{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T16:34:47Z","timestamp":1774888487041,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,13]],"date-time":"2022-11-13T00:00:00Z","timestamp":1668297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFB2103403"],"award-info":[{"award-number":["2020YFB2103403"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["42271397"],"award-info":[{"award-number":["42271397"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022NGCM05"],"award-info":[{"award-number":["2022NGCM05"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2020YFB2103403"],"award-info":[{"award-number":["2020YFB2103403"]}]},{"name":"National Natural Science Foundation of China","award":["42271397"],"award-info":[{"award-number":["42271397"]}]},{"name":"National Natural Science Foundation of China","award":["2022NGCM05"],"award-info":[{"award-number":["2022NGCM05"]}]},{"name":"Open Fund of Key Laboratory of National Geographical Census and Monitoring, Min-istry of Natural Resources","award":["2020YFB2103403"],"award-info":[{"award-number":["2020YFB2103403"]}]},{"name":"Open Fund of Key Laboratory of National Geographical Census and Monitoring, Min-istry of Natural Resources","award":["42271397"],"award-info":[{"award-number":["42271397"]}]},{"name":"Open Fund of Key Laboratory of National Geographical Census and Monitoring, Min-istry of Natural Resources","award":["2022NGCM05"],"award-info":[{"award-number":["2022NGCM05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Air quality has a significant influence on the environment and health. Instruments that efficiently and inexpensively detect air quality could be extremely valuable in detecting air quality indices. This study presents a robust deep learning model named AQE-Net, for estimating air quality from mobile images. The algorithm extracts features and patterns from scene photographs collected by the camera device and then classifies the images according to air quality index (AQI) levels. Additionally, an air quality dataset (KARACHI-AQI) of high-quality outdoor images was constructed to enable the model\u2019s training and assessment of performance. The sample data were collected from an air quality monitoring station in Karachi City, Pakistan, comprising 1001 hourly datasets, including photographs, PM2.5 levels, and the AQI. This study compares and examines traditional machine learning algorithms, e.g., a support vector machine (SVM), and deep learning models, such as VGG16, InceptionV3, and AQE-Net on the KHI-AQI dataset. The experimental findings demonstrate that, compared to other models, AQE-Net achieved more accurate categorization findings for air quality. AQE-Net achieved 70.1% accuracy, while SVM, VGG16, and InceptionV3 achieved 56.2% and 59.2% accuracy, respectively. In addition, MSE, MAE, and MAPE values were calculated for our model (1.278, 0.542, 0.310), which indicates the remarkable efficacy of our approach. The suggested method shows promise as a fast and accurate way to estimate and classify pollutants from only captured photographs. This flexible and scalable method of assessment has the potential to fill in significant gaps in the air quality data gathered from costly devices around the world.<\/jats:p>","DOI":"10.3390\/rs14225732","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T04:24:10Z","timestamp":1668399850000},"page":"5732","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["AQE-Net: A Deep Learning Model for Estimating Air Quality of Karachi City from Mobile Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Maqsood","family":"Ahmed","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yonglin","family":"Shen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Mansoor","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Economics and Management, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zemin","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ping","family":"Cheng","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Nafees","family":"Ali","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8920-5034","authenticated-orcid":false,"given":"Abdul","family":"Ghaffar","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8718-1881","authenticated-orcid":false,"given":"Sabir","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Systems Engineering, Quaid-E-Awam University of Engineering, Science & Technology, Nawabshah 67230, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.scitotenv.2011.09.019","article-title":"An Aggregate AQI: Comparing Different Standardizations and Introducing a Variability Index","volume":"420","author":"Ruggieri","year":"2012","journal-title":"Sci. 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