Geospatial AI for Disaster Risk Intelligence and Monitoring

The convergence of artificial intelligence (AI) and geospatial technologies has shifted how organizations monitor, assess, and manage environmental and societal risks. Natural disasters, climate change, pandemics, and human-induced hazards are increasing in frequency and intensity, causing unprecedented economic losses. Traditional risk management approaches, reliant on reactive measures and limited data processing capabilities, are often inadequate when addressing the complexity, scale, and velocity of current risks. Geospatial AI (GeoAI) represents a transformative advancement in risk management, enabling real-time hazard detection, predictive risk modelling, automated damage assessment, and intelligent decision support systems. Further research may bridge the gap between the technical aspects of GeoAI and practical applications in risk management across multiple hazard types and geographical contexts. Geospatial AI for Disaster Risk Intelligence and Monitoring examines GeoAI applications across the disaster risk management continuum. It explores cutting-edge methodologies including deep learning for hazard mapping, explainable AI for risk communication, edge computing for real-time monitoring, and digital twins for urban resilience. Covering topics such as early warning systems, environmental monitoring, and urban flood risk assessment, this book is a valuable resource for engineers, policymakers, disaster response personnel, researchers, and environmental scientists.