.org/W3082106469","doi":"https://doi.org/10.3390/rs12172817","title":"Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China","display_name":"Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China","publication_year":2020,"publication_date":"2020-08-31","ids":{"openalex":"https://openalex.org/W3082106469","doi":"https://doi.org/10.3390/rs12172817","mag":"3082106469"},"language":"en","primary_location":{"id":"doi:10.3390/rs12172817","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12172817","pdf_url":"https://www.mdpi.com/2072-4292/12/17/2817/pdf?version=1598865879","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/2072-4292/12/17/2817/pdf?version=1598865879","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050869421","display_name":"Wanliu Mao","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"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanliu Mao","raw_affiliation_strings":["Department of Land Management, Zhejiang University, Hangzhou 310058, China","Zhejiang Academy of Surveying and Mapping, Zhejiang 311100, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Land Management, Zhejiang University, Hangzhou 310058, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Zhejiang Academy of Surveying and Mapping, Zhejiang 311100, China","institution_ids":["https://openalex.org/I4210114963"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071529293","display_name":"Debin Lu","orcid":"https://orcid.org/0000-0002-7441-1828"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Debin Lu","raw_affiliation_strings":["Department of Land Management, Zhejiang University, Hangzhou 310058, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Land Management, Zhejiang University, Hangzhou 310058, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114164334","display_name":"Li Hou","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Hou","raw_affiliation_strings":["Department of Land Management, Zhejiang University, Hangzhou 310058, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Land Management, Zhejiang University, Hangzhou 310058, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100372175","display_name":"Xue Liu","orcid":"https://orcid.org/0000-0001-9406-2744"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Liu","raw_affiliation_strings":["Department of Land Management, Zhejiang University, Hangzhou 310058, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Land Management, Zhejiang University, Hangzhou 310058, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058672821","display_name":"Wenze Yue","orcid":"https://orcid.org/0000-0002-7533-3294"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenze Yue","raw_affiliation_strings":["Department of Land Management, Zhejiang University, Hangzhou 310058, China"],"raw_orcid":"https://orcid.org/0000-0002-7533-3294","affiliations":[{"raw_affiliation_string":"Department of Land Management, Zhejiang University, Hangzhou 310058, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058672821"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":3.3773,"has_fulltext":true,"cited_by_count":68,"citation_normalized_percentile":{"value":0.9358948,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"12","issue":"17","first_page":"2817","last_page":"2817"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9969000220298767,"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/T10226","display_name":"Land Use and Ecosystem Services","score":0.9969000220298767,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9937000274658203,"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/support-vector-machine","display_name":"Support vector machine","score":0.7435905933380127},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6305018663406372},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6237657070159912},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.5696808099746704},{"id":"https://openalex.org/keywords/urban-planning","display_name":"Urban planning","score":0.4695734679698944},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43848273158073425},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4369312524795532},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.423127681016922},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3517252206802368},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.24902969598770142},{"id":"https://openalex.org/keywords/civil-engineering","display_name":"Civil engineering","score":0.14270630478858948},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.06918779015541077}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7435905933380127},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6305018663406372},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6237657070159912},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.5696808099746704},{"id":"https://openalex.org/C49545453","wikidata":"https://www.wikidata.org/wiki/Q69883","display_name":"Urban planning","level":2,"score":0.4695734679698944},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43848273158073425},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4369312524795532},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.423127681016922},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3517252206802368},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.24902969598770142},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.14270630478858948},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.06918779015541077}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs12172817","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12172817","pdf_url":"https://www.mdpi.com/2072-4292/12/17/2817/pdf?version=1598865879","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:a8cdafbeaf414fe28dd05ff5f2c0bd40","is_oa":true,"landing_page_url":"https://doaj.org/article/a8cdafbeaf414fe28dd05ff5f2c0bd40","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 17, p 2817 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/12/17/2817/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs12172817","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 12; Issue 17; Pages: 