Revolutionizing Satellite Data Analysis: Deep Learning- Based Semantic Segmentation for Urban Planning and Environmental Monitoring

The rapid advancement in satellite imaging has enabled new possibilities for urban planning and environmental monitoring. This paper proposes a deep learning-based approach for semantic segmentation of high-resolution satellite imagery, aimed at automating the process of land cover classification. The primary objective is to efficiently distinguish between various land features such as buildings, roads, and water bodies, which are crucial for urban development and environmental monitoring. By addressing the challenges of manual annotation and pixel-based classification, this model leverages state-of-the-art deep learning architectures to process large-scale satellite images with high accuracy. The proposed solution enhances decision-making processes for urban planners and environmental scientists, offering real-time, scalable data analysis that supports sustainable urban growth and disaster management. The system’s flexibility and precision, combined with its ability to adapt to diverse geographic areas, make it an invaluable tool for urban planning, environmental conservation, and rapid response to emergencies.