{"id":"https://openalex.org/W4394864340","doi":"https://doi.org/10.1145/3650215.3650325","title":"Land Use Classification of Shenyang Based on Spatial Data","display_name":"Land Use Classification of Shenyang Based on Spatial Data","publication_year":2023,"publication_date":"2023-10-27","ids":{"openalex":"https://openalex.org/W4394864340","doi":"https://doi.org/10.1145/3650215.3650325"},"language":"en","primary_location":{"id":"doi:10.1145/3650215.3650325","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650215.3650325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 4th International Conference on Machine Learning and Computer Application","raw_type":"proceedings-article"},"type":"article","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/A5044806685","display_name":"Ruizhao Liu","orcid":"https://orcid.org/0009-0003-8416-7536"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruizhao Liu","raw_affiliation_strings":["Shenyang Jianzhu University, Shenyang, Liaoning, China, China"],"raw_orcid":"https://orcid.org/0009-0003-8416-7536","affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, Shenyang, Liaoning, China, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014043681","display_name":"Zhiwei Xie","orcid":"https://orcid.org/0000-0003-0505-5724"},"institutions":[{"id":"https://openalex.org/I83714178","display_name":"Shenyang Jianzhu University","ror":"https://ror.org/01zr73v18","country_code":"CN","type":"education","lineage":["https://openalex.org/I83714178"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiwei Xie","raw_affiliation_strings":["Shenyang Jianzhu University, Shenyang, Liaoning, China, China"],"raw_orcid":"https://orcid.org/0000-0003-0505-5724","affiliations":[{"raw_affiliation_string":"Shenyang Jianzhu University, Shenyang, Liaoning, China, China","institution_ids":["https://openalex.org/I83714178"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5095739566","display_name":"Xinyv Lv","orcid":"https://orcid.org/0009-0007-5295-6281"},"institutions":[{"id":"https://openalex.org/I4210135994","display_name":"China Railway Group (China)","ror":"https://ror.org/03za3eq42","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210135994"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyv Lv","raw_affiliation_strings":["China Railway Jiuju Group Seventh Engineering Co., Ltd, Shenyang, Liaoning, China, China"],"raw_orcid":"https://orcid.org/0009-0007-5295-6281","affiliations":[{"raw_affiliation_string":"China Railway Jiuju Group Seventh Engineering Co., Ltd, Shenyang, Liaoning, China, China","institution_ids":["https://openalex.org/I4210135994"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103050949","display_name":"Bo Peng","orcid":"https://orcid.org/0009-0000-4531-700X"},"institutions":[{"id":"https://openalex.org/I4210097538","display_name":"Heilongjiang Vocational Institute of Ecological Engineering","ror":"https://ror.org/00xqhy713","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210097538"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Peng","raw_affiliation_strings":["Liaoning Vocational College of Ecological Engineering, Shenyang, Liaoning, China, China"],"raw_orcid":"https://orcid.org/0009-0000-4531-700X","affiliations":[{"raw_affiliation_string":"Liaoning Vocational College of Ecological Engineering, Shenyang, Liaoning, China, China","institution_ids":["https://openalex.org/I4210097538"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5044806685"],"corresponding_institution_ids":["https://openalex.org/I83714178"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21714608,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"622","last_page":"627"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9781000018119812,"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"}},"topics":[{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9781000018119812,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9761000275611877,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9434999823570251,"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/word2vec","display_name":"Word2vec","score":0.6227356195449829},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5286705493927002},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5193906426429749},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4706917107105255},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.43435537815093994},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.4312148094177246},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4306308627128601},{"id":"https://openalex.org/keywords/point-of-interest","display_name":"Point of