{"id":"https://openalex.org/W4391095897","doi":"https://doi.org/10.1109/bigdata59044.2023.10386482","title":"Detection of methane plumes using Sentinel-2 satellite images and deep neural networks trained on synthetically created label data","display_name":"Detection of methane plumes using Sentinel-2 satellite images and deep neural networks trained on synthetically created label data","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391095897","doi":"https://doi.org/10.1109/bigdata59044.2023.10386482"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"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/A5061659921","display_name":"Maciel Zortea","orcid":"https://orcid.org/0000-0002-9758-5273"},"institutions":[{"id":"https://openalex.org/I4210113516","display_name":"IBM Research - Brazil","ror":"https://ror.org/01fxqdx25","country_code":"BR","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210113516","https://openalex.org/I4210114115"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Maciel Zortea","raw_affiliation_strings":["IBM Research – Brazil,Rio de Janeiro,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research – Brazil,Rio de Janeiro,Brazil","institution_ids":["https://openalex.org/I4210113516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072402264","display_name":"Jo\u00e3o Lucas de Sousa Almeida","orcid":"https://orcid.org/0000-0001-5872-064X"},"institutions":[{"id":"https://openalex.org/I4210113516","display_name":"IBM Research - Brazil","ror":"https://ror.org/01fxqdx25","country_code":"BR","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210113516","https://openalex.org/I4210114115"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jo\u00e3o Lucas De Sousa Almeida","raw_affiliation_strings":["IBM Research – Brazil,São Paulo,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research – Brazil,São Paulo,Brazil","institution_ids":["https://openalex.org/I4210113516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023556527","display_name":"Levente J. Klein","orcid":"https://orcid.org/0000-0001-9497-1403"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Levente Klein","raw_affiliation_strings":["IBM T.J. Watson Research Center,Yorktown Heights,United States","IBM T.J. Watson Research Center, Yorktown Heights, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM T.J. Watson Research Center,Yorktown Heights,United States","institution_ids":["https://openalex.org/I4210114115"]},{"raw_affiliation_string":"IBM T.J. Watson Research Center, Yorktown Heights, United States","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042896039","display_name":"Alberto Costa Nogueira","orcid":"https://orcid.org/0000-0001-6729-7206"},"institutions":[{"id":"https://openalex.org/I4210113516","display_name":"IBM Research - Brazil","ror":"https://ror.org/01fxqdx25","country_code":"BR","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210113516","https://openalex.org/I4210114115"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alberto Costa Nogueira Junior","raw_affiliation_strings":["IBM Research – Brazil,São Paulo,Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Research – Brazil,São Paulo,Brazil","institution_ids":["https://openalex.org/I4210113516"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.6008,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.98785047,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3830","last_page":"3839"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":1.0,"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/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":1.0,"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/T10995","display_name":"Methane Hydrates and Related Phenomena","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2304","display_name":"Environmental Chemistry"},"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/T10399","display_name":"Hydrocarbon exploration and reservoir analysis","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/methane","display_name":"Methane","score":0.7151582837104797},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.6123666763305664},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5160112380981445},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5154320001602173},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5072459578514099},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.4495035409927368},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4468477964401245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43342751264572144},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.41450533270835876},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34419387578964233},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2987983822822571}],"concepts":[{"id":"https://openalex.org/C516920438","wikidata":"https://www.wikidata.org/wiki/Q37129","display_name":"Methane","level":2,"score":0.7151582837104797},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