Elsevier Fundamentals and Advances in Remote Sensing: From Principles to AI Applications

Fundamentals and Advances in Remote Sensing: From Principles to AI Applications is an essential scientific reference for researchers, students, and professionals in geospatial sciences. It addresses the need to combine traditional remote sensing principles with modern AI-driven methods. Covering sensor technologies, image calibration, classification, semantic segmentation, InSAR, and data fusion, the book offers both theoretical insights and practical workflows. It features open-source code, real-world case studies, and visual aids like flow diagrams and comparison tables to enhance understanding. The structured chapters facilitate easy navigation and application, empowering users to deploy advanced techniques confidently in environmental monitoring, urban planning, and hazard assessment. By bridging fundamental concepts with cutting-edge tools, this volume supports innovative research and operational solutions to pressing societal and environmental challenges, making it a vital resource for advancing remote sensing science and applications. Offers open-source workflows using Python and Google Earth Engine for practical remote sensing applications Demonstrates real-world case studies across diverse sensors including optical, SAR, and UAV data Utilizes a modular, template-based chapter structure to enhance learning and cross-referencing Bridges traditional remote sensing techniques with cutting-edge AI-driven methods like semantic segmentation and InSAR Integrates visual elements such as flow diagrams, step-by-step boxes, comparison tables, and high-resolution figures for clarity