{"id":"https://openalex.org/W3094669684","doi":"https://doi.org/10.1145/3423422","title":"Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data","display_name":"Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data","publication_year":2020,"publication_date":"2020-10-27","ids":{"openalex":"https://openalex.org/W3094669684","doi":"https://doi.org/10.1145/3423422","mag":"3094669684"},"language":"en","primary_location":{"id":"doi:10.1145/3423422","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3423422","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3423422","source":{"id":"https://openalex.org/S2503711797","display_name":"ACM Transactions on Spatial Algorithms and Systems","issn_l":"2374-0353","issn":["2374-0353","2374-0361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Spatial Algorithms and Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3423422","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100353446","display_name":"Xiaowei Zhang","orcid":"https://orcid.org/0000-0002-8373-3570"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaowei Zhang","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061844901","display_name":"Aly Shehata","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aly Shehata","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061742294","display_name":"Bed\u0159ich Bene\u0161","orcid":"https://orcid.org/0000-0002-5293-2112"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bedrich Benes","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":"https://orcid.org/0000-0002-5293-2112","affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090414723","display_name":"Daniel G. Aliaga","orcid":"https://orcid.org/0000-0001-9794-462X"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Aliaga","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100353446"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":0.4113,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.65709878,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"7","issue":"2","first_page":"1","last_page":"28"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9993000030517578,"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":0.9993000030517578,"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/T11963","display_name":"Impact of Light on Environment and Health","score":0.9937999844551086,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/computer-science","display_name":"Computer science","score":0.6098493337631226},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5980914235115051},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.5732013583183289},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.499927282333374},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4861733317375183},{"id":"https://openalex.org/keywords/procedural-modeling","display_name":"Procedural modeling","score":0.48370200395584106},{"id":"https://openalex.org/keywords/elevation","display_name":"Elevation (ballistics)","score":0.4827827513217926},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.45238471031188965},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.451874703168869},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4308927655220032},{"id":"https://openalex.org/keywords/3d-city-models","display_name":"3D city models","score":0.41425657272338867},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3884413242340088},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38813620805740356},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.34134960174560547},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.30899012088775635},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.28030192852020264},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.14625456929206848},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13678699731826782}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6098493337631226},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5980914235115051},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.5732013583183289},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.499927282333374},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4861733317375183},{"id":"https://openalex.org/C113230428","wikidata":"https://www.wikidata.org/wiki/Q7247149","display_name":"Procedural modeling","level":2,"score":0.48370200395584106},{"id":"https://openalex.org/C37054046","wikidata":"https://www.wikidata.org/wiki/Q641888","display_name":"Elevation (ballistics)","level":2,"score":0.4827827513217926},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.45238471031188965},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.451874703168869},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4308927655220032},{"id":"https://openalex.org/C2778597888","wikidata":"https://www.wikidata.org/wiki/Q172169","display_name":"3D city models","level":3,"score":0.41425657272338867},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3884413242340088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38813620805740356},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.34134960174560547},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.30899012088775635},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.28030192852020264},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.14625456929206848},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13678699731826782},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3423422","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3423422","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3423422","source":{"id":"https://openalex.org/S2503711797","display_name":"ACM Transactions on Spatial Algorithms and Systems","issn_l":"2374-0353","issn":["2374-0353","2374-0361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Spatial Algorithms and Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3423422","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3423422","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3423422","source":{"id":"https://openalex.org/S2503711797","display_name":"ACM Transactions on Spatial Algorithms and Systems","issn_l":"2374-0353","issn":["2374-0353","2374-0361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Spatial Algorithms and Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8199999928474426,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2289194341","display_name":null,"funder_award_id":"1835739","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3840264594","display_name":null,"funder_award_id":"1816514","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3094669684.pdf","grobid_xml":"https://content.openalex.org/works/W3094669684.grobid-xml"},"referenced_works_count":57,"referenced_works":["https://openalex.org/W1549847324","https://openalex.org/W1590121217","https://openalex.org/W1611818215","https://openalex.org/W1909515874","https://openalex.org/W1955601057","https://openalex.org/W1968625395","https://openalex.org/W1989546934","https://openalex.org/W1989750313","https://openalex.org/W2000690667","https://openalex.org/W2012876265","https://openalex.org/W2030737358","https://openalex.org/W2038472047","https://openalex.org/W2041411484","https://openalex.org/W2046890933","https://openalex.org/W2053430767","https://openalex.org/W2073549223","https://openalex.org/W2103815986","https://openalex.org/W2116349204","https://openalex.org/W2117088012","https://openalex.org/W2117878059","https://openalex.org/W2133888192","https://openalex.org/W2140965591","https://openalex.org/W2154604972","https://openalex.org/W2155710590","https://openalex.org/W2155893237","https://openalex.org/W2159075734","https://openalex.org/W2160026749","https://openalex.org/W2234350292","https://openalex.org/W2460689045","https://openalex.org/W2471222198","https://openalex.org/W2474846919","https://openalex.org/W2488187315","https://openalex.org/W2494341560","https://openalex.org/W2519517723","https://openalex.org/W2562923060","https://openalex.org/W2609719703","https://openalex.org/W2737722331","https://openalex.org/W2741885505","https://openalex.org/W2742872594","https://openalex.org/W2753694176","https://openalex.org/W2769969171","https://openalex.org/W2770995554","https://openalex.org/W2791756395","https://openalex.org/W2794321690","https://openalex.org/W2794354663","https://openalex.org/W2808699988","https://openalex.org/W2891800489","https://openalex.org/W2896769147","https://openalex.org/W2912855704","https://openalex.org/W3004494160","https://openalex.org/W3105127913","https://openalex.org/W4205503054","https://openalex.org/W4235375376","https://openalex.org/W4238070559","https://openalex.org/W4246789165","https://openalex.org/W4248010398","https://openalex.org/W4252175893"],"related_works":["https://openalex.org/W292456094","https://openalex.org/W4284714707","https://openalex.org/W4368357547","https://openalex.org/W4224312031","https://openalex.org/W852120471","https://openalex.org/W2071285171","https://openalex.org/W4214813861","https://openalex.org/W4214835063","https://openalex.org/W2485427127","https://openalex.org/W2256264200"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,233,238],"big":[3],"spatial":[4,36,192],"data":[5,37],"acquisition":[6],"and":[7,39,46,58,65,72,82,108,133,149,157,170,180,196,202,227,235,247],"deep":[8],"learning":[9],"allow":[10],"novel":[11,23],"algorithms":[12],"that":[13,31,115,138],"were":[14],"not":[15],"possible":[16],"several":[17],"years":[18],"ago.":[19],"We":[20,155,188,220],"introduce":[21],"a":[22,48,86,90,93,125,130,191,203,214],"inverse":[24],"procedural":[25,87,102,173,222],"modeling":[26],"algorithm":[27],"for":[28,124,252],"urban":[29,53,76,218],"areas":[30,237],"addresses":[32],"the":[33,70,73,164,239],"problem":[34],"of":[35,51,75,89,129,143,194,205],"quality":[38],"uncertainty.":[40],"Our":[41,95],"method":[42],"is":[43],"fully":[44],"automatic":[45],"produces":[47],"3D":[49],"approximation":[50,88],"an":[52,135],"area":[54,113,148],"given":[55],"satellite":[56,127],"imagery":[57],"global-scale":[59],"data,":[60,77],"including":[61],"road":[62],"network,":[63],"population,":[64,81],"elevation":[66],"data.":[67],"By":[68],"analyzing":[69],"values":[71],"distribution":[74,204],"e.g.,":[78],"parcels,":[79],"buildings,":[80,179],"elevation,":[83],"we":[84],"construct":[85],"city":[91,131],"at":[92,224],"large-scale.":[94],"approach":[96,160],"has":[97],"three":[98],"main":[99],"components:":[100],"(1)":[101],"model":[103],"generation":[104],"to":[105,119,152,177,183,199,209,244],"create":[106],"parcel":[107,112,122,234],"building":[109,146,150,206,236],"geometries,":[110],"(2)":[111],"estimation":[114],"trains":[116],"neural":[117],"networks":[118],"provide":[120],"initial":[121],"sizes":[123,207],"segmented":[126],"image":[128],"block,":[132],"(3)":[134],"optional":[136],"optimization":[137],"can":[139],"use":[140],"partial":[141],"knowledge":[142],"overall":[144],"average":[145,251],"footprint":[147],"counts":[151],"improve":[153],"results.":[154],"demonstrate":[156],"evaluate":[158],"our":[159],"on":[161,250],"cities":[162],"around":[163],"globe":[165],"with":[166,175,228],"widely":[167],"different":[168],"structures":[169],"automatically":[171],"yield":[172],"models":[174,223],"up":[176,182],"91,000":[178],"spanning":[181],"150":[184],"km":[185],"2":[186],".":[187],"obtain":[189],"both":[190],"arrangement":[193],"parcels":[195],"buildings":[197],"similar":[198,208,216],"ground":[200,210,245],"truth":[201,246],"truth,":[211],"hence":[212],"yielding":[213],"statistically":[215],"synthetic":[217],"space.":[219],"produce":[221],"multiple":[225],"scales,":[226],"less":[229],"than":[230],"1%":[231],"error":[232,249],"best":[240],"case":[241],"as":[242],"compared":[243],"5.8%":[248],"tested":[253],"cities.":[254]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