6123666763305664},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5160112380981445},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5154320001602173},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5072459578514099},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.4495035409927368},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4468477964401245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43342751264572144},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.41450533270835876},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34419387578964233},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2987983822822571},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386482","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386482","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1485504984","https://openalex.org/W2101678239","https://openalex.org/W2153119498","https://openalex.org/W2194775991","https://openalex.org/W2564065614","https://openalex.org/W2982239427","https://openalex.org/W3113176484","https://openalex.org/W3193705985","https://openalex.org/W4210647337","https://openalex.org/W4284887753","https://openalex.org/W4313897960","https://openalex.org/W4318147143","https://openalex.org/W4385541303","https://openalex.org/W4385859305","https://openalex.org/W4386861335"],"related_works":["https://openalex.org/W2320243538","https://openalex.org/W4385063203","https://openalex.org/W2094288906","https://openalex.org/W1965175117","https://openalex.org/W4378716885","https://openalex.org/W2329184717","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983"],"abstract_inverted_index":{"Methane":[0],"emissions":[1],"from":[2,137,180],"oil":[3,231],"and":[4,8,111,199,235],"gas":[5,14],"infrastructure,":[6],"wetlands,":[7],"livestock":[9],"contribute":[10],"to":[11,29,53,63,117,123,160,182],"the":[12,91,96,103,138,155,165,169,173,186,195,212,228,236,240],"greenhouse":[13],"inventory.":[15],"The":[16],"analysis":[17],"of":[18,76,90,168,188,214,246,277],"satellite":[19,174],"short-wave":[20],"infrared":[21],"imagery":[22],"offers":[23],"opportunities":[24],"for":[25,47,82,119,266,280],"screening":[26],"large":[27,44,74,219],"areas":[28],"detect":[30],"methane":[31,65,79,124,135,144,170,190,220,260],"leaks.":[32],"Deep":[33],"learning":[34],"algorithms":[35],"excel":[36],"at":[37,129,222],"analyzing":[38],"these":[39],"data,":[40],"however,":[41],"they":[42],"require":[43],"annotated":[45],"datasets":[46],"model":[48,109,116,157],"calibration":[49],"that":[50,252,262],"are":[51],"difficult":[52],"get.":[54],"To":[55,94,132],"overcome":[56],"this":[57,83],"limitation,":[58],"we":[59,98,141,210],"explore":[60],"a":[61,73,113,143,149,177,189,201,275,278],"methodology":[62],"spot":[64],"plumes":[66,136,261],"using":[67,86,102,206],"deep":[68],"binary":[69],"classifiers":[70],"trained":[71],"on":[72,148],"dataset":[75],"synthetically":[77],"created":[78],"plumes,":[80],"customized":[81],"specific":[84],"task,":[85],"publicly":[87],"available":[88],"images":[89],"Sentine1-2":[92],"satellites.":[93],"build":[95],"database,":[97],"simulate":[99],"plume":[100],"patterns":[101],"Hybrid":[104],"Single-Particle":[105],"Lagrangian":[106],"Integrated":[107],"Trajectory":[108],"(HYSPLIT)":[110],"use":[112],"simple":[114],"stochastic":[115],"account":[118],"reflectance":[120],"attenuation":[121],"due":[122],"in":[125,164,217,227,233,239,256],"band":[126],"12":[127],"centered":[128,163],"2190":[130],"nm.":[131],"help":[133],"distinguish":[134],"image":[139,146,161],"background,":[140],"compute":[142],"signature":[145,171],"based":[147],"background":[150],"subtraction":[151],"technique.":[152],"Once":[153],"calibrated,":[154],"classification":[156],"is":[158,254],"applied":[159],"patches":[162],"local":[166],"minima":[167],"within":[172],"image,":[175],"scoring":[176],"value":[178],"ranging":[179],"0":[181],"1":[183],"associated":[184],"with":[185],"presence":[187],"plume.":[191],"We":[192,250],"compare":[193],"experimentally":[194],"general-purpose":[196],"ResNet":[197,253],"architecture":[198],"MethaNet,":[200],"domain-specific":[202],"convolutional":[203],"neural":[204],"network,":[205],"simulated":[207],"data.":[208],"Then,":[209],"evaluate":[211],"feasibility":[213],"our":[215],"approach":[216],"detecting":[218],"leaks":[221],"two":[223],"study":[224],"sites":[225],"located":[226],"Hassi":[229],"Messaoud":[230],"field":[232],"Algeria":[234],"Permian":[237],"Basin":[238],"US,":[241],"each":[242],"covering":[243],"an":[244],"area":[245],"0.25$\\times$":[247],"0.25":[248],"degrees.":[249],"found":[251],"effective":[255],"identifying":[257],"large,":[258],"known":[259],"were":[263],"set":[264],"aside":[265],"testing":[267],"purposes.":[268],"This":[269],"method":[270],"could":[271],"be":[272],"considered":[273],"as":[274],"component":[276],"solution":[279],"planning":[281],"mitigation":[282],"activities.":[283]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}