Deep Learning for Classifying Urban Land Cover: Utilizing Convolutional Neural Networks on the Amazon Cloud

For efficient urban planning and environmental management, accurate urban land cover categorization is essential. The present study investigates the use of Convolutional Neural Networks (CNNs), a type of deep learning technology, for the categorization of urban land cover using remote sensing data from the Urban Land Cover dataset. Using the scalability and high-performance computing capabilities of the AWS cloud platform, the CNN model is trained and deployed there. The superiority of the CNN method is shown through comparison with conventional machine learning techniques. With implications for resource management, environmental monituring, and urban planning, this research highlights the potential of CNNs and cloud-based deployment to advance urban land cover categorization.