Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Remote sensing and satellite imagery have become very important sources of data for monitoring Earth’s surface, environment, agriculture, urban growth, and disaster events. However, the large volume, high dimensionality, and complexity of satellite data makes manual analysis very difficult. Machine Learning (ML) methods provides automatic and efficient techniques to extract meaningful patterns and information from such data. This review paper discusses how different ML techniques are applied in remote sensing and satellite data processing. Traditional algorithms such as k-Nearest Neighbors, Support Vector Machines, and Random Forest are compared with modern deep learning approaches like Convolutional Neural Networks and Recurrent Neural Networks. The paper also covers applications in land use classification, crop monitoring, disaster management, weather forecasting, and environmental analysis. Challenges such as data heterogeneity, limited labeled data, and computational issues are also discussed. The aim of this paper is to present a comprehensive overview for researchers who are working in this emerging interdisciplinary domain.

Keywords: Remote Sensing, Satellite Imagery, Machine Learning, Deep Learning, Land Use Classification, Environmental Monitoring