{"id":"https://openalex.org/W2727957049","doi":"https://doi.org/10.1109/lgrs.2017.2719863","title":"Deep Learning-Based Large-Scale Automatic Satellite Crosswalk Classification","display_name":"Deep Learning-Based Large-Scale Automatic Satellite Crosswalk Classification","publication_year":2017,"publication_date":"2017-07-13","ids":{"openalex":"https://openalex.org/W2727957049","doi":"https://doi.org/10.1109/lgrs.2017.2719863","mag":"2727957049"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2017.2719863","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2017.2719863","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1706.09302","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5084789423","display_name":"Rodrigo F. Berriel","orcid":"https://orcid.org/0000-0002-6701-893X"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Rodrigo F. Berriel","raw_affiliation_strings":["Universidade Federal do Espirito Santo, Vit\u00f3ria, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-6701-893X","affiliations":[{"raw_affiliation_string":"Universidade Federal do Espirito Santo, Vit\u00f3ria, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038003603","display_name":"Andr\u00e9 T. Lopes","orcid":"https://orcid.org/0000-0001-9920-5556"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Andre Teixeira Lopes","raw_affiliation_strings":["Universidade Federal do Espirito Santo, Vit\u00f3ria, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal do Espirito Santo, Vit\u00f3ria, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005735333","display_name":"Alberto F. De Souza","orcid":"https://orcid.org/0000-0003-1561-8447"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Alberto F. de Souza","raw_affiliation_strings":["Universidade Federal do Espirito Santo, Vit\u00f3ria, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal do Espirito Santo, Vit\u00f3ria, Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037893575","display_name":"Thiago Oliveira-Santos","orcid":"https://orcid.org/0000-0001-7607-635X"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Thiago Oliveira-Santos","raw_affiliation_strings":["Universidade Federal do Espirito Santo, Vit\u00f3ria, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidade Federal do Espirito Santo, Vit\u00f3ria, Brazil","institution_ids":["https://openalex.org/I51235708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.9802,"has_fulltext":false,"cited_by_count":58,"citation_normalized_percentile":{"value":0.96990498,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"14","issue":"9","first_page":"1513","last_page":"1517"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9987999796867371,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9950000047683716,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9941999912261963,"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/schema-crosswalk","display_name":"Schema crosswalk","score":0.9040671586990356},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7798079252243042},{"id":"https://openalex.org/keywords/crowdsourcing","display_name":"Crowdsourcing","score":0.7611877918243408},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.5816524028778076},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.552994966506958},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5333953499794006},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5256215929985046},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5052803158760071},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4982788562774658},{"id":"https://openalex.org/keywords/satellite-imagery","display_name":"Satellite imagery","score":0.44281837344169617},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.43958547711372375},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42926323413848877},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3613733649253845},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33783966302871704},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.11637118458747864},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09460040926933289}],"concepts":[{"id":"https://openalex.org/C121193887","wikidata":"https://www.wikidata.org/wiki/Q7431117","display_name":"Schema crosswalk","level":3,"score":0.9040671586990356},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7798079252243042},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.7611877918243408},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.5816524028778076},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.552994966506958},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5333953499794006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5256215929985046},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5052803158760071},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4982788562774658},{"id":"https://openalex.org/C2778102629","wikidata":"https://www.wikidata.org/wiki/Q725252","display_name":"Satellite imagery","level":2,"score":0.44281837344169617},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.43958547711372375},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42926323413848877},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3613733649253845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33783966302871704},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.11637118458747864},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09460040926933289},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lgrs.2017.2719863","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2017.2719863","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1706.09302","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1706.09302","pdf_url":"https://arxiv.org/pdf/1706.09302","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1706.09302","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1706.09302","pdf_url":"https://arxiv.org/pdf/1706.09302","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G3574331637","display_name":null,"funder_award_id":"311120/2016-4","funder_id":"https://openalex.org/F4320322025","funder_display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico"}],"funders":[{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322025","display_name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","ror":"https://ror.org/03swz6y49"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1981799318","https://openalex.org/W2047112088","https://openalex.org/W2087950276","https://openalex.org/W2097117768","https://openalex.org/W2106129972","https://openalex.org/W2117539524","https://openalex.org/W2154579312","https://openalex.org/W2163605009","https://openalex.org/W2400551023","https://openalex.org/W2505558069","https://openalex.org/W2526513285","https://openalex.org/W6682751323","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W3048841968","https://openalex.org/W4292121402","https://openalex.org/W2357606590","https://openalex.org/W1964467305","https://openalex.org/W2809634635","https://openalex.org/W2303498155","https://openalex.org/W3013578941","https://openalex.org/W2588274242","https://openalex.org/W2004705050","https://openalex.org/W2168382779"],"abstract_inverted_index":{"High-resolution":[0],"satellite":[1,60,82],"imagery":[2,61],"has":[3,32],"been":[4,33],"increasingly":[5],"used":[6,72,111,126],"on":[7,35,137],"remote":[8],"sensing":[9],"classification":[10,39,136],"problems.":[11],"One":[12],"of":[13,20,23,57],"the":[14,18,26,36,52,113],"main":[15],"factors":[16],"is":[17,71],"availability":[19],"this":[21,42,68],"kind":[22],"data.":[24],"Despite":[25],"high":[27],"availability,":[28],"very":[29],"little":[30],"effort":[31],"placed":[34],"zebra":[37,89],"crossing":[38],"problem.":[40],"In":[41],"letter,":[43],"crowdsourcing":[44,122],"systems":[45],"are":[46],"exploited":[47],"in":[48,77,112],"order":[49,78],"to":[50,73,79,127,133],"enable":[51],"automatic":[53],"acquisition":[54],"and":[55,105],"annotation":[56],"a":[58,138],"large-scale":[59],"database":[62],"for":[63],"crosswalks":[64],"related":[65],"tasks.":[66],"Then,":[67],"data":[69,93,123],"set":[70,94],"train":[74,130],"deep-learning-based":[75],"models":[76,132],"accurately":[80,128],"classify":[81],"images":[83,99],"that":[84,119],"contain":[85,88],"or":[86],"not":[87],"crossings.":[90],"A":[91],"novel":[92],"with":[95],"more":[96,106],"than":[97,107],"240000":[98],"from":[100],"3":[101],"continents,":[102],"9":[103],"countries,":[104],"20":[108],"cities":[109],"was":[110],"experiments.":[114],"The":[115],"experimental":[116],"results":[117],"showed":[118],"freely":[120],"available":[121],"can":[124],"be":[125],"(97.11%)":[129],"robust":[131],"perform":[134],"crosswalk":[135],"global":[139],"scale.":[140]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
