{"id":"https://openalex.org/W4387829118","doi":"https://doi.org/10.1109/igarss52108.2023.10283274","title":"Have Foundational Models Seen Satellite Images?","display_name":"Have Foundational Models Seen Satellite Images?","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387829118","doi":"https://doi.org/10.1109/igarss52108.2023.10283274"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10283274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10283274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-04112634/document","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051448720","display_name":"Akash Panigrahi","orcid":"https://orcid.org/0000-0002-0233-910X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Akash Panigrahi","raw_affiliation_strings":["Granular AI,MA,USA","Granular AI, MA, USA"],"affiliations":[{"raw_affiliation_string":"Granular AI,MA,USA","institution_ids":[]},{"raw_affiliation_string":"Granular AI, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078173220","display_name":"Sagar Verma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sagar Verma","raw_affiliation_strings":["Granular AI,MA,USA","Granular AI, MA, USA"],"affiliations":[{"raw_affiliation_string":"Granular AI,MA,USA","institution_ids":[]},{"raw_affiliation_string":"Granular AI, MA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045608650","display_name":"Matthieu Terris","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthieu Terris","raw_affiliation_strings":["Granular AI,MA,USA","Granular AI, MA, USA"],"affiliations":[{"raw_affiliation_string":"Granular AI,MA,USA","institution_ids":[]},{"raw_affiliation_string":"Granular AI, MA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106466395","display_name":"Maria Vakalopoulou","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107720","display_name":"CentraleSup\u00e9lec","ror":"https://ror.org/019tcpt25","country_code":"FR","type":"facility","lineage":["https://openalex.org/I277688954","https://openalex.org/I4210107720"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Maria Vakalopoulou","raw_affiliation_strings":["Université Paris-Saclay,CentraleSupélec,France"],"affiliations":[{"raw_affiliation_string":"Université Paris-Saclay,CentraleSupélec,France","institution_ids":["https://openalex.org/I4210107720"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5051448720"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6949,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.76407885,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4998","last_page":"5001"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9962999820709229,"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"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9711999893188477,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.803780198097229},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.754482626914978},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5831164121627808},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5658240914344788},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.47961246967315674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45788681507110596},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.45455658435821533},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43124687671661377},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4226270318031311},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3387383818626404}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.803780198097229},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.754482626914978},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5831164121627808},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5658240914344788},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.47961246967315674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45788681507110596},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.45455658435821533},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43124687671661377},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4226270318031311},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3387383818626404},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss52108.2023.10283274","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10283274","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-04112634v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04112634","pdf_url":"https://hal.science/hal-04112634/document","source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IGARSS 2023 - International Geoscience and Remote Sensing Symposium, IEEE, Jul 2023, Pasadena, United States","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-04112634v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04112634","pdf_url":"https://hal.science/hal-04112634/document","source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IGARSS 2023 - International Geoscience and Remote Sensing Symposium, IEEE, Jul 2023, Pasadena, United States","raw_type":"Conference papers"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387829118.pdf"},"referenced_works_count":32,"referenced_works":["https://openalex.org/W2896365540","https://openalex.org/W2962749812","https://openalex.org/W2984954051","https://openalex.org/W2986943971","https://openalex.org/W3126337491","https://openalex.org/W3166396011","https://openalex.org/W3176164117","https://openalex.org/W3176198174","https://openalex.org/W3184735396","https://openalex.org/W3206072662","https://openalex.org/W3215626407","https://openalex.org/W4226182655","https://openalex.org/W4293065229","https://openalex.org/W4297670610","https://openalex.org/W4300176521","https://openalex.org/W4308234044","https://openalex.org/W4309569914","https://openalex.org/W4312601179","https://openalex.org/W4312786529","https://openalex.org/W4319336162","https://openalex.org/W4366781036","https://openalex.org/W6744073472","https://openalex.org/W6755507413","https://openalex.org/W6790019176","https://openalex.org/W6791353385","https://openalex.org/W6798805250","https://openalex.org/W6802517928","https://openalex.org/W6803872405","https://openalex.org/W6804143396","https://openalex.org/W6811013733","https://openalex.org/W6847055193","https://openalex.org/W6851793049"],"related_works":["https://openalex.org/W4367313141","https://openalex.org/W2004086023","https://openalex.org/W2733999579","https://openalex.org/W2910751785","https://openalex.org/W2110217573","https://openalex.org/W4283374591","https://openalex.org/W4366547507","https://openalex.org/W4387891126","https://openalex.org/W4390100400","https://openalex.org/W2074396925"],"abstract_inverted_index":{"This":[0,143],"paper":[1],"presents":[2],"an":[3,47],"investigation":[4],"into":[5,148],"the":[6,63,81,95,103,128,138,149,160],"zero-shot":[7,109],"performance":[8,110,135],"of":[9,31,41,50,83,90,130,151],"pre-trained":[10],"foundation":[11,152],"models":[12,52,69,106,153],"on":[13,28,53,111,122,137],"remote":[14,155],"sensing":[15,156],"tasks.":[16,43],"Recent":[17],"advances":[18],"in":[19,154],"self-supervised":[20],"learning":[21],"suggest":[22],"that":[23,102,120],"these":[24,51,68,112],"models,":[25],"when":[26],"trained":[27],"vast":[29],"amounts":[30],"unsupervised":[32],"data,":[33],"could":[34],"potentially":[35],"improve":[36],"generalization":[37],"across":[38],"a":[39,85],"number":[40],"downstream":[42],"Our":[44,99],"study":[45],"offers":[46],"empirical":[48],"evaluation":[49],"standard":[54,96,117,123],"remote-sensing":[55],"benchmarks":[56,113,124],"such":[57],"as":[58],"EuroSAT":[59],"and":[60,141,158],"BigEarthNet-S2,":[61],"with":[62,94],"intent":[64],"to":[65,115],"confirm":[66],"whether":[67],"have":[70],"encountered":[71],"satellite":[72],"imagery":[73],"during":[74],"their":[75,116],"training":[76],"phase.":[77],"Moreover,":[78],"we":[79],"examine":[80],"impact":[82],"adding":[84],"geospatial":[86,131],"domain-specific":[87],"textual":[88],"description":[89],"classes,":[91],"contrasting":[92],"it":[93],"class-based":[97],"prompts.":[98],"findings":[100],"indicate":[101],"fine-tuned":[104],"BLIP":[105],"exhibit":[107],"superior":[108],"compared":[114],"counterparts,":[118],"signifying":[119],"fine-tuning":[121],"enhances":[125],"performance.":[126],"Furthermore,":[127],"addition":[129],"context":[132],"variably":[133],"influences":[134],"depending":[136],"specific":[139],"model":[140],"dataset.":[142],"work":[144],"provides":[145],"crucial":[146],"insights":[147],"applicability":[150],"tasks":[157],"lays":[159],"groundwork":[161],"for":[162],"further":[163],"research.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}