Artificial Intelligence and Remote Sensing Technologies in Modern Geoscientific Research: A Multidisciplinary Sustainable Review

Abstract

The integration of Remote Sensing (RS) and Artificial Intelligence (AI) has revolutionized modern geoscientific research by enabling large-scale, data-driven, and sustainable analysis of Earth systems. RS platforms, including satellite and aerial sensors such as Landsat-8, Sentinel-2, ASTER, LiDAR, and SAR, provide spatially and temporally extensive datasets that capture lithology, structural features, aquifer potential, surface deformation, and land-use/land-cover dynamics. AI techniques, encompassing machine learning (ML) and deep learning (DL), facilitate automated data processing, feature extraction, predictive modeling, and anomaly detection, significantly enhancing accuracy, efficiency, and scalability compared to traditional geoscience methods. This study evaluates multidisciplinary applications of RS and AI, including geological mapping, mineral exploration, hydrogeology, geomorphology, environmental monitoring, and hazard assessment. Methodologies involve multi-source dataset acquisition, preprocessing, AI-driven classification and regression, integration with Geographic Information Systems (GIS), and field-based model validation to ensure reliability. Accuracy assessment, comparative sensor analysis, and ground-truth verification are employed to quantify the performance of different RS techniques. Expected outcomes include automated workflows, predictive maps, and decision-support tools that inform sustainable resource management, climate-resilient planning, and disaster mitigation. The study underscores how the RS-AI synergy addresses limitations of conventional approaches, minimizing environmental impact while providing actionable insights for policymakers, researchers, and stakeholders. Findings demonstrate that RS, when combined with AI and field validation, offers a robust framework for enhancing geoscientific investigations, optimizing resource use, and supporting sustainable Earth system management. This research provides a template for future geoscientific applications in regions experiencing environmental stress, resource depletion, and climatic variability, highlighting the transformative potential of advanced technologies in multidisciplinary, sustainable geoscience research.