{"id":"https://openalex.org/W2903097915","doi":"https://doi.org/10.1109/icpr.2018.8546004","title":"Image-based Air Pollution Estimation Using Hybrid Convolutional Neural Network","display_name":"Image-based Air Pollution Estimation Using Hybrid Convolutional Neural Network","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2903097915","doi":"https://doi.org/10.1109/icpr.2018.8546004","mag":"2903097915"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2018.8546004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","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/A5034675932","display_name":"Jian Ma","orcid":"https://orcid.org/0000-0002-9269-1130"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jian Ma","raw_affiliation_strings":["Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100377578","display_name":"Kun Li","orcid":"https://orcid.org/0000-0003-2326-0166"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Li","raw_affiliation_strings":["Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031819155","display_name":"Yahong Han","orcid":"https://orcid.org/0000-0003-2768-1398"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yahong Han","raw_affiliation_strings":["Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063288559","display_name":"Jingyu Yang","orcid":"https://orcid.org/0000-0002-7521-7920"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu Yang","raw_affiliation_strings":["School of Electrical and Information Engineering, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5034675932"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.913,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.73181721,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"471","last_page":"476"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9958999752998352,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9905999898910522,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.719609797000885},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6605909466743469},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5573103427886963},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5522027611732483},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.445600688457489},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.42043066024780273},{"id":"https://openalex.org/keywords/air-pollution","display_name":"Air pollution","score":0.41616925597190857},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3815343379974365},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3480166792869568},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3470919728279114},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3438827097415924},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09287187457084656}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.719609797000885},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6605909466743469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5573103427886963},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5522027611732483},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.445600688457489},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.42043066024780273},{"id":"https://openalex.org/C559116025","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollution","level":2,"score":0.41616925597190857},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3815343379974365},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3480166792869568},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3470919728279114},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3438827097415924},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09287187457084656},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2018.8546004","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2018.8546004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 24th International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1990592195","https://openalex.org/W2017356321","https://openalex.org/W2155893237","https://openalex.org/W2156380531","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2363704663","https://openalex.org/W2523989733","https://openalex.org/W2791942352","https://openalex.org/W2885605478","https://openalex.org/W2963446712","https://openalex.org/W2964121744","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6647720530","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W4312417841","https://openalex.org/W4210874298","https://openalex.org/W2778653980"],"abstract_inverted_index":{"Air":[0],"pollution":[1,19,35,159],"has":[2],"a":[3,26,45,51,62,100,162],"serious":[4],"impact":[5],"on":[6,141,155],"our":[7,150],"daily":[8],"life,":[9],"and":[10,14,97,144],"how":[11],"to":[12,58,103,130],"quickly":[13],"easily":[15],"measure":[16],"the":[17,59,68,87,90,105,132,135],"air":[18,34,158],"level":[20],"without":[21],"any":[22],"expensive":[23],"equipment":[24],"is":[25,56,95],"quite":[27],"challenging":[28],"task.":[29],"This":[30,79],"paper":[31],"proposes":[32],"an":[33],"estimation":[36],"method":[37,151],"using":[38],"deep":[39,64],"hybrid":[40],"convolutional":[41],"neural":[42],"network":[43,82,102,133],"from":[44,161],"single":[46,163],"image,":[47],"e.g.,":[48],"captured":[49,54,146,164],"by":[50,76,84],"smartphone.":[52],"The":[53],"image":[55],"input":[57],"main":[60],"network,":[61,65],"very":[63],"which":[66],"solves":[67],"side":[69],"effects":[70],"of":[71,89,115,121,157],"increased":[72],"depth":[73,88],"(degradation":[74],"issues)":[75],"skip":[77],"connection.":[78],"can":[80],"improve":[81],"performance":[83,154],"simply":[85],"increasing":[86],"network.":[91],"Dark":[92],"channel":[93],"map":[94],"computed":[96],"fed":[98],"into":[99],"secondary":[101],"enrich":[104],"features":[106],"with":[107,118],"implicit":[108],"representation.":[109],"We":[110],"have":[111],"collected":[112],"1575":[113],"images":[114],"different":[116,119],"scenes":[117],"values":[120],"PM":[122],"<sub":[123,127],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[124,128],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub>":[125],".":[126],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">5</sub>":[129],"train":[131],"in":[134],"end-to-end":[136],"fusion":[137],"mode.":[138],"Experimental":[139],"results":[140],"synthetic":[142],"dataset":[143,147],"real":[145],"demonstrate":[148],"that":[149],"achieves":[152],"excellent":[153],"classification":[156],"levels":[160],"image.":[165]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2}],"updated_date":"2026-01-23T23:20:30.427331","created_date":"2025-10-10T00:00:00"}