{"id":"https://openalex.org/W7114890258","doi":"https://doi.org/10.1145/3748636.3764156","title":"FieldSAT: A Scalable Query Workflow for Precision Agriculture with Large Raster Datasets","display_name":"FieldSAT: A Scalable Query Workflow for Precision Agriculture with Large Raster Datasets","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W7114890258","doi":"https://doi.org/10.1145/3748636.3764156"},"language":null,"primary_location":{"id":"doi:10.1145/3748636.3764156","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3764156","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3764156","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 33rd 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://dl.acm.org/doi/pdf/10.1145/3748636.3764156","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhuocheng Shang","orcid":"https://orcid.org/0009-0006-8583-4136"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhuocheng Shang","raw_affiliation_strings":["University of California, Riverside, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0009-0006-8583-4136","affiliations":[{"raw_affiliation_string":"University of California, Riverside, Los Angeles, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ahmed Eldawy","orcid":"https://orcid.org/0000-0002-6584-1455"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed Eldawy","raw_affiliation_strings":["University of California, Riverside, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0000-0002-6584-1455","affiliations":[{"raw_affiliation_string":"University of California, Riverside, Los Angeles, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Elia Scudiero","orcid":"https://orcid.org/0000-0003-4944-721X"},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elia Scudiero","raw_affiliation_strings":["University of California, Riverside, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0000-0003-4944-721X","affiliations":[{"raw_affiliation_string":"University of California, Riverside, Los Angeles, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ramesh Dhungel","orcid":"https://orcid.org/0000-0001-6648-8326"},"institutions":[{"id":"https://openalex.org/I4210148676","display_name":"U.S. Salinity Laboratory","ror":"https://ror.org/04pp5vm71","country_code":"US","type":"facility","lineage":["https://openalex.org/I1312222531","https://openalex.org/I1336096307","https://openalex.org/I4210094889","https://openalex.org/I4210148676"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramesh Dhungel","raw_affiliation_strings":["USDA Salinity Lab, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0000-0001-6648-8326","affiliations":[{"raw_affiliation_string":"USDA Salinity Lab, Los Angeles, USA","institution_ids":["https://openalex.org/I4210148676"]}]},{"author_position":"last","author":{"id":null,"display_name":"Ray Anderson","orcid":"https://orcid.org/0000-0002-6202-5890"},"institutions":[{"id":"https://openalex.org/I4210148676","display_name":"U.S. Salinity Laboratory","ror":"https://ror.org/04pp5vm71","country_code":"US","type":"facility","lineage":["https://openalex.org/I1312222531","https://openalex.org/I1336096307","https://openalex.org/I4210094889","https://openalex.org/I4210148676"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ray Anderson","raw_affiliation_strings":["USDA Salinity Lab, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0000-0002-6202-5890","affiliations":[{"raw_affiliation_string":"USDA Salinity Lab, Los Angeles, USA","institution_ids":["https://openalex.org/I4210148676"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61147187,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"905","last_page":"915"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.2529999911785126,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.2529999911785126,"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.13729999959468842,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.07490000128746033,"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/workflow","display_name":"Workflow","score":0.6266999840736389},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5902000069618225},{"id":"https://openalex.org/keywords/raster-graphics","display_name":"Raster graphics","score":0.5662999749183655},{"id":"https://openalex.org/keywords/query-optimization","display_name":"Query optimization","score":0.46619999408721924},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.45170000195503235},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.43720000982284546},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.40220001339912415},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3971000015735626},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.38940000534057617},{"id":"https://openalex.org/keywords/abstraction","display_name":"Abstraction","score":0.38609999418258667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8295000195503235},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6266999840736389},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5902000069618225},{"id":"https://openalex.org/C181844469","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster graphics","level":2,"score":0.5662999749183655},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5379999876022339},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5275999903678894},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.46619999408721924},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.43720000982284546},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.40220001339912415},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3971000015735626},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C140824633","wikidata":"https://www.wikidata.org/wiki/Q2808660","display_name":"Workflow management