Deep learning for impervious surface segmentation from high resolution aerial imagery
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The rapid urbanization of landscapes presents significant environmental and ecological challenges, which necessitates the precise mapping of impervious surfaces. This study aims to leverage the capabilities of deep learning, specifically U-Net architectures, for the semantic segmentation of impervious surfaces from high-resolution aerial imagery. Our approach enhances the conventional U-Net architecture to improve its efficiency and accuracy in handling the spatial complexity of urban landscapes. We utilized a dataset comprising aerial images from urban and suburban areas within the city limits of Columbia, Missouri. The datasets are annotated for training, validation, and testing. The models evaluated include U-Net, Residual U-Net, Attention U-Net, Attention Residual U-Net, scSE U-Net, and scSE Residual U-Net. The models were trained and validated under different conditions, with performance metrics calculated for the overall testing datasets. Our results from the 2017 (leaf-on conditions) and 2019 (leaf-off conditions) test sets indicate improvements. The scSE Residual U-Net model achieved the highest IoU of 88.0 percent, precision of 93.6 percent, recall of 93.6 percent, F1 score of 93.6 percent, and pixel accuracy of 94.6 percent. These results underscore the effectiveness of advanced U-Net models in delineating impervious surfaces with high precision and highlight their potential in remote sensing applications for urban planning and environmental monitoring.
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M.A.