{"id":"https://openalex.org/W4380052818","doi":"https://doi.org/10.3390/rs15122980","title":"AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning","display_name":"AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning","publication_year":2023,"publication_date":"2023-06-07","ids":{"openalex":"https://openalex.org/W4380052818","doi":"https://doi.org/10.3390/rs15122980"},"language":"en","primary_location":{"id":"doi:10.3390/rs15122980","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15122980","pdf_url":"https://www.mdpi.com/2072-4292/15/12/2980/pdf?version=1686299525","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/15/12/2980/pdf?version=1686299525","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034409884","display_name":"Teodora Selea","orcid":"https://orcid.org/0000-0001-8411-9081"},"institutions":[{"id":"https://openalex.org/I3121153395","display_name":"West University of Timi\u015foara","ror":"https://ror.org/0583a0t97","country_code":"RO","type":"education","lineage":["https://openalex.org/I3121153395"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Teodora Selea","raw_affiliation_strings":["Faculty of Mathematics and Informatics, West University of Timisoara, 300223 Timisoara, Romania"],"affiliations":[{"raw_affiliation_string":"Faculty of Mathematics and Informatics, West University of Timisoara, 300223 Timisoara, Romania","institution_ids":["https://openalex.org/I3121153395"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5034409884"],"corresponding_institution_ids":["https://openalex.org/I3121153395"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.9666,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.90420046,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"15","issue":"12","first_page":"2980","last_page":"2980"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9995999932289124,"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":0.9937999844551086,"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/T10895","display_name":"Species Distribution and Climate Change","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"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/computer-science","display_name":"Computer science","score":0.7549365162849426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5598868131637573},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4848845899105072},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.479274719953537},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4585852324962616},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4286649823188782},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4106898009777069},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3879864811897278},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.15549063682556152},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07563263177871704}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7549365162849426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5598868131637573},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4848845899105072},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.479274719953537},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4585852324962616},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4286649823188782},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4106898009777069},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3879864811897278},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.15549063682556152},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07563263177871704}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs15122980","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15122980","pdf_url":"https://www.mdpi.com/2072-4292/15/12/2980/pdf?version=1686299525","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:26952d32de224b5186bf76b42f2d084f","is_oa":true,"landing_page_url":"https://doaj.org/article/26952d32de224b5186bf76b42f2d084f","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 15, Iss 12, p 2980 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/15/12/2980/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs15122980","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Remote Sensing; Volume 15; Issue 12; Pages: 2980","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs15122980","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs15122980","pdf_url":"https://www.mdpi.com/2072-4292/15/12/2980/pdf?version=1686299525","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G713261907","display_name":null,"funder_award_id":"4000125049/18/NL/CBI","funder_id":"https://openalex.org/F4320318240","funder_display_name":"European Space Agency"}],"funders":[{"id":"https://openalex.org/F4320318240","display_name":"European Space