Urban-E: Satellite-based Urban and Environmental Change Analysis in Egypt with Deep Learning

Abstract The detection of urban and environmental change using satellite imagery is crucial for urban planning, sustainable development, and environmental monitoring. Remote sensing and artificial intelligence breakthroughs have enabled the analysis of large-scale, high-resolution data to reveal long-term alterations. This study introduces Urban-E, a satellite-based system for monitoring urban growth and environmental changes in Egypt’s New Administrative Capital from 2016 to 2024. A dataset of 1,605 multi-temporal satellite images was methodically collected and preprocessed to improve their clarity utilizing techniques like denoising, histogram equalization, and edge detection. Deep learning is employed to classify satellite images into predefined geographic regions, enabling automated spatial localization of urban zones. Change analysis is then performed using image-based comparison techniques, including pixel-level differencing, texture analysis, segmentation, and NDVI-based vegetation assessment, to quantify urban expansion and environmental variation over time. In addition, geoJSON datasets from OpenStreetMap are integrated to analyze the spatial distribution of urban services such as schools, hospitals, and commercial facilities. The CNN-based regional classification achieved an accuracy of 93.3%, facilitating reliable region-specific change analysis. The results reveal significant urban expansion, infrastructure growth, and increased green coverage across multiple zones. The proposed framework demonstrates how deep learning–assisted regional classification combined with satellite-based analytical techniques can support evidence-based urban and environmental monitoring for sustainable city planning.