{"id":"https://openalex.org/W2609402060","doi":"https://doi.org/10.1109/igarss.2017.8127684","title":"Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark","display_name":"Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2609402060","doi":"https://doi.org/10.1109/igarss.2017.8127684","mag":"2609402060"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8127684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://inria.hal.science/hal-01468452","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090274212","display_name":"Emmanuel Maggiori","orcid":"https://orcid.org/0000-0002-5765-9629"},"institutions":[{"id":"https://openalex.org/I4210106545","display_name":"Research Centre Inria Sophia Antipolis - M\u00e9diterran\u00e9e","ror":"https://ror.org/01nzkaw91","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283","https://openalex.org/I201841394","https://openalex.org/I4210106545"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Emmanuel Maggiori","raw_affiliation_strings":["TITANE team, Inria Sophia Antipolis - M\u00e9diterran\u00e9e"],"affiliations":[{"raw_affiliation_string":"TITANE team, Inria Sophia Antipolis - M\u00e9diterran\u00e9e","institution_ids":["https://openalex.org/I4210106545"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025954012","display_name":"Yuliya Tarabalka","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106545","display_name":"Research Centre Inria Sophia Antipolis - M\u00e9diterran\u00e9e","ror":"https://ror.org/01nzkaw91","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283","https://openalex.org/I201841394","https://openalex.org/I4210106545"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Yuliya Tarabalka","raw_affiliation_strings":["TITANE team, Inria Sophia Antipolis - M\u00e9diterran\u00e9e"],"affiliations":[{"raw_affiliation_string":"TITANE team, Inria Sophia Antipolis - M\u00e9diterran\u00e9e","institution_ids":["https://openalex.org/I4210106545"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021255142","display_name":"Guillaume Charpiat","orcid":"https://orcid.org/0009-0003-6000-9410"},"institutions":[{"id":"https://openalex.org/I4210126360","display_name":"Inria Saclay - \u00cele de France","ror":"https://ror.org/0315e5x55","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283","https://openalex.org/I4210126360"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Guillaume Charpiat","raw_affiliation_strings":["TAO team, Inria Saclay, France"],"affiliations":[{"raw_affiliation_string":"TAO team, Inria Saclay, France","institution_ids":["https://openalex.org/I4210126360"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086753450","display_name":"Pierre Alliez","orcid":"https://orcid.org/0000-0002-6214-4005"},"institutions":[{"id":"https://openalex.org/I4210106545","display_name":"Research Centre Inria Sophia Antipolis - M\u00e9diterran\u00e9e","ror":"https://ror.org/01nzkaw91","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283","https://openalex.org/I201841394","https://openalex.org/I4210106545"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Pierre Alliez","raw_affiliation_strings":["TITANE team, Inria Sophia Antipolis - M\u00e9diterran\u00e9e"],"affiliations":[{"raw_affiliation_string":"TITANE team, Inria Sophia Antipolis - M\u00e9diterran\u00e9e","institution_ids":["https://openalex.org/I4210106545"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5090274212"],"corresponding_institution_ids":["https://openalex.org/I4210106545"],"apc_list":null,"apc_paid":null,"fwci":50.5654,"has_fulltext":false,"cited_by_count":800,"citation_normalized_percentile":{"value":0.99908352,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3226","last_page":"3229"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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":0.9950000047683716,"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/aerial-image","display_name":"Aerial image","score":0.7744038105010986},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7384225130081177},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7355461120605469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6918467283248901},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6488286256790161},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6099031567573547},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5990990400314331},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5553125143051147},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.5424284934997559},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.524804949760437},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5245419144630432},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.45366814732551575},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4391513466835022},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.43678227066993713},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.41140252351760864},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3710680603981018},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3533898591995239},