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This study advocates for the innovative use of advanced deep learning and remote sensing technologies in monitoring urban dynamics to enhance our understanding of the environmental implications of urb...anization and formulate strategies for sustainable land management. Leveraging the potential of these technologies contributes to the realization of sustainable development goals (SDGs), nurturing the growth of sustainable and resilient cities for the future. Focused on mapping land cover and change detection in the new extensions of Greater Cairo, Egypt, the study employs Sentinel-2 imagery and convolutional neural networks (CNNs). The CNN model, trained on the BigEarthNet dataset with transfer learning using a pre-trained U-Net model, reveals significant land cover changes, particularly in Greater Cairo's eastern region due to the construction of the New Administrative Capital. These findings contribute to the advancement of urban monitoring and its applications in fostering sustainable development.続きを見る
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