{"id":"https://openalex.org/W7162444137","doi":"https://doi.org/10.48550/arxiv.2605.24273","title":"Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing","display_name":"Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing","publication_year":2026,"publication_date":"2026-05-22","ids":{"openalex":"https://openalex.org/W7162444137","doi":"https://doi.org/10.48550/arxiv.2605.24273"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24273","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.24273","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136632808","display_name":"Manuel Perez-Carrasco","orcid":"https://orcid.org/0000-0003-4644-8698"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P\u00e9rez-Carrasco, Manuel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135371838","display_name":"Maya Nasr","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nasr, Maya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137011844","display_name":"Zhan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086974809","display_name":"Apisada Chulakadabba","orcid":"https://orcid.org/0000-0001-8180-4200"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chulakadabba, Apisada","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033644764","display_name":"Javier Roger","orcid":"https://orcid.org/0000-0003-4851-4185"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roger, Javier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116620251","display_name":"Raia Ottenheimer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ottenheimer, Raia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137073300","display_name":"S\u00e9bastien Roche","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roche, S\u00e9bastien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137017064","display_name":"Maryann Sargent","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sargent, Maryann","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136998315","display_name":"Chris Chan Miller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Miller, Chris Chan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137006508","display_name":"Daniel Varon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Varon, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135396237","display_name":"Jack Warren","orcid":"https://orcid.org/0000-0003-2961-1158"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Warren, Jack","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137081578","display_name":"Luis Guanter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guanter, Luis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137078001","display_name":"Kang Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Kang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137082581","display_name":"Jonathan Franklin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Franklin, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137018081","display_name":"Jia Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020090078","display_name":"Cecilia Garraffo","orcid":"https://orcid.org/0000-0002-8791-6286"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garraffo, Cecilia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137004409","display_name":"Xiong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137058814","display_name":"Ritesh Gautam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gautam, Ritesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137040920","display_name":"Steven Wofsy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wofsy, Steven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":19,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.6046000123023987,"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":0.6046000123023987,"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/T11111","display_name":"Spectroscopy and Laser Applications","score":0.06889999657869339,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.0640999972820282,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/segmentation","display_name":"Segmentation","score":0.5331000089645386},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5281000137329102},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4936999976634979},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.45570001006126404},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44119998812675476},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.42910000681877136},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4235999882221222},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.37959998846054077},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.3343999981880188}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6446999907493591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5734000205993652},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5331000089645386},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5281000137329102},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.45570001006126404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4235999882221222},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.39959999918937683},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34380000829696655},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.32659998536109924},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.2777000069618225},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27250000834465027},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24273","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.24273","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24273","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Automated":[0],"detection":[1,27,243],"and":[2,17,48,57,82,92,109,131,163,174,198,247],"masking":[3],"of":[4,35,44,80,90,129,135,172,176,196,200,204,221],"individual":[5],"methane":[6,226],"plumes":[7],"from":[8,28,117],"satellite":[9],"imagery":[10],"is":[11,121,155,180],"important":[12],"for":[13,25,51,166,190],"operational":[14],"emission":[15,168],"attribution":[16],"quantification.":[18],"We":[19,37,60],"present":[20],"a":[21,67,132,156,181,187,218],"machine":[22],"learning":[23],"framework":[24],"plume":[26],"MethaneSAT":[29,46,83,97],"retrieved":[30],"column-averaged":[31],"dry-air":[32],"mole":[33],"fractions":[34],"methane.":[36],"address":[38,96],"two":[39,149],"core":[40],"challenges:":[41],"the":[42,49,122,138],"scarcity":[43],"labeled":[45],"data":[47,98,246],"need":[50],"inference":[52],"reliability":[53],"across":[54],"diverse":[55],"atmospheric":[56],"surface":[58],"conditions.":[59],"first":[61,154],"demonstrate":[62],"that":[63,159,184,217,234],"Mask":[64,112],"R-CNN":[65,113],"with":[66,85,114],"ResNet-50":[68,115],"backbone":[69],"outperforms":[70],"U-Net":[71],"semantic":[72],"segmentation":[73],"on":[74,241],"both":[75],"MethaneAIR":[76,107,118],"(an":[77],"airborne":[78],"version":[79],"MethaneSAT)":[81],"data,":[84],"pixel-level":[86],"F1":[87],"score":[88],"gains":[89],"10.49":[91],"5.48":[93],"respectively.":[94],"To":[95],"scarcity,":[99],"we":[100],"evaluate":[101],"three":[102],"cross-sensor":[103],"transfer":[104],"strategies":[105],"leveraging":[106],"flights":[108],"synthetic":[110],"plumes.":[111],"fine-tuned":[116],"pre-trained":[119],"weights":[120],"most":[123],"effective":[124],"strategy,":[125],"achieving":[126,170,194],"instance-level":[127],"precision":[128,171,195,235],"0.60":[130],"near-perfect":[133],"recall":[134,175,199],"0.98":[136],"at":[137],"baseline":[139],"operating":[140],"point.":[141],"A":[142],"physics-informed":[143],"post-processing":[144],"pipeline":[145],"converts":[146],"detections":[147,205],"into":[148],"operationally":[150],"distinct":[151],"modes.":[152],"The":[153,178],"high-sensitivity":[157],"mode":[158,183],"applies":[160,186],"morphological":[161],"filtering":[162],"proximity-based":[164],"merging":[165],"comprehensive":[167],"screening,":[169],"0.71":[173],"0.94.":[177],"second":[179],"high-precision":[182],"additionally":[185],"distribution-based":[188],"classifier":[189],"confident":[191],"source":[192],"attribution,":[193],"0.92":[197],"0.70.":[201],"Manual":[202],"review":[203],"classified":[206],"as":[207],"false":[208],"positives":[209],"against":[210],"our":[211],"wavelet-based":[212],"ground":[213],"truth":[214],"labels":[215],"reveals":[216],"meaningful":[219],"fraction":[220],"cases":[222],"correspond":[223],"to":[224],"real":[225],"enhancements":[227],"excluded":[228],"by":[229],"conservative":[230],"labeling":[231],"criteria,":[232],"indicating":[233],"values":[236],"reported":[237],"are":[238,249],"lower":[239],"bounds":[240],"true":[242],"performance...":[244],"Our":[245],"code":[248],"available":[250],"at:":[251],"https://doi.org/10.7910/DVN/FR959H":[252]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-27T00:00:00"}