{"id":"https://openalex.org/W4312634899","doi":"https://doi.org/10.1109/igarss46834.2022.9883919","title":"Extracting High-Resolution Cultivated Land Maps from Sentinel-2 Image Series","display_name":"Extracting High-Resolution Cultivated Land Maps from Sentinel-2 Image Series","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312634899","doi":"https://doi.org/10.1109/igarss46834.2022.9883919"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9883919","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883919","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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/A5040096140","display_name":"Tomasz Tarasiewicz","orcid":"https://orcid.org/0000-0002-7706-1317"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tomasz Tarasiewicz","raw_affiliation_strings":["KP Labs,Gliwice,Poland,44-100"],"affiliations":[{"raw_affiliation_string":"KP Labs,Gliwice,Poland,44-100","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060559828","display_name":"\u0141ukasz Tulczyjew","orcid":"https://orcid.org/0000-0003-0763-0745"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lukasz Tulczyjew","raw_affiliation_strings":["KP Labs,Gliwice,Poland,44-100"],"affiliations":[{"raw_affiliation_string":"KP Labs,Gliwice,Poland,44-100","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001358973","display_name":"Micha\u0142 Myller","orcid":"https://orcid.org/0000-0001-9265-3997"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michal Myller","raw_affiliation_strings":["KP Labs,Gliwice,Poland,44-100"],"affiliations":[{"raw_affiliation_string":"KP Labs,Gliwice,Poland,44-100","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086221093","display_name":"Micha\u0142 Kawulok","orcid":"https://orcid.org/0000-0002-3669-5110"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michal Kawulok","raw_affiliation_strings":["KP Labs,Gliwice,Poland,44-100"],"affiliations":[{"raw_affiliation_string":"KP Labs,Gliwice,Poland,44-100","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009020295","display_name":"Nicolas Long\u00e9p\u00e9","orcid":"https://orcid.org/0000-0002-6832-3274"},"institutions":[{"id":"https://openalex.org/I4210165017","display_name":"European Space Research Institute","ror":"https://ror.org/05vt9rv16","country_code":"IT","type":"facility","lineage":["https://openalex.org/I2801994115","https://openalex.org/I4210165017"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nicolas Longepe","raw_affiliation_strings":["&#x03A6;-Lab,European Space Agency,Frascati,Italy,00044"],"affiliations":[{"raw_affiliation_string":"&#x03A6;-Lab,European Space Agency,Frascati,Italy,00044","institution_ids":["https://openalex.org/I4210165017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041796575","display_name":"Jakub Nalepa","orcid":"https://orcid.org/0000-0002-4026-1569"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jakub Nalepa","raw_affiliation_strings":["KP Labs,Gliwice,Poland,44-100"],"affiliations":[{"raw_affiliation_string":"KP Labs,Gliwice,Poland,44-100","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5040096140"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4795,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.63275782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"12","issue":null,"first_page":"175","last_page":"178"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.995199978351593,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multispectral-image","display_name":"Multispectral image","score":0.7535523772239685},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6956326961517334},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5549161434173584},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5252865552902222},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5177575945854187},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4605483412742615},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4550945460796356},{"id":"https://openalex.org/keywords/high-resolution","display_name":"High resolution","score":0.41456690430641174},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3399425148963928},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3300594687461853},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.19425460696220398},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12955275177955627},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09592753648757935}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.7535523772239685},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6956326961517334},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5549161434173584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5252865552902222},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5177575945854187},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4605483412742615},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4550945460796356},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.41456690430641174},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3399425148963928},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3300594687461853},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.19425460696220398},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12955275177955627},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09592753648757935},{"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9883919","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883919","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1966580635","https://openalex.org/W2101234009","https://openalex.org/W2767953525","https://openalex.org/W2982389494","https://openalex.org/W2991616716","https://openalex.org/W3036739482","https://openalex.org/W3044233001","https://openalex.org/W3166222868","https://openalex.org/W3175740668","https://openalex.org/W3207652155","https://openalex.org/W6642159730","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W4318664220","https://openalex.org/W2771047279","https://openalex.org/W4388409104","https://openalex.org/W2124951708","https://openalex.org/W1544811710","https://openalex.org/W172072032","https://openalex.org/W2006066416","https://openalex.org/W2011962637","https://openalex.org/W2163677711","https://openalex.org/W2036727360"],"abstract_inverted_index":{"The":[0,87],"recent":[1],"advances":[2],"in":[3,131],"Earth":[4],"observation":[5],"and":[6,23,53,67,74,112],"artificial":[7],"in-telligence":[8],"allow":[9],"us":[10,123],"to":[11,124],"improve":[12],"the":[13,20,27,40,64,71,78,92,95,109,126,132],"agricultural":[14],"management":[15],"practices":[16],"through":[17],"effectively":[18],"exploiting":[19],"spectral,":[21,65],"spatial,":[22,66],"temporal":[24,68],"characteristics":[25],"of":[26,29,42,70,80,94],"area":[28],"interest":[30],"captured":[31,82],"by":[32],"satellite":[33],"images.":[34],"In":[35],"this":[36,60,120],"paper,":[37],"we":[38],"tackle":[39],"problem":[41],"extracting":[43],"high-resolution":[44],"(2.5-meter)":[45],"cultivated":[46],"land":[47,116],"maps":[48],"from":[49,77],"Sentinel-2":[50,97],"multispectral":[51],"images,":[52,73],"propose":[54],"a":[55,84],"machine":[56],"learning":[57],"algorithm":[58,121],"for":[59,83],"task.":[61],"It":[62],"aggregates":[63],"features":[69],"upsampled":[72],"is":[75],"independent":[76],"number":[79],"observations":[81],"given":[85],"scene.":[86],"experimental":[88],"results,":[89],"performed":[90],"within":[91],"framework":[93],"Enhanced":[96],"Agriculture":[98],"chal-lenge":[99],"show":[100],"that":[101],"our":[102],"technique":[103],"manifests":[104],"high":[105],"generalization":[106],"abilities":[107],"over":[108],"unseen":[110],"data":[111],"elaborates":[113],"high-quality":[114],"cul-tivated":[115],"maps.":[117],"Finally,":[118],"utilizing":[119],"led":[122],"taking":[125],"<tex":[127],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[128],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$6^{\\text{th}}$</tex>":[129],"place":[130],"aforementioned":[133],"challenge.":[134]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}