AI Applications in Satellite Image Processing: Enhancing Earth Observation and Environmental Monitoring

Abstract

Satellite image processing is a cornerstone of modern Earth observation, providing critical data for meteorology, resource management, and urban studies. The integration of artificial intelligence (AI) has significantly enhanced image analysis through machine learning, deep learning, and data fusion techniques, improving efficiency and accuracy. This study examines AI-driven transformations in satellite image processing, focusing on applications in disaster monitoring, urban growth assessment, hyperspectral vegetation analysis, and air quality evaluation. We also explore AI algorithms such as supervised and unsupervised learning models, convolutional neural networks (CNNs), and data fusion techniques, which facilitate complex image interpretation. Additionally, challenges in AI integration with satellite systems are discussed, along with future research directions to advance Earth observation and environmental monitoring. Overall, this review highlights AI’s indispensable role in satellite imagery for global decision-making.