system","level":3,"score":0.36160001158714294},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3612000048160553},{"id":"https://openalex.org/C192939062","wikidata":"https://www.wikidata.org/wiki/Q104840822","display_name":"Sargable","level":4,"score":0.33739998936653137},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3296999931335449},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C2776397876","wikidata":"https://www.wikidata.org/wiki/Q1450531","display_name":"Cyberinfrastructure","level":2,"score":0.3125},{"id":"https://openalex.org/C172722865","wikidata":"https://www.wikidata.org/wiki/Q2302053","display_name":"Spatial query","level":5,"score":0.30730000138282776},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.2989000082015991},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2797999978065491},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C2692088","wikidata":"https://www.wikidata.org/wiki/Q182270","display_name":"Raster data","level":3,"score":0.27250000834465027},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C188220564","wikidata":"https://www.wikidata.org/wiki/Q3325097","display_name":"Workflow engine","level":3,"score":0.2540999948978424},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748636.3764156","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3764156","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3764156","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 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3748636.3764156","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748636.3764156","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3748636.3764156","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 33rd ACM International Conference on Advances in Geographic Information Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.7381986975669861,"display_name":"Zero hunger"}],"awards":[{"id":"https://openalex.org/G8822760016","display_name":null,"funder_award_id":"2020-69012-31914","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G96987679","display_name":"CAREER: Towards Exploratory Data Science on Spatio-temporal Big Data","funder_award_id":"2046236","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"},{"id":"https://openalex.org/F4320306114","display_name":"U.S. Department of Agriculture","ror":"https://ror.org/01na82s61"},{"id":"https://openalex.org/F4320332299","display_name":"National Institute of Food and Agriculture","ror":"https://ror.org/05qx3fv49"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7114890258.pdf","grobid_xml":"https://content.openalex.org/works/W7114890258.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W2003625635","https://openalex.org/W2030932406","https://openalex.org/W2111752896","https://openalex.org/W2159825340","https://openalex.org/W2204857797","https://openalex.org/W2295338537","https://openalex.org/W2413075059","https://openalex.org/W2507421417","https://openalex.org/W2725897987","https://openalex.org/W3057621515","https://openalex.org/W3138242734","https://openalex.org/W3215993294","https://openalex.org/W4200416251","https://openalex.org/W4221084764","https://openalex.org/W4225556465","https://openalex.org/W4317904729","https://openalex.org/W4382653202","https://openalex.org/W4409219184","https://openalex.org/W6894143524","https://openalex.org/W6929611314","https://openalex.org/W6950175931"],"related_works":[],"abstract_inverted_index":{"Remote":[0],"sensing":[1],"data":[2,16,40,63,81,127,182],"has":[3],"become":[4],"increasingly":[5],"important":[6],"for":[7,122,176],"domain":[8,206],"scientists":[9,207],"from":[10,74,95,125],"diverse":[11],"areas":[12],"including":[13],"agriculture.":[14],"Traditional":[15],"analytics":[17],"methods":[18,175],"cannot":[19],"scale":[20],"to":[21,33,51,58,153,184,198],"big-data":[22],"as":[23,66],"they":[24],"normally":[25],"run":[26],"on":[27,41,110,128],"a":[28,87,100,116],"single":[29],"machine.":[30],"This":[31,83,107],"led":[32],"new":[34],"systems":[35,53],"that":[36,148],"can":[37,149,194],"process":[38],"satellite":[39],"distributed":[42,52,192],"environments.":[43],"However,":[44],"directly":[45],"translating":[46],"traditional":[47],"query":[48,93],"processing":[49,103],"pipelines":[50,94,203],"is":[54,115],"still":[55],"challenging":[56],"due":[57],"the":[59,96,111,138],"complexity":[60],"of":[61,91,137],"raster":[62,102,126,173],"processing,":[64],"such":[65],"varying":[67],"spatial":[68],"and":[69,77,140,145,166,180],"temporal":[70],"resolutions,":[71],"computational":[72,210],"overhead":[73],"iterative":[75],"operations,":[76],"dealing":[78],"with":[79],"excessive":[80],"volumes.":[82],"paper":[84,132],"introduces":[85,141],"FieldSAT,":[86],"real-life":[88],"case":[89],"study":[90],"scaling":[92],"agriculture":[97],"field":[98],"using":[99],"Spark-based":[101],"system":[104],"named":[105],"RDPro.":[106],"work":[108],"focuses":[109],"BAITSSS":[112],"model,":[113],"which":[114],"complex,":[117],"yet":[118],"very":[119],"important,":[120],"model":[121,139],"estimating":[123],"evapotranspiration":[124],"large":[129],"scale.":[130],"The":[131,156],"goes":[133],"beyond":[134],"straightforward":[135],"implementation":[136],"key":[142],"optimization":[143],"techniques":[144],"lessons":[146],"learned":[147],"be":[150,195],"broadly":[151],"applied":[152],"similar":[154],"applications.":[155],"optimizations":[157],"introduced":[158],"in":[159],"FieldSAT":[160],"include:":[161],"managing":[162],"prolonged":[163],"Spark":[164],"DAGs":[165],"dependency":[167],"chains,":[168],"minimizing":[169],"shuffle":[170],"volumes,":[171],"implementing":[172],"interpolation":[174],"extreme":[177],"resolution":[178],"differences,":[179],"selective":[181],"persistence":[183],"eliminate":[185],"redundant":[186],"processing.":[187],"Our":[188],"approach":[189],"demonstrates":[190],"how":[191],"frameworks":[193],"effectively":[196],"adapted":[197],"handle":[199],"complex":[200],"scientific":[201],"analysis":[202],"used":[204],"by":[205],"while":[208],"maintaining":[209],"efficiency.":[211]},"counts_by_year":[],"updated_date":"2026-06-22T08:00:12.763002","created_date":"2025-12-12T00:00:00"}