Agency","ror":"https://ror.org/03wd9za21"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4380052818.pdf"},"referenced_works_count":67,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1485981043","https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W2027025039","https://openalex.org/W2056435747","https://openalex.org/W2064675550","https://openalex.org/W2084616221","https://openalex.org/W2108598243","https://openalex.org/W2122111042","https://openalex.org/W2133059825","https://openalex.org/W2525726844","https://openalex.org/W2604239613","https://openalex.org/W2892654670","https://openalex.org/W2907663452","https://openalex.org/W2937531545","https://openalex.org/W2940726923","https://openalex.org/W2945887186","https://openalex.org/W2963131120","https://openalex.org/W2973660294","https://openalex.org/W2976120863","https://openalex.org/W2986943971","https://openalex.org/W2992136964","https://openalex.org/W3016537533","https://openalex.org/W3021798522","https://openalex.org/W3028167980","https://openalex.org/W3031909544","https://openalex.org/W3037957586","https://openalex.org/W3046918937","https://openalex.org/W3048987266","https://openalex.org/W3090408655","https://openalex.org/W3095668211","https://openalex.org/W3107296377","https://openalex.org/W3134173255","https://openalex.org/W3152821670","https://openalex.org/W3176720156","https://openalex.org/W3190941789","https://openalex.org/W3191920065","https://openalex.org/W3198940810","https://openalex.org/W3205533682","https://openalex.org/W3206279381","https://openalex.org/W3206628053","https://openalex.org/W3209731719","https://openalex.org/W3210256600","https://openalex.org/W3214997015","https://openalex.org/W4205550387","https://openalex.org/W4206617818","https://openalex.org/W4224250287","https://openalex.org/W4224256157","https://openalex.org/W4225728415","https://openalex.org/W4280583662","https://openalex.org/W4281703655","https://openalex.org/W4285294545","https://openalex.org/W4288081131","https://openalex.org/W4297793267","https://openalex.org/W4300402905","https://openalex.org/W4303980688","https://openalex.org/W4307261353","https://openalex.org/W4308532805","https://openalex.org/W4313361786","https://openalex.org/W4313595226","https://openalex.org/W4317207956","https://openalex.org/W6604803494","https://openalex.org/W6628877408","https://openalex.org/W6718471829","https://openalex.org/W6739901393","https://openalex.org/W6749040057"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4380075502"],"abstract_inverted_index":{"With":[0],"the":[1,56,77,80,97,116,127,165,168,209,213],"increasing":[2],"volume":[3],"of":[4,28,58,82,99,118,140,167,221],"collected":[5],"Earth":[6],"observation":[7],"(EO)":[8],"data,":[9],"artificial":[10],"intelligence":[11],"(AI)":[12],"methods":[13],"have":[14],"become":[15],"state-of-the-art":[16,176],"in":[17,101,212],"processing":[18],"and":[19,76,89,103,144,161,187,190,206,224,250],"analyzing":[20],"them.":[21],"However,":[22],"there":[23],"is":[24,87],"still":[25],"a":[26,41,217,232,238,246],"lack":[27],"high-quality,":[29],"large-scale":[30,42],"EO":[31],"datasets":[32],"for":[33,45,132,179],"training":[34],"robust":[35],"networks.":[36],"This":[37],"paper":[38],"presents":[39],"AgriSen-COG,":[40],"benchmark":[43],"dataset":[44,131],"crop":[46,133],"type":[47,134],"mapping":[48],"based":[49,95],"on":[50,96],"Sentinel-2":[51],"data.":[52],"AgriSen-COG":[53,107,121,245],"deals":[54],"with":[55,183,193,216,237],"challenges":[57],"remote":[59],"sensing":[60],"(RS)":[61],"datasets.":[62],"First,":[63],"it":[64,86,126],"includes":[65,108,137],"data":[66,143,185,228],"from":[67],"five":[68],"different":[69],"European":[70],"countries":[71],"(Austria,":[72],"Belgium,":[73],"Spain,":[74],"Denmark,":[75],"Netherlands),":[78],"targeting":[79],"problem":[81],"domain":[83],"adaptation.":[84],"Second,":[85],"multitemporal":[88],"multiyear":[90],"(2019\u20132020),":[91],"therefore":[92],"enabling":[93],"analysis":[94],"growth":[98],"crops":[100],"time":[102],"yearly":[104],"variability.":[105],"Third,":[106],"an":[109],"anomaly":[110],"detection":[111],"preprocessing":[112],"step,":[113],"which":[114,243],"reduces":[115],"amount":[117],"mislabeled":[119],"information.":[120,147],"comprises":[122],"6,972,485":[123],"parcels,":[124],"making":[125],"most":[128,202],"extensive":[129],"available":[130],"mapping.":[135],"It":[136],"two":[138,154],"types":[139],"data:":[141],"pixel-level":[142,184],"parcel":[145,194],"aggregated":[146,195],"By":[148],"carrying":[149],"this":[150],"out,":[151],"we":[152,171],"target":[153],"computer":[155],"vision":[156],"(CV)":[157],"problems:":[158],"semantic":[159,181],"segmentation":[160,182],"classification.":[162],"To":[163],"establish":[164],"validity":[166],"proposed":[169,227],"dataset,":[170],"conducted":[172],"several":[173],"experiments":[174],"using":[175],"deep-learning":[177],"models":[178,204],"temporal":[180],"(U-Net":[186,205],"ConvStar":[188],"networks)":[189],"time-series":[191],"classification":[192],"information":[196],"(LSTM,":[197],"Transformer,":[198],"TempCNN":[199],"networks).":[200],"The":[201],"popular":[203],"LSTM)":[207],"achieve":[208],"best":[210],"performance":[211],"Belgium":[214],"region,":[215],"weighted":[218],"F1":[219],"score":[220],"0.956":[222],"(U-Net)":[223],"0.918":[225],"(LSTM).The":[226],"are":[229],"distributed":[230],"as":[231],"cloud-optimized":[233],"GeoTIFF":[234],"(COG),":[235],"together":[236],"SpatioTemporal":[239],"Asset":[240],"Catalog":[241],"(STAC),":[242],"makes":[244],"findable,":[247],"accessible,":[248],"interoperable,":[249],"reusable":[251],"(FAIR)":[252],"dataset.":[253]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}