{"id":"https://openalex.org/W4401597797","doi":"https://doi.org/10.1109/coins61597.2024.10622428","title":"Deep Learning Models to Estimate High Resolution NDVI for Multiple Augmentation Factors","display_name":"Deep Learning Models to Estimate High Resolution NDVI for Multiple Augmentation Factors","publication_year":2024,"publication_date":"2024-07-29","ids":{"openalex":"https://openalex.org/W4401597797","doi":"https://doi.org/10.1109/coins61597.2024.10622428"},"language":"en","primary_location":{"id":"doi:10.1109/coins61597.2024.10622428","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/coins61597.2024.10622428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","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/A5047217039","display_name":"M\u00edkel Zabala","orcid":"https://orcid.org/0000-0002-8700-0382"},"institutions":[{"id":"https://openalex.org/I4210092551","display_name":"Vicomtech","ror":"https://ror.org/0023sah13","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210092551"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Mikel Zabala","raw_affiliation_strings":["Data Intelligence for Energy and Industrial Processes Vicomtech Foundation,Donostia,Spain"],"affiliations":[{"raw_affiliation_string":"Data Intelligence for Energy and Industrial Processes Vicomtech Foundation,Donostia,Spain","institution_ids":["https://openalex.org/I4210092551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081889691","display_name":"Izar Azpiroz","orcid":"https://orcid.org/0000-0003-0401-8139"},"institutions":[{"id":"https://openalex.org/I4210092551","display_name":"Vicomtech","ror":"https://ror.org/0023sah13","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210092551"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Izar Azpiroz","raw_affiliation_strings":["Data Intelligence for Energy and Industrial Processes Vicomtech Foundation,Donostia,Spain"],"affiliations":[{"raw_affiliation_string":"Data Intelligence for Energy and Industrial Processes Vicomtech Foundation,Donostia,Spain","institution_ids":["https://openalex.org/I4210092551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008595989","display_name":"Paula Gonz\u00e1lez","orcid":"https://orcid.org/0000-0003-0154-0087"},"institutions":[{"id":"https://openalex.org/I4210092551","display_name":"Vicomtech","ror":"https://ror.org/0023sah13","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210092551"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Paula Gonzalez","raw_affiliation_strings":["Data Intelligence for Energy and Industrial Processes Vicomtech Foundation,Donostia,Spain"],"affiliations":[{"raw_affiliation_string":"Data Intelligence for Energy and Industrial Processes Vicomtech Foundation,Donostia,Spain","institution_ids":["https://openalex.org/I4210092551"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018769938","display_name":"Mikel Maiza","orcid":"https://orcid.org/0000-0002-6321-8053"},"institutions":[{"id":"https://openalex.org/I4210092551","display_name":"Vicomtech","ror":"https://ror.org/0023sah13","country_code":"ES","type":"facility","lineage":["https://openalex.org/I4210092551"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Mikel Maiza","raw_affiliation_strings":["Data Intelligence for Energy and Industrial Processes Vicomtech Foundation,Donostia,Spain"],"affiliations":[{"raw_affiliation_string":"Data Intelligence for Energy and Industrial Processes Vicomtech Foundation,Donostia,Spain","institution_ids":["https://openalex.org/I4210092551"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5047217039"],"corresponding_institution_ids":["https://openalex.org/I4210092551"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21734222,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.4893999993801117,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.4893999993801117,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.657957911491394},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.6040073037147522},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5757995247840881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.495220810174942},{"id":"https://openalex.org/keywords/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.41742151975631714},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3527807593345642},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33416664600372314},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.15756160020828247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.657957911491394},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.6040073037147522},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5757995247840881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.495220810174942},{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.41742151975631714},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3527807593345642},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33416664600372314},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.15756160020828247},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/coins61597.2024.10622428","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/coins61597.2024.10622428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2037133124","https://openalex.org/W2054054246","https://openalex.org/W2059523177","https://openalex.org/W2133125644","https://openalex.org/W2133665775","https://openalex.org/W2478493250","https://openalex.org/W2950689937","https://openalex.org/W2952773607","https://openalex.org/W2954996726","https://openalex.org/W2963372104","https://openalex.org/W2963470893","https://openalex.org/W2995545753","https://openalex.org/W3023704589","https://openalex.org/W3031909544","https://openalex.org/W3046020299","https://openalex.org/W3080988368","https://openalex.org/W3081611557","https://openalex.org/W3086482765","https://openalex.org/W3108538688","https://openalex.org/W4283022469","https://openalex.org/W4285080405","https://openalex.org/W4387801770","https://openalex.org/W6631190155"],"related_works":["https://openalex.org/W3207046288","https://openalex.org/W3023446922","https://openalex.org/W4324030030","https://openalex.org/W1980260791","https://openalex.org/W4385533602","https://openalex.org/W3189212133","https://openalex.org/W4382239404","https://openalex.org/W2053086167","https://openalex.org/W4382519838","https://openalex.org/W4380075502"],"abstract_inverted_index":{"The":[0,123],"Normalised":[1],"Difference":[2],"Vegetation":[3],"Index":[4],"(NDVI)":[5],"is":[6],"a":[7],"highly":[8],"useful":[9],"tool":[10],"for":[11,73,93,160],"monitoring":[12],"vegetation,":[13],"derived":[14],"from":[15,78,105],"remote":[16],"sensing":[17],"satellite":[18,164],"data":[19],"such":[20],"as":[21,70],"Sentinel-2":[22,147],"satellites.":[23],"However,":[24],"the":[25,85,110,129,143,158],"10m":[26],"resolution":[27,45,76,80,144],"provided":[28,151],"by":[29,152],"these":[30],"satellites":[31,41,154],"may":[32],"not":[33],"always":[34],"be":[35,50],"sufficient.":[36],"While":[37],"there":[38],"are":[39,103,121,132],"commercial":[40,153],"that":[42],"offer":[43],"high":[44,75],"images,":[46],"they":[47],"tend":[48],"to":[49,56,61,127,141],"prohibitively":[51],"expensive":[52],"and":[53,109,135,149],"thus":[54],"inaccessible":[55],"many":[57],"users.":[58],"In":[59,82],"response":[60],"this":[62,83,99],"challenge,":[63],"Deep":[64],"Learning":[65],"(DL)":[66],"models":[67,120],"have":[68],"emerged":[69],"valuable":[71],"tools":[72],"estimating":[74],"images":[77,102,148],"low":[79],"inputs.":[81],"context,":[84],"present":[86],"study":[87],"provides":[88],"distinct":[89],"augmentation":[90],"factor":[91],"results":[92,131],"High":[94],"Resolution":[95],"NDVI":[96,130],"estimations.":[97],"For":[98],"purpose,":[100],"training-testing":[101],"extracted":[104],"WorldView":[106],"satellite-derived":[107],"products,":[108],"two":[111],"variants":[112],"of":[113],"well-known":[114],"DL":[115],"Super-Resolution":[116],"Residual":[117],"Network":[118],"(SRResNet)":[119],"selected.":[122],"augmenting":[124],"factors":[125],"considered":[126],"enhance":[128],"5,":[133],"6.25,":[134],"8.":[136],"This":[137],"preliminary":[138],"context":[139],"aims":[140],"bridge":[142],"gap":[145],"between":[146],"those":[150],"like":[155],"WorldView,":[156],"laying":[157],"groundwork":[159],"future":[161],"improvements":[162],"in":[163],"image":[165],"resolution.":[166]},"counts_by_year":[],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
