{"id":"https://openalex.org/W7151218930","doi":"https://doi.org/10.48550/arxiv.2604.04153","title":"Uncertainty-Aware Test-Time Adaptation for Cross-Region Spatio-Temporal Fusion of Land Surface Temperature","display_name":"Uncertainty-Aware Test-Time Adaptation for Cross-Region Spatio-Temporal Fusion of Land Surface Temperature","publication_year":2026,"publication_date":"2026-04-05","ids":{"openalex":"https://openalex.org/W7151218930","doi":"https://doi.org/10.48550/arxiv.2604.04153"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04153","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.04153","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042804466","display_name":"Sofiane Bouaziz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bouaziz, Sofiane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061197809","display_name":"Adel Hafiane","orcid":"https://orcid.org/0000-0003-3185-9996"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hafiane, Adel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068195100","display_name":"Rapha\u00ebl Canals","orcid":"https://orcid.org/0000-0001-9100-7539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Canals, Raphael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133102932","display_name":"Rachid Nedjai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nedjai, Rachid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10766","display_name":"Urban Heat Island Mitigation","score":0.8665000200271606,"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/T10766","display_name":"Urban Heat Island Mitigation","score":0.8665000200271606,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.03689999878406525,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.027000000700354576,"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/land-cover","display_name":"Land cover","score":0.6632000207901001},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.6534000039100647},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.579800009727478},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5778999924659729},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5763000249862671},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5277000069618225},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.487199991941452},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46860000491142273}],"concepts":[{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6632000207901001},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.6534000039100647},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.579800009727478},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5778999924659729},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5763000249862671},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5277000069618225},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.487199991941452},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46860000491142273},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46230000257492065},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4607999920845032},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.45969998836517334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4336000084877014},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3873000144958496},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.33379998803138733},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31769999861717224},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3111000061035156},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3050999939441681},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04153","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.04153","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04153","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":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.841095507144928}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep":[0],"learning":[1],"models":[2],"have":[3],"shown":[4],"great":[5],"promise":[6],"in":[7,46,145,148,151,155,160,168],"diverse":[8,141],"remote":[9],"sensing":[10],"applications.":[11],"However,":[12],"they":[13],"often":[14],"struggle":[15],"to":[16,25,44,61,77],"generalize":[17],"across":[18],"geographic":[19],"regions":[20,139],"unseen":[21],"during":[22],"training":[23,37],"due":[24,43],"domain":[26],"shifts.":[27],"Domain":[28],"shifts":[29],"occur":[30],"when":[31],"data":[32,130,184],"distributions":[33],"differ":[34],"between":[35],"the":[36,85,106],"region":[38],"and":[39,50,72,120,124,153,162,176,185],"new":[40],"target":[41,133,138,183],"regions,":[42],"variations":[45],"land":[47,93,118,121],"cover,":[48],"climate,":[49],"environmental":[51],"conditions.":[52],"Test-time":[53],"adaptation":[54],"(TTA)":[55],"has":[56],"emerged":[57],"as":[58],"a":[59,110,165],"solution":[60],"such":[62],"shifts,":[63],"but":[64],"existing":[65],"methods":[66],"are":[67,73,174],"primarily":[68],"designed":[69],"for":[70,92,164],"classification":[71],"not":[74],"directly":[75],"applicable":[76],"regression":[78,86],"tasks.":[79],"In":[80],"this":[81],"work,":[82],"we":[83],"address":[84],"task":[87],"of":[88,109],"spatio-temporal":[89],"fusion":[90,107],"(STF)":[91],"surface":[94],"temperature":[95],"estimation.":[96],"We":[97],"propose":[98],"an":[99],"uncertainty-aware":[100],"TTA":[101,188],"framework":[102],"that":[103],"updates":[104],"only":[105,186],"module":[108],"pre-trained":[111,166],"STF":[112],"model,":[113],"guided":[114],"by":[115],"epistemic":[116],"uncertainty,":[117],"use":[119],"cover":[122],"consistency,":[123],"bias":[125],"correction,":[126],"without":[127],"requiring":[128],"source":[129],"or":[131],"labeled":[132],"samples.":[134],"Experiments":[135],"on":[136],"four":[137],"with":[140,180],"climates,":[142],"namely":[143],"Rome":[144],"Italy,":[146],"Cairo":[147],"Egypt,":[149],"Madrid":[150],"Spain,":[152],"Montpellier":[154],"France,":[156],"show":[157],"consistent":[158],"improvements":[159],"RMSE":[161],"MAE":[163],"model":[167],"Orl\u00e9ans,":[169],"France.":[170],"The":[171],"average":[172],"gains":[173],"24.2%":[175],"27.9%,":[177],"respectively,":[178],"even":[179],"limited":[181],"unlabeled":[182],"10":[187],"epochs.":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}