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.17258095741271973},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.16933369636535645},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11026829481124878}],"concepts":[{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.7744038105010986},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7384225130081177},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7355461120605469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6918467283248901},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6488286256790161},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6099031567573547},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5990990400314331},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5553125143051147},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.5424284934997559},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.524804949760437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5245419144630432},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.45366814732551575},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4391513466835022},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.43678227066993713},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.41140252351760864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3710680603981018},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3533898591995239},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.17258095741271973},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.16933369636535645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11026829481124878},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss.2017.8127684","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127684","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01468452v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-01468452","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Jul 2017, Fort Worth, United States. pp.3226-3229, &#x27E8;10.1109/IGARSS.2017.8127684&#x27E9;","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-01468452v1","is_oa":true,"landing_page_url":"https://inria.hal.science/hal-01468452","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Jul 2017, Fort Worth, United States. pp.3226-3229, &#x27E8;10.1109/IGARSS.2017.8127684&#x27E9;","raw_type":"Conference papers"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2001298023","https://openalex.org/W2069412682","https://openalex.org/W2205800717","https://openalex.org/W2222496544","https://openalex.org/W2538244214","https://openalex.org/W2623331213","https://openalex.org/W2997148986","https://openalex.org/W3102850314","https://openalex.org/W3105127913","https://openalex.org/W6728725855","https://openalex.org/W6786026892"],"related_works":["https://openalex.org/W4225124612","https://openalex.org/W2043806667","https://openalex.org/W1999699871","https://openalex.org/W2021633306","https://openalex.org/W2006801911","https://openalex.org/W2033669961","https://openalex.org/W3147207884","https://openalex.org/W1972167985","https://openalex.org/W2971899271","https://openalex.org/W2350644419"],"abstract_inverted_index":{"New":[0],"challenges":[1],"in":[2,109],"remote":[3],"sensing":[4],"impose":[5],"the":[6,25,32,35,44,76,106,110,118],"necessity":[7],"of":[8,24,34,38,97,117],"designing":[9],"pixel":[10],"classification":[11],"methods":[12],"that,":[13],"once":[14],"trained":[15],"on":[16,128],"a":[17,51,64,94],"certain":[18],"dataset,":[19],"generalize":[20],"to":[21,49,62,79],"other":[22,80],"areas":[23],"earth.":[26],"This":[27],"may":[28],"include":[29],"regions":[30],"where":[31],"appearance":[33],"same":[36],"type":[37],"objects":[39],"is":[40,47],"significantly":[41],"different.":[42],"In":[43,82],"literature":[45],"it":[46,56],"common":[48],"use":[50],"single":[52],"image":[53,89],"and":[54,59,66],"split":[55],"into":[57],"training":[58,119],"test":[60,111],"sets":[61],"train":[63],"classifier":[65],"assess":[67],"its":[68],"performance,":[69],"respectively.":[70],"However,":[71],"this":[72,83],"does":[73],"not":[74],"prove":[75],"generalization":[77],"capabilities":[78],"inputs.":[81],"paper,":[84],"we":[85],"propose":[86],"an":[87],"aerial":[88],"labeling":[90],"dataset":[91],"that":[92],"covers":[93],"wide":[95],"range":[96],"urban":[98],"settlement":[99],"appearances,":[100],"from":[101,115],"different":[102,114],"geographic":[103],"locations.":[104],"Moreover,":[105],"cities":[107],"included":[108],"set":[112],"are":[113],"those":[116],"set.":[120],"We":[121],"also":[122],"experiment":[123],"with":[124],"convolutional":[125],"neural":[126],"networks":[127],"our":[129],"dataset.":[130]},"counts_by_year":[{"year":2026,"cited_by_count":19},{"year":2025,"cited_by_count":107},{"year":2024,"cited_by_count":106},{"year":2023,"cited_by_count":116},{"year":2022,"cited_by_count":124},{"year":2021,"cited_by_count":116},{"year":2020,"cited_by_count":101},{"year":2019,"cited_by_count":75},{"year":2018,"cited_by_count":34},{"year":2017,"cited_by_count":2}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
