(untitled) (Prakriti - The International Multidisciplinary Research Journal, 2025)
The rapid growth of urbanization and environmental stress has intensified the need for precise, scalable, and timely impact assessment tools. Remote sensing (RS) imagery, with its ability to provide spatial and temporal data across large extents, has become central to monitoring land surface changes. In recent years, the integration of machine learning (ML) techniques with remote sensing has transformed how environmental and urban impacts are assessed, offering more accurate classifications, predictive capabilities, and dynamic monitoring. This review explores the current landscape of ML-based methodologies applied to RS imagery in the context of environmental degradation, urban expansion, land use/ land cover (LULC) change, vegetation health, and disaster impact evaluation. It provides a comparative assessment of commonly used algorithms such as Support Vector Machines, Random Forest, Artificial Neural Networks, and emerging deep learning models like Convolutional Neural Networks. The study examines how these methods are applied across diverse datasets—Landsat, Sentinel, UAV imagery and highlights their performance in detecting subtle and complex landscape changes. The review also identifies promising trends, including explainable AI, integration with GIS-based spatial analytics, and real-time processing via cloud platforms.