{"id":"https://openalex.org/W4404611762","doi":"https://doi.org/10.1145/3678717.3691246","title":"SRL: Towards a General-Purpose Framework for Spatial Representation Learning","display_name":"SRL: Towards a General-Purpose Framework for Spatial Representation Learning","publication_year":2024,"publication_date":"2024-10-29","ids":{"openalex":"https://openalex.org/W4404611762","doi":"https://doi.org/10.1145/3678717.3691246"},"language":"en","primary_location":{"id":"doi:10.1145/3678717.3691246","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3678717.3691246","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3678717.3691246","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036007312","display_name":"Gengchen Mai","orcid":"https://orcid.org/0000-0002-7818-7309"},"institutions":[{"id":"https://openalex.org/I86519309","display_name":"The University of Texas at Austin","ror":"https://ror.org/00hj54h04","country_code":"US","type":"education","lineage":["https://openalex.org/I86519309"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gengchen Mai","raw_affiliation_strings":["University of Texas at Austin"],"raw_orcid":"https://orcid.org/0000-0002-7818-7309","affiliations":[{"raw_affiliation_string":"University of Texas at Austin","institution_ids":["https://openalex.org/I86519309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004439650","display_name":"Xiaobai Yao","orcid":"https://orcid.org/0000-0003-2719-2017"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaobai Yao","raw_affiliation_strings":["University of Georgia"],"raw_orcid":"https://orcid.org/0000-0003-2719-2017","affiliations":[{"raw_affiliation_string":"University of Georgia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049041437","display_name":"Yiqun Xie","orcid":"https://orcid.org/0000-0002-6439-1333"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiqun Xie","raw_affiliation_strings":["University of Maryland, College Park"],"raw_orcid":"https://orcid.org/0000-0002-6439-1333","affiliations":[{"raw_affiliation_string":"University of Maryland, College Park","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023215843","display_name":"Jinmeng Rao","orcid":"https://orcid.org/0000-0003-2370-5129"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]},{"id":"https://openalex.org/I4210090411","display_name":"Google DeepMind (United Kingdom)","ror":"https://ror.org/00971b260","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210090411","https://openalex.org/I4210128969"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Jinmeng Rao","raw_affiliation_strings":["Google DeepMind"],"raw_orcid":"https://orcid.org/0000-0003-2370-5129","affiliations":[{"raw_affiliation_string":"Google DeepMind","institution_ids":["https://openalex.org/I1291425158","https://openalex.org/I4210090411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100348619","display_name":"Hao Li","orcid":"https://orcid.org/0000-0002-6336-8772"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hao Li","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":"https://orcid.org/0000-0002-6336-8772","affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084208769","display_name":"Qing Zhu","orcid":"https://orcid.org/0000-0003-2441-944X"},"institutions":[{"id":"https://openalex.org/I148283060","display_name":"Lawrence Berkeley National Laboratory","ror":"https://ror.org/02jbv0t02","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I148283060","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qing Zhu","raw_affiliation_strings":["Lawrence Berkeley, National Lab"],"raw_orcid":"https://orcid.org/0000-0003-2441-944X","affiliations":[{"raw_affiliation_string":"Lawrence Berkeley, National Lab","institution_ids":["https://openalex.org/I148283060"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104473714","display_name":"Ziyuan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ziyuan Li","raw_affiliation_strings":["University of Connecticut"],"raw_orcid":"https://orcid.org/0009-0001-1835-3336","affiliations":[{"raw_affiliation_string":"University of Connecticut","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087022523","display_name":"Ni Lao","orcid":"https://orcid.org/0000-0002-4034-7784"},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ni