{"id":"https://openalex.org/W2963066447","doi":"https://doi.org/10.1109/igarss.2018.8518882","title":"AID++: An Updated Version of AID on Scene Classification","display_name":"AID++: An Updated Version of AID on Scene Classification","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2963066447","doi":"https://doi.org/10.1109/igarss.2018.8518882","mag":"2963066447"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2018.8518882","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518882","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 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/A5023815489","display_name":"Pu Jin","orcid":"https://orcid.org/0000-0001-6327-017X"},"institutions":[{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]},{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Pu Jin","raw_affiliation_strings":["Key State Laboratory LIESMARS, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Key State Laboratory LIESMARS, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073032922","display_name":"Gui-Song Xia","orcid":"https://orcid.org/0000-0001-7660-6090"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gui-Song Xia","raw_affiliation_strings":["Key State Laboratory LIESMARS, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Key State Laboratory LIESMARS, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101971382","display_name":"Fan Hu","orcid":"https://orcid.org/0000-0001-5876-0721"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Hu","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063038804","display_name":"Qikai Lu","orcid":"https://orcid.org/0000-0002-9879-3648"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qikai Lu","raw_affiliation_strings":["Electronic Information School, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Electronic Information School, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100673818","display_name":"Liangpei Zhang","orcid":"https://orcid.org/0000-0001-6890-3650"},"institutions":[{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]},{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liangpei Zhang","raw_affiliation_strings":["Key State Laboratory LIESMARS, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Key State Laboratory LIESMARS, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5023815489"],"corresponding_institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"],"apc_list":null,"apc_paid":null,"fwci":2.4685,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.91225724,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4721","last_page":"4724"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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":0.9995999932289124,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer 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":0.7961962223052979},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7594629526138306},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6970297694206238},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6555891036987305},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.6179875731468201},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.6135952472686768},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5691484212875366},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5586110353469849},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5405915975570679},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.45643699169158936},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.43841981887817383},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4378803074359894},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43016815185546875},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32926470041275024},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.32678550481796265},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10549196600914001},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.09302136301994324}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7961962223052979},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7594629526138306},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6970297694206238},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6555891036987305},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.6179875731468201},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.6135952472686768},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5691484212875366},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5586110353469849},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5405915975570679},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.45643699169158936},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43841981887817383},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4378803074359894},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43016815185546875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32926470041275024},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.32678550481796265},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10549196600914001},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.09302136301994324},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2018.8518882","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8518882","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1980038761","https://openalex.org/W2097117768","https://openalex.org/W2098676252","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2515866431","https://openalex.org/W2592962403","https://openalex.org/W2617297862","https://openalex.org/W2732026016","https://openalex.org/W2963446712","https://openalex.org/W3103856189","https://openalex.org/W3105577662","https://openalex.org/W6740934225"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2952813363","https://openalex.org/W4378678253","https://openalex.org/W2911497689","https://openalex.org/W4360783045"],"abstract_inverted_index":{"Aerial":[0,82],"image":[1,89,136],"scene":[2,45,121,174],"classification":[3,122],"is":[4],"a":[5,33,100,110,170],"fundamental":[6],"problem":[7],"for":[8,44,119,173],"understanding":[9],"high-resolution":[10],"remote":[11,25],"sensing":[12,26],"images":[13],"and":[14,52,159],"has":[15],"become":[16],"an":[17],"active":[18],"research":[19],"task":[20],"in":[21,32,74,113],"the":[22,39,49,56,60,81,125,144,146,156,160],"field":[23],"of":[24,36,41,59,132],"due":[27],"to":[28,97],"its":[29],"important":[30],"role":[31],"wide":[34],"range":[35],"applications.":[37],"However,":[38],"limitations":[40],"existing":[42,145],"datasets":[43,77],"classification,":[46],"such":[47],"as":[48,117,169],"small":[50],"scale":[51],"low-diversity,":[53],"severely":[54],"hamper":[55],"potential":[57],"usage":[58],"new":[61],"generation":[62],"deep":[63,102],"convolutional":[64],"neural":[65],"networks":[66],"(CNNs).":[67],"Although":[68],"huge":[69],"efforts":[70],"have":[71],"been":[72],"made":[73],"building":[75],"large-scale":[76],"very":[78],"recently,":[79],"e.g.,":[80],"Image":[83],"Dataset":[84],"(AID)":[85],"which":[86],"contains":[87],"10,000":[88],"samples,":[90],"they":[91],"are":[92,139],"still":[93],"far":[94],"from":[95],"sufficient":[96],"fully":[98],"train":[99],"high-capacity":[101],"CNN":[103,153],"model.":[104],"To":[105],"this":[106,114],"end,":[107],"we":[108],"present":[109],"larger-scale":[111],"dataset":[112,165],"paper,":[115],"named":[116],"AID++,":[118],"aerial":[120],"based":[123],"on":[124,155],"AID":[126],"dataset.":[127],"The":[128],"proposed":[129,157],"AID++":[130],"consists":[131],"more":[133],"than":[134],"400,000":[135],"samples":[137],"that":[138,163],"semi-automatically":[140],"annotated":[141],"by":[142],"using":[143],"geographical":[147],"data.":[148],"We":[149],"evaluate":[150],"several":[151],"prevalent":[152],"models":[154],"dataset,":[158],"results":[161],"show":[162],"our":[164],"can":[166],"be":[167],"used":[168],"promising":[171],"benchmark":[172],"classification.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}