Abstract
Up-to-date information regarding impervious surface is valuable for urban planning and management. 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-accelerated 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-2 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-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based 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 = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management.
Highlights
Urban impervious surface, developed by anthropogenic activities, is one of the most crucial land cover forms. e impenetrable surface areas consist of buildings, roads, parking lots, sidewalks, pavements, and many others. ese surfaces prevent the absorption of water into the soil
Up-to-date information regarding impervious surface is of paramount importance for supporting urban land management/planning, detection of unplanned built-up areas, study of regional land-use pattern, and ecosystem monitoring [5, 7,8,9,10]
To train and verify the neural computing model used for impervious surface area detection, the extracted dataset has been divided into two sets of training (70%) and testing (30%) datasets
Summary
Urban impervious surface, developed by anthropogenic activities, is one of the most crucial land cover forms. e impenetrable surface areas consist of buildings, roads, parking lots, sidewalks, pavements, and many others. ese surfaces prevent the absorption of water into the soil. Erefore, they have been identified as a key indicator used in evaluating urbanization influences on surrounding natural environment and ecosystem [6]. Due to such reasons, up-to-date information regarding impervious surface is of paramount importance for supporting urban land management/planning, detection of unplanned built-up areas, study of regional land-use pattern, and ecosystem monitoring [5, 7,8,9,10]. In developing countries including Vietnam, the conventional approach for obtaining such information is field survey. This approach is very time-consuming and requires considerable effort in data collection, processing, and storing. Remote sensing technology and processing of satellite images have been increasingly applied to tackle various challenging problems in a wide span of domains including agriculture [11,12,13], natural hazard prevention [14,15,16,17], civil engineering [18,19,20], and environmental engineering [21,22,23]
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