CLASSIFICATION OF HAZE IN CITY IMAGES WITH CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING

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dc.contributor.authorIsikdag, U.
dc.contributor.authorApak, Sudi
dc.date.accessioned2025-03-26T17:35:08Z
dc.date.available2025-03-26T17:35:08Z
dc.date.issued2021
dc.departmentİstanbul Esenyurt Üniversitesi, Fakülteler, İşletme ve Yönetim Bilimleri Fakültesi, Ekonomi ve Finans Bölümü
dc.description.abstractAir quality has an enormous impact on health. To take preventive measures on time, it is important to track and estimate air pollution. In the estimation of air pollution, the data acquisition from images is easy and of low-cost, when compared with sensor-based data acquisition. Machine and Deep Learning methods utilise images and videos from city cameras or social media and provide accurate estimations of air pollution. In this context, the aim of this study was testing the accuracy and efficiency of Deep Learning and Convolutional Neural Networks (CNNs) in differentiating between fog and polluted air (smog) in city images through transfer learning. The results demonstrated that Convolutional Neural Networks (CNNs) and Transfer Learning can be used as efficient methods for fog/smog classification.
dc.identifier.endpage1385
dc.identifier.issn1311-5065
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85115936264
dc.identifier.scopusqualityQ3
dc.identifier.startpage1379
dc.identifier.urihttps://hdl.handle.net/20.500.14704/1036
dc.identifier.volume22
dc.identifier.wosWOS:000720305600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherScibulcom Ltd
dc.relation.ispartofJournal of Environmental Protection and Ecology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250326
dc.subjectfog; smog; air quality; transfer learning; convolutional neural networks
dc.titleCLASSIFICATION OF HAZE IN CITY IMAGES WITH CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING
dc.typeArticle

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