2817","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs12172817","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12172817","pdf_url":"https://www.mdpi.com/2072-4292/12/17/2817/pdf?version=1598865879","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":[{"id":"https://metadata.un.org/sdg/11","score":0.5699999928474426,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G3635343852","display_name":"\u57ce\u5e02\u591a\u4e2d\u5fc3\u5f00\u53d1\u7684\u70ed\u5c9b\u54cd\u5e94\u673a\u5236\u4e0e\u9002\u5e94\u6027\u7b56\u7565\u6a21\u62df","funder_award_id":"41671533","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4833019754","display_name":"\u57ce\u5e02\u8513\u5ef6\u9a71\u52a8\u673a\u7406\u7684\u5fae\u89c2\u51b3\u7b56\u4ea4\u4e92\u6a21\u62df\u4e0e\u8c03\u63a7\u7b56\u7565\u7814\u7a76","funder_award_id":"41871169","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4842454620","display_name":null,"funder_award_id":"2017XZA216","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6351310804","display_name":null,"funder_award_id":"41671533 and 41871169","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"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3082106469.pdf","grobid_xml":"https://content.openalex.org/works/W3082106469.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W205877516","https://openalex.org/W1498436455","https://openalex.org/W1572417417","https://openalex.org/W2001564322","https://openalex.org/W2050571985","https://openalex.org/W2060594561","https://openalex.org/W2112325898","https://openalex.org/W2167634486","https://openalex.org/W2276327097","https://openalex.org/W2359817795","https://openalex.org/W2377203876","https://openalex.org/W2480801522","https://openalex.org/W2613571842","https://openalex.org/W2906604916","https://openalex.org/W2906910742","https://openalex.org/W2907981848","https://openalex.org/W2920254659","https://openalex.org/W2963333059","https://openalex.org/W2990323597","https://openalex.org/W2993303109","https://openalex.org/W3006512337","https://openalex.org/W3010776030","https://openalex.org/W3013386193","https://openalex.org/W3017059353","https://openalex.org/W3022527331","https://openalex.org/W3022716904","https://openalex.org/W3029149175","https://openalex.org/W3034812927","https://openalex.org/W3037382174","https://openalex.org/W3037757446","https://openalex.org/W3039045927","https://openalex.org/W3039295076","https://openalex.org/W3040094586","https://openalex.org/W3041298401","https://openalex.org/W3044282875","https://openalex.org/W3046976781","https://openalex.org/W4239510810","https://openalex.org/W4385161151","https://openalex.org/W6780002198","https://openalex.org/W7064770191"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W1924178503","https://openalex.org/W4396689146","https://openalex.org/W4200112873","https://openalex.org/W2955796858","https://openalex.org/W2004826645","https://openalex.org/W1898428141"],"abstract_inverted_index":{"Urban":[0],"land-use":[1,72,176,182,214,271,298,312,325],"information":[2,326],"is":[3,30],"important":[4],"for":[5,21,70,134,190,323],"urban":[6,26,71,88,175,297,324],"land-resource":[7],"planning":[8],"and":[9,60,96,124,138,145,154,156,160,201,234,252,288,317],"management.":[10,28],"However,":[11,162,259],"current":[12],"methods":[13,37,69,81,171,308],"using":[14],"traditional":[15],"surveys":[16],"cannot":[17],"meet":[18],"the":[19,22,40,47,52,86,103,131,135,163,166,170,174,179,184,191,199,207,211,216,226,229,242,245,249,260,264,268,279,295,304],"demand":[20],"rapid":[23],"development":[24],"of":[25,42,112,114,116,130,165,219,232,248,263,306],"land":[27,89,119,141,193,236,254],"There":[29],"an":[31,77],"urgent":[32],"need":[33],"to":[34,38,66,151],"develop":[35],"new":[36],"overcome":[39],"shortcomings":[41],"conventional":[43],"methods.":[44,196],"To":[45],"address":[46],"issue,":[48],"this":[49],"study":[50],"used":[51],"random":[53],"forest":[54],"(RF),":[55],"support":[56],"vector":[57],"machine":[58],"(SVM),":[59],"artificial":[61],"neural":[62],"network":[63],"(ANN)":[64],"models":[65,158,203,221],"build":[67],"machine-leaning":[68,80],"classification.":[73,299],"Taking":[74],"Hangzhou":[75],"as":[76],"example,":[78],"these":[79],"could":[82],"all":[83,195],"successfully":[84],"classify":[85],"essential":[87,296],"use":[90,142,305],"into":[91],"6":[92],"Level":[93,98,136,139,180,212,269],"I":[94,137,181],"classes":[95,100],"13":[97],"II":[99,140,213,270],"based":[101],"on":[102,173,267],"semantic":[104],"features":[105,111],"extracted":[106],"from":[107],"Sentinel-2A":[108],"images,":[109],"multi-source":[110],"types":[113,313],"points":[115],"interest":[117],"(POIs),":[118],"surface":[120],"temperature,":[121],"night":[122],"lights,":[123],"building":[125],"height.":[126],"The":[127,300],"validation":[128],"accuracy":[129,168,186,218,231,247,262],"RF":[132,200,227,280],"model":[133,266,281,290],"was":[143,187,222,237,256,273,291],"79.88%":[144],"71.89%,":[146],"respectively,":[147],"performing":[148],"better":[149,205,320],"compared":[150],"SVM":[152,202,243,286],"(78.40%":[153],"68.64%)":[155],"ANN":[157,208,265,289],"(71.30%":[159],"63.02%).":[161],"variations":[164],"user":[167,185,217,230,246,261],"among":[169],"depended":[172],"level.":[177],"For":[178,210],"classification,":[183,215],"high,":[188],"except":[189],"transportation":[192],"by":[194,285],"In":[197],"general,":[198],"performed":[204],"than":[206],"model.":[209],"different":[220],"quite":[223],"distinct.":[224],"With":[225],"model,":[228,244,287],"educational":[233,253],"medical":[235],"above":[238,257],"80%.":[239],"Moreover,":[240],"with":[241,314],"business":[250],"office":[251],"classification":[255,272],"75%.":[258],"poor.":[274],"Our":[275],"results":[276,301],"showed":[277],"that":[278,303],"performs":[282],"best,":[283],"followed":[284],"relatively":[292],"poor":[293],"in":[294],"proved":[302],"machine-learning":[307],"can":[309],"quickly":[310],"extract":[311],"high":[315],"accuracy,":[316],"provided":[318],"a":[319],"method":[321],"choice":[322],"acquisition.":[327]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":17},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
