{"id":"https://openalex.org/W4303980603","doi":"https://doi.org/10.3390/rs14195027","title":"CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation","display_name":"CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation","publication_year":2022,"publication_date":"2022-10-09","ids":{"openalex":"https://openalex.org/W4303980603","doi":"https://doi.org/10.3390/rs14195027"},"language":"en","primary_location":{"id":"doi:10.3390/rs14195027","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14195027","pdf_url":"https://www.mdpi.com/2072-4292/14/19/5027/pdf?version=1665401623","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/2072-4292/14/19/5027/pdf?version=1665401623","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032424181","display_name":"Zhiqiang Liu","orcid":"https://orcid.org/0000-0001-8185-4945"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi-Qiang Liu","raw_affiliation_strings":["Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China","University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"],"raw_orcid":"https://orcid.org/0000-0001-8185-4945","affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences (UCAS), Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101748090","display_name":"Ping Tang","orcid":"https://orcid.org/0000-0002-8721-4209"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Tang","raw_affiliation_strings":["Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103138677","display_name":"Weixiong Zhang","orcid":"https://orcid.org/0000-0003-3730-9223"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weixiong Zhang","raw_affiliation_strings":["Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China","University of Chinese Academy of Sciences (UCAS), Beijing 100049, China"],"raw_orcid":"https://orcid.org/0000-0003-3730-9223","affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences (UCAS), Beijing 100049, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100701053","display_name":"Zheng Zhang","orcid":"https://orcid.org/0000-0002-4549-3502"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zheng Zhang","raw_affiliation_strings":["Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China"],"raw_orcid":"https://orcid.org/0000-0002-4549-3502","affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing 100094, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100701053"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2784},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2784},"fwci":1.4189,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.82567888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"14","issue":"19","first_page":"5027","last_page":"5027"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9955999851226807,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9955999851226807,"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"}},{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9908000230789185,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9868999719619751,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7281297445297241},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.6464033126831055},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5288478136062622},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4924810826778412},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49086886644363403},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.46646013855934143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41116029024124146},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.38938260078430176},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36674100160598755},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.3648262023925781},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3576205372810364},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23500072956085205},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.19453886151313782},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.10711970925331116}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7281297445297241},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6464033126831055},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5288478136062622},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4924810826778412},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49086886644363403},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.46646013855934143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41116029024124146},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.38938260078430176},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36674100160598755},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.3648262023925781},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3576205372810364},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23500072956085205},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.19453886151313782},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.10711970925331116},{"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/rs14195027","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14195027","pdf_url":"https://www.mdpi.com/2072-4292/14/19/5027/pdf?version=1665401623","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:03992f77e6c44d7e839d03d7a436a179","is_oa":true,"landing_page_url":"https://doaj.org/article/03992f77e6c44d7e839d03d7a436a179","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 14, Iss 19, p 5027 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/14/19/5027/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs14195027","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 14; Issue 19; Pages: 5027","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs14195027","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs14195027","pdf_url":"https://www.mdpi.com/2072-4292/14/19/5027/pdf?version=1665401623","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/15","score":0.6499999761581421,"display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G3074294386","display_name":null,"funder_award_id":"2021YFB3900503","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G4167312699","display_name":null,"funder_award_id":"2020KTYWLZX03","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5074685245","display_name":null,"funder_award_id":"2021KTYWLZX07","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6116934701","display_name":null,"funder_award_id":"2022127","funder_id":"https://openalex.org/F4320322847","funder_display_name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences"},{"id":"https://openalex.org/G750555687","display_name":null,"funder_award_id":"2022127","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320321133","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35"},{"id":"https://openalex.org/F4320322847","display_name":"Youth Innovation Promotion Association of the Chinese Academy of Sciences","ror":"https://ror.org/031141b54"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null},{"id":"https://openalex.org/F4320335892","display_name":"Youth Innovation Promotion Association","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4303980603.pdf","grobid_xml":"https://content.openalex.org/works/W4303980603.