Lao","raw_affiliation_strings":["Google"],"raw_orcid":"https://orcid.org/0000-0002-4034-7784","affiliations":[{"raw_affiliation_string":"Google","institution_ids":["https://openalex.org/I1291425158"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5036007312"],"corresponding_institution_ids":["https://openalex.org/I86519309"],"apc_list":null,"apc_paid":null,"fwci":2.1427,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.88994141,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"465","last_page":"468"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9986000061035156,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9986000061035156,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9941999912261963,"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/T10757","display_name":"Geographic Information Systems Studies","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6878306865692139},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5653507709503174},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39989107847213745},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.32052963972091675},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.06984570622444153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6878306865692139},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5653507709503174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39989107847213745},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32052963972091675},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.06984570622444153},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3678717.3691246","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3678717.3691246","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3678717.3691246","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3678717.3691246","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4099999964237213,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G8729999162","display_name":null,"funder_award_id":"2126474, 2147195, and 2425844","funder_id":"https://openalex.org/F4320323817","funder_display_name":"Universitas Brawijaya"}],"funders":[{"id":"https://openalex.org/F4320323817","display_name":"Universitas Brawijaya","ror":"https://ror.org/01wk3d929"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2082894754","https://openalex.org/W2909390430","https://openalex.org/W2944451185","https://openalex.org/W2963727135","https://openalex.org/W3100582685","https://openalex.org/W3115648857","https://openalex.org/W3194456427","https://openalex.org/W3214340375","https://openalex.org/W4206905964","https://openalex.org/W4225366678","https://openalex.org/W4307085104","https://openalex.org/W4323542977","https://openalex.org/W4385403811","https://openalex.org/W4387460511"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Representation":[0,130],"learning":[1,82,137,150],"(RL)":[2],"techniques":[3],"are":[4,56],"widely":[5],"adopted":[6],"in":[7,53,86,175],"areas":[8],"such":[9,20],"as":[10,21,51],"natural":[11],"language":[12],"processing":[13],"and":[14,23,69,162,171,179],"computer":[15],"vision,":[16],"with":[17],"prominent":[18],"examples":[19],"attention":[22],"ConvNet":[24],"architectures.":[25],"In":[26,92],"comparison,":[27],"many":[28],"GeoAI":[29],"works":[30],"still":[31],"rely":[32],"on":[33],"feature":[34,73],"engineering":[35,74],"or":[36,121],"data":[37,42],"conversion":[38],"to":[39,61,83,116,185],"represent":[40],"spatial":[41,102,109,125,140,148],"(e.g.,":[43],"points,":[44],"polylines,":[45],"polygons,":[46],"3D":[47],"building":[48],"models,":[49],"etc.)":[50],"features":[52],"formats":[54],"that":[55],"easier":[57],"for":[58,72,79,139],"neural":[59,64,114],"networks":[60],"handle.":[62],"The":[63],"network":[65],"architectures":[66],"remain":[67],"unchanged,":[68],"the":[70,87,97,144,168],"need":[71],"has":[75],"become":[76],"a":[77,133,186],"bottleneck":[78],"applying":[80],"deep":[81],"new":[84,134,187],"tasks":[85],"age":[88],"of":[89,99,147,173,189],"big":[90],"data.":[91,126],"this":[93,181],"paper,":[94],"we":[95],"advocate":[96],"idea":[98],"developing":[100],"learnable":[101],"representation":[103,136,149],"modules,":[104],"which":[105],"not":[106],"only":[107],"enable":[108,113],"reasoning":[110],"but":[111],"also":[112,166],"nets":[115],"directly":[117],"consume":[118],"(i.e.,":[119,123],"encoding)":[120],"generate":[122],"decoding)":[124],"We":[127,142,165],"propose":[128],"Spatial":[129],"Learning":[131],"(SRL),":[132],"general-purpose":[135],"framework":[138],"reasoning.":[141],"discuss":[143,167],"key":[145],"challenges":[146],"including":[151],"multi-scale":[152],"RL,":[153,155,157,159,161],"continuous":[154],"shape-centric":[156],"noise-robust":[158],"heterogeneity-aware":[160],"fairness-aware":[163],"RL.":[164],"critical":[169],"role":[170],"potential":[172],"SRL":[174],"various":[176],"geospatial":[177],"subdomains":[178],"how":[180],"technique":[182],"can":[183],"lead":[184],"generation":[188],"GeoAI.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}