{"id":"https://openalex.org/W3205244585","doi":"https://doi.org/10.1109/igarss47720.2021.9553999","title":"Estimating the Area of Applicability of Remote Sensing-Based Machine Learning Models with Limited Training Data","display_name":"Estimating the Area of Applicability of Remote Sensing-Based Machine Learning Models with Limited Training Data","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3205244585","doi":"https://doi.org/10.1109/igarss47720.2021.9553999","mag":"3205244585"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9553999","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9553999","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","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/A5003590626","display_name":"Hanna Meyer","orcid":"https://orcid.org/0000-0003-0556-0210"},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Hanna Meyer","raw_affiliation_strings":["Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster, M\u00fcnster, Germany"],"affiliations":[{"raw_affiliation_string":"Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster, M\u00fcnster, Germany","institution_ids":["https://openalex.org/I22465464"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054607061","display_name":"Edzer Pebesma","orcid":"https://orcid.org/0000-0001-8049-7069"},"institutions":[{"id":"https://openalex.org/I22465464","display_name":"University of M\u00fcnster","ror":"https://ror.org/00pd74e08","country_code":"DE","type":"education","lineage":["https://openalex.org/I22465464"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Edzer Pebesma","raw_affiliation_strings":["Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster, M\u00fcnster, Germany"],"affiliations":[{"raw_affiliation_string":"Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster, M\u00fcnster, Germany","institution_ids":["https://openalex.org/I22465464"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003590626"],"corresponding_institution_ids":["https://openalex.org/I22465464"],"apc_list":null,"apc_paid":null,"fwci":0.3304,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.64088655,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2028","last_page":"2030"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9980999827384949,"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":0.9980999827384949,"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/T10895","display_name":"Species Distribution and Climate Change","score":0.9952999949455261,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9805999994277954,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7306861877441406},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6861639022827148},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6499096155166626},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.6227166056632996},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5978589653968811},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5903064608573914},{"id":"https://openalex.org/keywords/cover","display_name":"Cover (algebra)","score":0.5839509963989258},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5218759775161743},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.47567692399024963},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4245322346687317},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.41368696093559265},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.332941472530365},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.27259284257888794},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10478892922401428},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09458670020103455},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08500862121582031},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06696116924285889}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7306861877441406},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6861639022827148},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6499096155166626},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6227166056632996},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5978589653968811},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5903064608573914},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.5839509963989258},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5218759775161743},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.47567692399024963},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4245322346687317},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.41368696093559265},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.332941472530365},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.27259284257888794},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10478892922401428},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09458670020103455},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08500862121582031},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06696116924285889},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9553999","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9553999","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W273955616","https://openalex.org/W2121025662","https://openalex.org/W2804233742","https://openalex.org/W2955316307","https://openalex.org/W2987883775","https://openalex.org/W3169855463","https://openalex.org/W4399636002","https://openalex.org/W6610017368","https://openalex.org/W6797397005"],"related_works":["https://openalex.org/W4232403550","https://openalex.org/W623607250","https://openalex.org/W4245429118","https://openalex.org/W4205110281","https://openalex.org/W4212927854","https://openalex.org/W4211151614","https://openalex.org/W4244798043","https://openalex.org/W4361866086","https://openalex.org/W4285322112","https://openalex.org/W4292794239"],"abstract_inverted_index":{"Machine":[0],"learning":[1,112],"has":[2],"become":[3],"state":[4],"of":[5,57,63,74,77],"the":[6,30,37,54,60,72,89,94,102,114],"art":[7],"to":[8,24,28,53,84,88,96],"map":[9],"environmental":[10],"patterns":[11],"based":[12,100],"on":[13,44,101],"remote":[14,109],"sensing":[15],"data.":[16,104],"However,":[17],"usually":[18],"few":[19],"training":[20,103],"data":[21],"points":[22],"contrast":[23],"a":[25,40,64,78],"large":[26],"area":[27,56,90],"which":[29],"model":[31,41,80,95,121],"is":[32,116],"eventually":[33],"applied.":[34],"This":[35],"raises":[36],"question":[38],"if":[39],"being":[42],"trained":[43],"limited":[45],"field":[46],"samples":[47],"can":[48,81],"actually":[49],"be":[50,82],"reliably":[51],"applied":[52],"entire":[55],"interest.":[58],"At":[59],"case":[61],"study":[62],"land":[65],"use/land":[66],"cover":[67],"classification,":[68],"we":[69,92],"present":[70],"how":[71],"\u201cArea":[73],"Applicability\u201d":[75],"(AOA)":[76],"prediction":[79],"assessed":[83],"limit":[85],"predictions":[86,119],"only":[87],"where":[91],"enabled":[93],"learn":[97],"about":[98],"relationships":[99],"We":[105],"suggest":[106],"that":[107],"in":[108],"sensing-based":[110],"machine":[111],"applciations,":[113],"AOA":[115],"reported":[117],"alongside":[118],"and":[120],"performance":[122],"estimates.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}