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W1665214252","https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1996698879","https://openalex.org/W2001510610","https://openalex.org/W2020299137","https://openalex.org/W2029610469","https://openalex.org/W2047354018","https://openalex.org/W2081939672","https://openalex.org/W2102200338","https://openalex.org/W2118246710","https://openalex.org/W2125027820","https://openalex.org/W2140435402","https://openalex.org/W2153409933","https://openalex.org/W2163605009","https://openalex.org/W2248723555","https://openalex.org/W2412782625","https://openalex.org/W2558748708","https://openalex.org/W2560023338","https://openalex.org/W2751808960","https://openalex.org/W2810004461","https://openalex.org/W2890779863","https://openalex.org/W2892264623","https://openalex.org/W2911286998","https://openalex.org/W2943287822","https://openalex.org/W2963091558","https://openalex.org/W2963319519","https://openalex.org/W2964236544","https://openalex.org/W2964309882","https://openalex.org/W2991494819","https://openalex.org/W3024007459","https://openalex.org/W3028306149","https://openalex.org/W3047443805","https://openalex.org/W3087257704","https://openalex.org/W3103695279","https://openalex.org/W3107591966","https://openalex.org/W3109196706","https://openalex.org/W3117977576","https://openalex.org/W3118358609","https://openalex.org/W3124992511","https://openalex.org/W3170552979","https://openalex.org/W4236215227","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W6662428914","https://openalex.org/W6680382358","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6738964360","https://openalex.org/W6743410771","https://openalex.org/W6745537798","https://openalex.org/W6754309021","https://openalex.org/W6783827371"],"related_works":["https://openalex.org/W4391621807","https://openalex.org/W3133615129","https://openalex.org/W2087854757","https://openalex.org/W2188959887","https://openalex.org/W2373152553","https://openalex.org/W2972049582","https://openalex.org/W4389201442","https://openalex.org/W2386169820","https://openalex.org/W604251607","https://openalex.org/W3170595163"],"abstract_inverted_index":{"Land":[0],"use":[1,44],"segmentation":[2,45],"is":[3,279,295],"a":[4,78],"fundamental":[5],"yet":[6],"challenging":[7],"task":[8],"in":[9,263],"remote":[10],"sensing.":[11],"Most":[12],"current":[13],"methods":[14],"mainly":[15],"take":[16],"images":[17,151],"as":[18],"input":[19],"and":[20,38,68,121,123,137,152,171,222,241,246,297],"sometimes":[21],"cannot":[22],"achieve":[23,205],"satisfactory":[24,299],"results":[25],"due":[26],"to":[27,95,132,160,174,181,211,251,288],"limited":[28],"information.":[29],"Inspired":[30],"by":[31,77,110,144,164,243],"the":[32,62,125,176,190,206,217,224,232,264,268,273],"inherent":[33],"relations":[34,53,73,103,143,163],"between":[35,65],"land":[36,39,43,48,55,66,69,153,179,182,191,209,212,228,292],"cover":[37,49,56,67,154,180,192,210,229,293],"use,":[40],"we":[41,118,157,168],"investigate":[42],"using":[46],"additional":[47],"data.":[50,155,230],"The":[51,201],"topological":[52,98,142,162],"among":[54,104],"objects":[57,105],"are":[58,74,87,148],"beneficial":[59],"for":[60,198],"bridging":[61],"semantic":[63],"gap":[64],"use.":[70,183,213],"Specifically,":[71],"these":[72],"usually":[75],"depicted":[76],"geo-object-based":[79],"graph":[80,111,165,199],"structure.":[81],"Deep":[82],"convolutional":[83,112],"neural":[84],"networks":[85,113],"(CNNs)":[86],"capable":[88],"of":[89,220,227,260,276],"extracting":[90],"local":[91,134],"patterns":[92],"but":[93],"fail":[94],"efficiently":[96],"explore":[97,272],"relations.":[99],"In":[100,115,249],"contrast,":[101],"contextual":[102],"can":[106,204,284],"be":[107,285],"easily":[108,286],"captured":[109],"(GCNs).":[114],"this":[116],"study,":[117],"integrated":[119],"CNNs":[120,240],"GCNs":[122,159,242],"proposed":[124,202],"CNN-enhanced":[126],"HEterogeneous":[127],"Graph":[128],"Convolutional":[129],"Network":[130],"(CHeGCN)":[131],"incorporate":[133],"spectral-spatial":[135],"features":[136,173],"long-range":[138],"dependencies.":[139],"We":[140],"represent":[141],"heterogeneous":[145,254],"graphs":[146,255],"which":[147],"constructed":[149],"with":[150,185],"Afterwards,":[156],"employed":[158],"build":[161],"reasoning.":[166,200],"Finally,":[167],"fused":[169],"CNN":[170],"GCN":[172],"accomplish":[175],"inference":[177],"from":[178,208],"Compared":[184],"other":[186],"homogeneous":[187,252],"graph-based":[188],"models,":[189],"data":[193],"provide":[194],"more":[195],"sufficient":[196],"information":[197,294],"method":[203],"transformation":[207],"Extensive":[214],"experiments":[215],"showed":[216],"competitive":[218],"performance":[219],"CHeGCN":[221,238,283],"demonstrated":[223],"positive":[225],"effects":[226],"On":[231],"IoU":[233,258],"metric":[234],"over":[235],"two":[236],"datasets,":[237],"outperforms":[239],"nearly":[244],"3.5%":[245],"5%,":[247],"respectively.":[248],"contrast":[250],"graphs,":[253],"have":[256],"an":[257],"improvement":[259],"approximately":[261],"2.5%":[262],"ablation":[265],"experiments.":[266],"Furthermore,":[267],"generated":[269],"visualizations":[270],"help":[271],"underlying":[274],"mechanism":[275],"CHeGCN.":[277],"It":[278],"worth":[280],"noting":[281],"that":[282],"degenerated":[287],"scenarios":[289],"where":[290],"no":[291],"available":[296],"achieves":[298],"performance.":[300]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":5}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2022-10-10T00:00:00"}