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The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov\u2010accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel\u20102 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model\u2010based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer\u2010based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision\u2009=\u20090.961, recall\u2009=\u20090.984, negative predictive value\u2009=\u20090.985, and F1 score\u2009=\u20090.972. Therefore, the model developed in this study can be a helpful tool for decision\u2010makers in the task of urban land\u2010use planning and management.<\/jats:p>","DOI":"10.1155\/2021\/8820116","type":"journal-article","created":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T09:31:55Z","timestamp":1613554315000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced 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