{"id":"https://openalex.org/W2964420342","doi":"https://doi.org/10.24963/ijcai.2019/365","title":"Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring","display_name":"Recurrent Generative Networks for Multi-Resolution Satellite Data: An Application in Cropland Monitoring","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2964420342","doi":"https://doi.org/10.24963/ijcai.2019/365","mag":"2964420342"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/365","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/365","pdf_url":"https://www.ijcai.org/proceedings/2019/0365.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2019/0365.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001445783","display_name":"Xiaowei Jia","orcid":"https://orcid.org/0000-0001-8544-5233"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaowei Jia","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Minnesota"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Minnesota","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101399973","display_name":"Mengdie Wang","orcid":"https://orcid.org/0000-0002-9835-5509"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mengdie Wang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Minnesota"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Minnesota","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016611537","display_name":"Ankush Khandelwal","orcid":"https://orcid.org/0000-0003-1023-5279"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ankush Khandelwal","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Minnesota"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Minnesota","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081622450","display_name":"Anuj Karpatne","orcid":"https://orcid.org/0000-0003-1647-3534"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anuj Karpatne","raw_affiliation_strings":["Department of Computer Science, Virginia Tech"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Virginia Tech","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100645812","display_name":"Vipin Kumar","orcid":"https://orcid.org/0000-0002-9040-2665"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vipin Kumar","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Minnesota"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Minnesota","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5001445783"],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":1.4552,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.86227388,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2628","last_page":"2634"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9868000149726868,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9868000149726868,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.98089998960495,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/leverage","display_name":"Leverage (statistics)","score":0.746787428855896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6998581290245056},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.56612229347229},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4784657657146454},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.42695122957229614},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4126126766204834},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3792878985404968},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33110499382019043},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1067453920841217},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.0791189968585968}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.746787428855896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6998581290245056},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.56612229347229},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4784657657146454},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.42695122957229614},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4126126766204834},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3792878985404968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33110499382019043},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1067453920841217},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0791189968585968},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/365","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/365","pdf_url":"https://www.ijcai.org/proceedings/2019/0365.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/365","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/365","pdf_url":"https://www.ijcai.org/proceedings/2019/0365.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/2"}],"awards":[{"id":"https://openalex.org/G1107591779","display_name":null,"funder_award_id":"1838159","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1251807142","display_name":null,"funder_award_id":"1739191","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/W2964420342.pdf","grobid_xml":"https://content.openalex.org/works/W2964420342.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W592244745","https://openalex.org/W1894683164","https://openalex.org/W1959608418","https://openalex.org/W1998281138","https://openalex.org/W2023939759","https://openalex.org/W2035419249","https://openalex.org/W2119879130","https://openalex.org/W2147055881","https://openalex.org/W2157242556","https://openalex.org/W2342893289","https://openalex.org/W2411563882","https://openalex.org/W2431738724","https://openalex.org/W2438450043","https://openalex.org/W2529283833","https://openalex.org/W2615237590","https://openalex.org/W2622626827","https://openalex.org/W2742706476","https://openalex.org/W2782565073","https://openalex.org/W2802441648","https://openalex.org/W2914655379","https://openalex.org/W2944014586","https://openalex.org/W2944361765","https://openalex.org/W2982585804","https://openalex.org/W4297772798"],"related_works":["https://openalex.org/W2787993192","https://openalex.org/W2121524756","https://openalex.org/W782553550","https://openalex.org/W2158269427","https://openalex.org/W4381280689","https://openalex.org/W2847365777","https://openalex.org/W1987967678","https://openalex.org/W3128025644","https://openalex.org/W2139939267","https://openalex.org/W1974511032"],"abstract_inverted_index":{"Effective":[0],"and":[1,59,135],"timely":[2],"monitoring":[3],"of":[4,152],"croplands":[5,24,34,102],"is":[6,30],"critical":[7],"for":[8,32],"managing":[9],"food":[10],"supply.":[11],"While":[12],"remote":[13,49,97],"sensing":[14,50,98],"data":[15,51,75,99,118],"from":[16,116],"earth-observing":[17],"satellites":[18],"can":[19,128],"be":[20,38,70],"used":[21],"to":[22,76,83,94,100,119,138],"monitor":[23],"over":[25],"large":[26],"regions,":[27],"this":[28,87],"task":[29],"challenging":[31],"small-scale":[33],"as":[35],"they":[36],"cannot":[37,69],"captured":[39],"precisely":[40],"using":[41],"coarse-resolution":[42,117],"data.":[43,64,123],"On":[44],"the":[45,48,107,112,125,150,153],"other":[46],"hand,":[47],"in":[52,132,144,156],"higher":[53],"resolution":[54],"are":[55,81],"collected":[56],"less":[57],"frequently":[58],"contain":[60],"missing":[61,121],"or":[62],"disturbed":[63],"Hence,":[65],"traditional":[66],"sequential":[67],"models":[68],"directly":[71],"applied":[72],"on":[73],"high-resolution":[74,122],"extract":[77],"temporal":[78,113],"patterns,":[79],"which":[80],"essential":[82],"identify":[84],"crops.":[85],"In":[86],"work,":[88],"we":[89,110],"propose":[90],"a":[91],"generative":[92],"model":[93,127],"combine":[95],"multi-scale":[96],"detect":[101],"at":[103],"high":[104],"resolution.":[105],"During":[106],"learning":[108],"process,":[109],"leverage":[111],"patterns":[114],"learned":[115],"generate":[120],"Additionally,":[124],"proposed":[126,154],"track":[129],"classification":[130],"confidence":[131],"real":[133],"time":[134],"potentially":[136],"lead":[137],"an":[139,145],"early":[140],"detection.":[141,158],"The":[142],"evaluation":[143],"intensively":[146],"cultivated":[147],"region":[148],"demonstrates":[149],"effectiveness":[151],"method":[155],"cropland":[157]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}