{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T09:43:00Z","timestamp":1775209380560,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T00:00:00Z","timestamp":1608249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\/MES","award":["UIDB\/EEA\/50008\/2020"],"award-info":[{"award-number":["UIDB\/EEA\/50008\/2020"]}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["UIDB\/00742\/2020"],"award-info":[{"award-number":["UIDB\/00742\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems\u2019 potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output\u2014future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution\u2014which is an inherently much more difficult problem.<\/jats:p>","DOI":"10.3390\/rs12244142","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T01:01:08Z","timestamp":1608512468000},"page":"4142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":102,"title":["Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1620-9007","authenticated-orcid":false,"given":"Jovan","family":"Kalajdjieski","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Rugjer Boshkovik 16, 1000 Skopje, North Macedonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7664-0168","authenticated-orcid":false,"given":"Eftim","family":"Zdravevski","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Rugjer Boshkovik 16, 1000 Skopje, North Macedonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8366-6059","authenticated-orcid":false,"given":"Roberto","family":"Corizzo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, American University, Washington, DC 20016, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5336-1796","authenticated-orcid":false,"given":"Petre","family":"Lameski","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Rugjer Boshkovik 16, 1000 Skopje, North Macedonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3373-8637","authenticated-orcid":false,"given":"Slobodan","family":"Kalajdziski","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Rugjer Boshkovik 16, 1000 Skopje, North Macedonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3394-6762","authenticated-orcid":false,"given":"Ivan Miguel","family":"Pires","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade da Beira Interior, 6201001 Covilh\u00e3, Portugal"},{"name":"Computer Science Department, Polytechnic Institute of Viseu, 3504510 Viseu, Portugal"},{"name":"UICISA:E Research Centre, School of Health, Polytechnic Institute of Viseu, 3504510 Viseu, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3195-3168","authenticated-orcid":false,"given":"Nuno M.","family":"Garcia","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es, Universidade da Beira Interior, 6201001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8103-8059","authenticated-orcid":false,"given":"Vladimir","family":"Trajkovik","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, Ss. 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