{"id":"https://openalex.org/W7131077550","doi":"https://doi.org/10.1109/iccvw69036.2025.00297","title":"Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction","display_name":"Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131077550","doi":"https://doi.org/10.1109/iccvw69036.2025.00297"},"language":null,"primary_location":{"id":"doi:10.1109/iccvw69036.2025.00297","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"},"type":"article","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/A5114221461","display_name":"Shengjie Kris Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shengjie Liu","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110653828","display_name":"Lu Zhang","orcid":"https://orcid.org/0000-0003-2284-8005"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lu Zhang","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126611328","display_name":"Siqin Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I2800817003","display_name":"Southern California University for Professional Studies","ror":"https://ror.org/058zz0t50","country_code":"US","type":"education","lineage":["https://openalex.org/I2800817003"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siqin Wang","raw_affiliation_strings":["University of Southern California"],"affiliations":[{"raw_affiliation_string":"University of Southern California","institution_ids":["https://openalex.org/I2800817003","https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114221461"],"corresponding_institution_ids":["https://openalex.org/I1174212","https://openalex.org/I2800817003"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.7468929,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2845","last_page":"2854"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.09839999675750732,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12994","display_name":"Infrared Thermography in Medicine","score":0.09839999675750732,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.07829999923706055,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10766","display_name":"Urban Heat Island Mitigation","score":0.039500001817941666,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7251999974250793},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5906000137329102},{"id":"https://openalex.org/keywords/temporal-resolution","display_name":"Temporal resolution","score":0.5374000072479248},{"id":"https://openalex.org/keywords/earth-observation","display_name":"Earth observation","score":0.4828999936580658},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4722000062465668},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4602000117301941},{"id":"https://openalex.org/keywords/temperature-measurement","display_name":"Temperature measurement","score":0.4390000104904175},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.38040000200271606}],"concepts":[{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7251999974250793},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5906000137329102},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5662999749183655},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.5374000072479248},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5263000130653381},{"id":"https://openalex.org/C39399123","wikidata":"https://www.wikidata.org/wiki/Q1348989","display_name":"Earth observation","level":3,"score":0.4828999936580658},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C72293138","wikidata":"https://www.wikidata.org/wiki/Q909741","display_name":"Temperature measurement","level":2,"score":0.4390000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.413100004196167},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.38040000200271606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3720000088214874},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C80368990","wikidata":"https://www.wikidata.org/wiki/Q3046459","display_name":"Earth system science","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.2784999907016754},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.27300000190734863},{"id":"https://openalex.org/C2779242930","wikidata":"https://www.wikidata.org/wiki/Q720957","display_name":"Global temperature","level":4,"score":0.2696000039577484},{"id":"https://openalex.org/C607359","wikidata":"https://www.wikidata.org/wiki/Q845339","display_name":"Atmospheric temperature","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00297","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00297","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.7727981209754944,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2003133188","https://openalex.org/W2020977453","https://openalex.org/W2021057500","https://openalex.org/W2024339093","https://openalex.org/W2028191110","https://openalex.org/W2042546476","https://openalex.org/W2805159576","https://openalex.org/W2883055646","https://openalex.org/W2918246750","https://openalex.org/W2939432878","https://openalex.org/W2968209379","https://openalex.org/W2974432251","https://openalex.org/W2974527409","https://openalex.org/W2986335958","https://openalex.org/W3024727387","https://openalex.org/W3029542277","https://openalex.org/W3043547428","https://openalex.org/W3136729388","https://openalex.org/W3155993469","https://openalex.org/W3183250374","https://openalex.org/W3212625943","https://openalex.org/W4221002889","https://openalex.org/W4295677933","https://openalex.org/W4312203760","https://openalex.org/W4313064055","https://openalex.org/W4367671691","https://openalex.org/W4380987158","https://openalex.org/W4390873026","https://openalex.org/W4396782951","https://openalex.org/W4402475106","https://openalex.org/W4404970461","https://openalex.org/W4406657617","https://openalex.org/W4407271135","https://openalex.org/W4407806811","https://openalex.org/W4408318394","https://openalex.org/W4409094242"],"related_works":[],"abstract_inverted_index":{"Central":[0],"to":[1,105],"Earth":[2,51,109],"observation":[3],"is":[4,15],"the":[5,96,107],"trade-off":[6],"between":[7],"spatial":[8,64,168],"and":[9,100,133,140,147,169,176,179],"temporal":[10,170],"resolution.":[11],"For":[12],"temperature,":[13],"this":[14,122],"especially":[16],"critical":[17],"because":[18],"real-world":[19],"applications":[20],"require":[21],"high":[22],"spatiotemporal":[23],"resolution":[24,65],"data.":[25],"Current":[26],"technology":[27],"allows":[28],"for":[29,76,162],"hourly":[30,56],"temperature":[31,57,77,98,115,143,165],"observations":[32],"at":[33,41,60,183],"2":[34],"km,":[35,131],"but":[36,59],"only":[37],"every":[38,137],"16":[39,138],"days":[40],"100":[42],"m,":[43,136],"a":[44,61,71,90,102],"gap":[45],"further":[46],"exacerbated":[47],"by":[48],"cloud":[49],"cover.":[50],"system":[52,110],"models":[53],"offer":[54],"continuous":[55],"data,":[58],"much":[62],"coarser":[63],"(9\u201331":[66],"km).":[67],"Here,":[68],"we":[69],"present":[70],"physics-guided":[72,155],"deep":[73,156],"learning":[74,157],"framework":[75,88,123,158],"data":[78,84,125,150,166,180],"reconstruction":[79,144],"that":[80,94],"integrates":[81],"these":[82],"two":[83,127],"sources.":[85],"The":[86],"proposed":[87],"uses":[89],"convolutional":[91],"neural":[92],"network":[93],"incorporates":[95],"annual":[97],"cycle":[99],"includes":[101],"linear":[103],"term":[104],"amplify":[106],"coarse":[108],"model":[111],"output":[112],"into":[113],"fine-scale":[114],"values":[116],"observed":[117],"from":[118,126],"satellites.":[119],"We":[120],"evaluated":[121],"using":[124],"satellites,":[128],"GOES-16":[129],"(2":[130],"hourly)":[132],"Landsat":[134],"(100":[135],"days),":[139],"demonstrated":[141],"effective":[142],"with":[145],"hold-out":[146],"in":[148],"situ":[149],"across":[151,167],"four":[152],"datasets.":[153],"This":[154],"opens":[159],"new":[160],"possibilities":[161],"generating":[163],"high-resolution":[164],"scales,":[171],"under":[172],"all":[173],"weather":[174],"conditions":[175],"globally.":[177],"Code":[178],"are":[181],"available":[182],"skrisliu.com/r2lst.":[184]},"counts_by_year":[],"updated_date":"2026-02-25T06:17:34.324206","created_date":"2026-02-24T00:00:00"}
