ARTIFICIAL INTELLIGENCE AND REMOTE SENSING IN DISASTER MANAGEMENT

This book, Artificial Intelligence and Remote Sensing in Disaster Management, provides a comprehensive and interdisciplinary exploration of how advanced digital technologies are transforming the way disasters are understood, monitored, predicted, and managed. In an era marked by increasing disaster frequency and intensity due to climate change, rapid urbanization, and environmental degradation, there is a growing need for intelligent, data-driven approaches that can support proactive risk reduction and resilient development. This book responds to that need by systematically integrating concepts from disaster management, artificial intelligence, remote sensing, and geospatial science.The primary objective of the book is to present both theoretical foundations and practical applications of AI and remote sensing across the entire disaster management cycle—mitigation, preparedness, response, and recovery. Rather than treating these technologies in isolation, the book emphasizes their combined use in developing intelligent disaster management systems capable of handling large volumes of heterogeneous data, supporting real-time decision-making, and improving the accuracy of hazard assessment and damage evaluation.The content is structured in a progressive and learner-friendly manner. The initial chapters introduce fundamental concepts of disaster management, including disaster types, management frameworks, and global and national policies. This is followed by detailed coverage of artificial intelligence fundamentals, learning paradigms, architectures, workflows, and ethical considerations. The principles of remote sensing, electromagnetic energy interactions, sensor types, resolutions, and observation platforms are explained in a clear and systematic way, ensuring that readers from diverse academic backgrounds can develop a strong conceptual foundation.The book provides in-depth discussions on AI-based disaster prediction, early warning systems, hazard-specific applications, and post-disaster damage assessment. Major disaster types such as floods, earthquakes, landslides, cyclones, droughts, wildfires, coastal and marine hazards, and urban disasters are addressed through dedicated chapters, supported by real-world examples and case studies from India and other regions. The final chapters explore future research directions, including explainable AI, real-time analytics, autonomous response systems, and climate-driven disaster risk modeling. This book is designed to serve multiple audiences. It is suitable as a core or reference textbook for undergraduate and postgraduate courses in disaster management, civil and environmental engineering, geoinformatics, remote sensing, artificial intelligence, and urban planning. Researchers will find it useful for identifying methodological frameworks, research gaps, and emerging opportunities. Practitioners, planners, and policymakers can use the book as a practical guide for understanding technology-enabled disaster risk reduction and for designing data-driven strategies aligned with national and international frameworks.Overall, this book aims to bridge the gap between theory and practice and to contribute meaningfully to the development of resilient, informed, and technology-enabled disaster management systems.