{"id":"https://openalex.org/W4417283650","doi":"https://doi.org/10.1145/3748636.3762726","title":"Enhancing Urban Region Representation via Adaptive Risk-aware Consensus Learning","display_name":"Enhancing Urban Region Representation via Adaptive Risk-aware Consensus Learning","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W4417283650","doi":"https://doi.org/10.1145/3748636.3762726"},"language":null,"primary_location":{"id":"doi:10.1145/3748636.3762726","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3748636.3762726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101829754","display_name":"Li Huang","orcid":"https://orcid.org/0000-0003-0086-5461"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Huang","raw_affiliation_strings":["School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-0086-5461","affiliations":[{"raw_affiliation_string":"School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yujie Wu","orcid":"https://orcid.org/0009-0003-6276-5340"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yujie Wu","raw_affiliation_strings":["Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":"https://orcid.org/0009-0003-6276-5340","affiliations":[{"raw_affiliation_string":"Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100982381","display_name":"Xiaolong Song","orcid":"https://orcid.org/0009-0007-9795-5549"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolong Song","raw_affiliation_strings":["Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":"https://orcid.org/0009-0007-9795-5549","affiliations":[{"raw_affiliation_string":"Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048618271","display_name":"Qiang Gao","orcid":"https://orcid.org/0000-0002-9621-5414"},"institutions":[{"id":"https://openalex.org/I204831749","display_name":"Southwestern University of Finance and Economics","ror":"https://ror.org/04ewct822","country_code":"CN","type":"education","lineage":["https://openalex.org/I204831749"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Gao","raw_affiliation_strings":["Southwestern University of Finance and Economics, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9621-5414","affiliations":[{"raw_affiliation_string":"Southwestern University of Finance and Economics, Chengdu, China","institution_ids":["https://openalex.org/I204831749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086447943","display_name":"Goce Trajcevski","orcid":"https://orcid.org/0000-0002-8839-6278"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Goce Trajcevski","raw_affiliation_strings":["Iowa State University, Ames, USA"],"raw_orcid":"https://orcid.org/0000-0002-8839-6278","affiliations":[{"raw_affiliation_string":"Iowa State University, Ames, USA","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100649527","display_name":"Xueqin Chen","orcid":"https://orcid.org/0000-0003-1538-3713"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]},{"id":"https://openalex.org/I4210125143","display_name":"Chengdu University","ror":"https://ror.org/034z67559","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueqin Chen","raw_affiliation_strings":["College of Computer Science, Sichuan University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-1538-3713","affiliations":[{"raw_affiliation_string":"College of Computer Science, Sichuan University, Chengdu, China","institution_ids":["https://openalex.org/I24185976","https://openalex.org/I4210125143"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"233","last_page":"243"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.19699999690055847,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.19699999690055847,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.18649999797344208,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.1379999965429306,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.7577000260353088},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.525600016117096},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.504800021648407},{"id":"https://openalex.org/keywords/nestedness","display_name":"Nestedness","score":0.4609000086784363},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4377000033855438},{"id":"https://openalex.org/keywords/masking","display_name":"Masking (illustration)","score":0.3749000132083893}],"concepts":[{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.7577000260353088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6736999750137329},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.525600016117096},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.504800021648407},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48840001225471497},{"id":"https://openalex.org/C2780267512","wikidata":"https://www.wikidata.org/wiki/Q6997828","display_name":"Nestedness","level":3,"score":0.4609000086784363},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40549999475479126},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.3749000132083893},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.35190001130104065},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3018999993801117},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748636.3762726","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3748636.3762726","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1737795253","display_name":null,"funder_award_id":"62102326","funder_id":"https://openalex.org/F4320334062","funder_display_name":"National Natural Science Foundation of China-Liaoning Joint Fund"},{"id":"https://openalex.org/G8641536534","display_name":null,"funder_award_id":"2025ZNSFSC1495","funder_id":"https://openalex.org/F4320329861","funder_display_name":"Natural Science Foundation of Sichuan Province"}],"funders":[{"id":"https://openalex.org/F4320329861","display_name":"Natural Science Foundation of Sichuan Province","ror":null},{"id":"https://openalex.org/F4320334062","display_name":"National Natural Science Foundation of China-Liaoning Joint Fund","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2056716515","https://openalex.org/W2768009948","https://openalex.org/W2807954821","https://openalex.org/W2855988163","https://openalex.org/W2903883820","https://openalex.org/W2952611035","https://openalex.org/W3034277777","https://openalex.org/W3035524453","https://openalex.org/W3190469032","https://openalex.org/W3211626409","https://openalex.org/W4224911291","https://openalex.org/W4284968770","https://openalex.org/W4312310776","https://openalex.org/W4312794196","https://openalex.org/W4312973985","https://openalex.org/W4313156423","https://openalex.org/W4313706055","https://openalex.org/W4322502493","https://openalex.org/W4382239598","https://openalex.org/W4385270450","https://openalex.org/W4385562633","https://openalex.org/W4389252896","https://openalex.org/W4390591001","https://openalex.org/W4393147851","https://openalex.org/W4394862694","https://openalex.org/W4400910487","https://openalex.org/W4401863622","https://openalex.org/W4402525442"],"related_works":[],"abstract_inverted_index":{"High-quality":[0],"embeddings":[1,76,164],"for":[2,98],"urban":[3,10,99],"regions":[4],"have":[5],"enabled":[6],"influential":[7],"insights":[8],"into":[9],"structures":[11],"and":[12,41,79,107,140,155],"characteristics,":[13],"facilitating":[14],"the":[15,22,39,54,73,80,111,115,145,153,162],"creation":[16],"of":[17,75,117,147,157],"more":[18],"sustainable":[19],"cities.":[20],"However,":[21],"existing":[23],"practices":[24],"still":[25],"face":[26],"certain":[27],"challenges,":[28,87],"notably:":[29],"(1)":[30],"When":[31],"multiple":[32],"views":[33,51,63],"contain":[34],"distinct":[35],"semantic":[36],"information,":[37],"ignoring":[38],"reliability":[40],"possibly":[42],"inadequate":[43],"collection":[44],"differences":[45],"(e.g.,":[46],"data":[47],"missingness)":[48],"among":[49],"those":[50],"may":[52],"degrade":[53],"representation":[55],"robustness.":[56],"(2)":[57],"Consensus":[58,94],"semantics":[59],"extracted":[60],"from":[61],"different":[62],"are":[64],"often":[65],"fused":[66],"in":[67,133],"a":[68,90,129],"simplistic":[69],"manner,":[70],"without":[71],"considering":[72],"uniformity":[74,146],"(quality":[77],"variations)":[78],"complementarity":[81,156],"between":[82],"views.":[83],"To":[84,143],"address":[85],"such":[86],"we":[88,103,127,149,160],"propose":[89],"novel":[91],"Adaptive":[92],"Risk-aware":[93],"learning":[95,135],"(ARC)":[96],"solution":[97],"region":[100],"embeddings.":[101],"Specifically,":[102],"design":[104],"both":[105],"local-":[106],"region-level":[108],"masking":[109],"within":[110],"inter-view":[112],"representation,":[113],"following":[114],"paradigm":[116],"masked":[118],"autoencoders,":[119],"to":[120,136,165,172],"better":[121],"handle":[122],"uncertainty":[123],"risks.":[124],"More":[125],"importantly,":[126],"introduce":[128],"self-weighted":[130],"contrastive":[131],"mechanism":[132],"consensus":[134],"achieve":[137],"maximum":[138],"alignment":[139],"mitigate":[141],"degradation.":[142],"enhance":[144],"embeddings,":[148],"employ":[150],"entropy,":[151],"ensuring":[152],"diversity":[154],"information.":[158],"Ultimately,":[159],"apply":[161],"learned":[163],"down-stream":[166],"tasks,":[167],"demonstrating":[168],"remarkable":[169],"improvements":[170],"compared":[171],"several":[173],"representative":[174],"baselines.":[175]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-12-12T00:00:00"}
