{"id":"https://openalex.org/W67750685","doi":"https://doi.org/10.5591/978-1-57735-516-8/ijcai11-287","title":"Modeling multivariate spatio-temporal remote sensing data with large gaps","display_name":"Modeling multivariate spatio-temporal remote sensing data with large gaps","publication_year":2011,"publication_date":"2011-07-16","ids":{"openalex":"https://openalex.org/W67750685","doi":"https://doi.org/10.5591/978-1-57735-516-8/ijcai11-287","mag":"67750685"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.352.1776","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.1776","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.dabi.temple.edu/~zoran/papers/lou11.pdf","raw_type":"text"},"type":"article","indexed_in":[],"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/A5001281583","display_name":"Qiang Lou","orcid":"https://orcid.org/0009-0006-4902-4304"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qiang Lou","raw_affiliation_strings":["Center for Information Science and Technology, Temple University, Philadelphia PA#TAB#"],"raw_orcid":"https://orcid.org/0009-0006-4902-4304","affiliations":[{"raw_affiliation_string":"Center for Information Science and Technology, Temple University, Philadelphia PA#TAB#","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044038055","display_name":"Zoran Obradovi\u0107","orcid":"https://orcid.org/0000-0002-2051-0142"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zoran Obradovi\u0107","raw_affiliation_strings":["Center for Information Science and Technology, Temple University, Philadelphia PA#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-2051-0142","affiliations":[{"raw_affiliation_string":"Center for Information Science and Technology, Temple University, Philadelphia PA#TAB#","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I84392919"],"apc_list":null,"apc_paid":null,"fwci":0.6934,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.7040729,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1711","last_page":"1716"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","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"}},"topics":[{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","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"}},{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.9879999756813049,"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/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9853000044822693,"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/missing-data","display_name":"Missing data","score":0.7870453596115112},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.6564337611198425},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6294867992401123},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5986048579216003},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4943351745605469},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.46467217803001404},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.4612305462360382},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.4460066556930542},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.43489015102386475},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42072370648384094},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3595947027206421},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3137359023094177},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2513037621974945},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1694912314414978},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15761160850524902}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7870453596115112},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.6564337611198425},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6294867992401123},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5986048579216003},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4943351745605469},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.46467217803001404},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.4612305462360382},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.4460066556930542},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.43489015102386475},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42072370648384094},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3595947027206421},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3137359023094177},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2513037621974945},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1694912314414978},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15761160850524902},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.352.1776","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.352.1776","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.dabi.temple.edu/~zoran/papers/lou11.pdf","raw_type":"text"},{"id":"mag:67750685","is_oa":false,"landing_page_url":"http://www.dabi.temple.edu/~zoran/papers/lou11.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","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":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"International Joint Conference on Artificial Intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/14","display_name":"Life below water","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1506806321","https://openalex.org/W1663973292","https://openalex.org/W1800223764","https://openalex.org/W2006761437","https://openalex.org/W2101936384","https://openalex.org/W2116659053","https://openalex.org/W2118502261","https://openalex.org/W2129479849","https://openalex.org/W2176494706"],"related_works":["https://openalex.org/W3048054637","https://openalex.org/W2978102369","https://openalex.org/W419500785","https://openalex.org/W1954464750","https://openalex.org/W2167808470","https://openalex.org/W2989605714","https://openalex.org/W2273952958","https://openalex.org/W1987940986","https://openalex.org/W2729033468","https://openalex.org/W2770900454","https://openalex.org/W2620778781","https://openalex.org/W61965043","https://openalex.org/W3111424705","https://openalex.org/W3034271926","https://openalex.org/W3105445637","https://openalex.org/W3198600528","https://openalex.org/W2763813995","https://openalex.org/W2793997912","https://openalex.org/W3134691369","https://openalex.org/W2738823087"],"abstract_inverted_index":{"Prediction":[0],"models":[1],"for":[2,84,153],"multivariate":[3],"spatio-temporal":[4,60,135],"functions":[5],"in":[6,88,122,144,161,207],"geosciences":[7],"are":[8,36,51,79],"typically":[9],"developed":[10,187],"using":[11,63],"supervised":[12],"learning":[13],"from":[14,115,163],"attributes":[15,71,102,114],"collected":[16,103],"by":[17,62,68],"remote":[18],"sensing":[19],"instruments":[20],"collocated":[21,33,65],"with":[22],"the":[23,105,166],"outcome":[24,49,77],"variable":[25],"provided":[26],"at":[27,46,72,104,132,138],"sparsely":[28],"located":[29],"sites.":[30,117],"In":[31],"such":[32,177],"data":[34,66,162,179],"there":[35],"often":[37],"large":[38,85],"temporal":[39,95],"gaps":[40,86],"due":[41],"to":[42,56,125,173],"missing":[43,70,130,159],"attribute":[44],"values":[45,160],"sites":[47],"where":[48,76],"labels":[50,78],"available.":[52,80],"Our":[53],"objective":[54],"is":[55,186],"develop":[57],"more":[58,191],"accurate":[59,192],"predictors":[61,194],"enlarged":[64,178],"obtained":[67],"imputing":[69,123,154],"time":[73,91,107],"and":[74,90,108,110,137],"locations":[75],"The":[81],"proposed":[82],"method":[83],"estimation":[87],"space":[89],"(called":[92],"LarGEST)":[93],"exploits":[94],"correlation":[96],"of":[97,128,141,157,165,182],"attributes,":[98],"correlations":[99,112],"among":[100,113],"multiple":[101,116],"same":[106],"space,":[109],"spatial":[111],"LarGEST":[118,149],"outperformed":[119],"alternative":[120,197],"methods":[121,199],"up":[124],"80":[126,155],"%":[127,156],"randomly":[129],"observations":[131],"a":[133,139,145,180],"synthetic":[134],"signal":[136],"model":[140],"fluoride":[142],"content":[143],"water":[146],"distribution":[147],"system.":[148],"was":[150,189],"also":[151],"applied":[152],"nonrandom":[158],"one":[164],"most":[167],"challenging":[168],"Earth":[169],"science":[170],"problems":[171],"related":[172],"aerosol":[174,183],"properties.":[175],"Using":[176],"predictor":[181],"optical":[184],"depth":[185],"that":[188],"much":[190],"than":[193],"based":[195],"on":[196],"imputation":[198],"when":[200],"tested":[201],"rigorously":[202],"over":[203],"entire":[204],"continental":[205],"US":[206],"year":[208],"2005.":[209],"1":[210]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}