interest","score":0.4166712462902069},{"id":"https://openalex.org/keywords/urban-area","display_name":"Urban area","score":0.41585856676101685},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.36272603273391724},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3585701882839203},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3508504629135132},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3449188768863678},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18606314063072205}],"concepts":[{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.6227356195449829},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5286705493927002},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5193906426429749},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4706917107105255},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.43435537815093994},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.4312148094177246},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4306308627128601},{"id":"https://openalex.org/C150140777","wikidata":"https://www.wikidata.org/wiki/Q960648","display_name":"Point of interest","level":2,"score":0.4166712462902069},{"id":"https://openalex.org/C2778368647","wikidata":"https://www.wikidata.org/wiki/Q702492","display_name":"Urban area","level":2,"score":0.41585856676101685},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.36272603273391724},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3585701882839203},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3508504629135132},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3449188768863678},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18606314063072205},{"id":"https://openalex.org/C136264566","wikidata":"https://www.wikidata.org/wiki/Q159810","display_name":"Economy","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3650215.3650325","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3650215.3650325","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 4th International Conference on Machine Learning and Computer Application","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8299999833106995,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2061569531","https://openalex.org/W2742878349","https://openalex.org/W3072767238"],"related_works":["https://openalex.org/W2980729574","https://openalex.org/W1560851690","https://openalex.org/W3092047717","https://openalex.org/W4390881630","https://openalex.org/W2770162183","https://openalex.org/W3110772647","https://openalex.org/W2947721150","https://openalex.org/W2894231409","https://openalex.org/W3127365535","https://openalex.org/W2995297654"],"abstract_inverted_index":{"In":[0,99],"order":[1],"to":[2,5,46,60,80,90,191],"be":[3,192],"able":[4],"carry":[6],"out":[7],"fine":[8,25],"urban":[9,15,26,92],"management":[10],"by":[11,76,96],"the":[12,48,51,71,77,84,109,113,132,149,152,155,159,169,174,179,197],"results":[13,95,129],"of":[14,55,70,117,122,151,166,200],"land":[16,27,93],"use":[17,28],"classification,":[18],"this":[19,100],"paper":[20],"designs":[21],"and":[22,36,50,83,112,119,135,145,161,171,176,186,194],"implements":[23],"a":[24,62,142,163],"classification":[29,42,94],"method":[30,43],"based":[31],"on":[32,141],"remote":[33,114],"sensing":[34,115],"images":[35],"spatial":[37],"data.":[38],"The":[39,67,127,181],"nearest":[40],"neighbor":[41],"is":[44,58,88,106,158],"used":[45,59,89],"classify":[47],"images,":[49],"weighted":[52,63,85],"BOW":[53,86],"(Bag":[54],"Words)":[56],"model":[57,82,87],"generate":[61],"word":[64],"frequency":[65],"vector.":[66],"innovative":[68],"combination":[69],"type-word":[72],"similarity":[73],"matrix":[74],"generated":[75],"Word2vec":[78],"(Word":[79],"Vector)":[81],"extract":[91],"vector":[97],"calculation.":[98],"paper,":[101],"Shenyang,":[102],"Liaoning":[103],"Province,":[104],"China,":[105],"taken":[107],"as":[108],"study":[110],"area,":[111,154],"image":[116],"Gaofen-2(GF-2)":[118],"POI":[120],"(Point":[121],"Interesting)":[123],"data":[124],"are":[125,139],"used.":[126],"experimental":[128],"prove":[130],"that":[131],"residential":[133],"areas":[134],"companies":[136],"in":[137,148,168,173,178,196],"Shenyang":[138],"concentrated":[140,147,195],"large":[143],"area":[144,157],"mainly":[146],"northeast":[150],"design":[153],"commercial":[156],"smallest":[160],"shows":[162],"distribution":[164],"pattern":[165],"more":[167,177],"west":[170],"less":[172],"east":[175],"north.":[180],"Land":[182],"for":[183],"government":[184],"agency":[185],"public":[187],"life":[188],"service":[189],"tends":[190],"scattered":[193],"southeaster":[198],"part":[199],"Shenbei":[201],"New":[202],"Area.":[203]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}