Geospatial AI for Disaster Risk Assessment and Resilience: Methods Applications

Addresses the growing complexity of 21st-century disasters through geospatial AI and intelligent technologies. It demonstrates how AI, machine learning, GIS, remote sensing, and IoT transform disaster risk reduction and resilience building, particularly in the Global South and India. It is a multidisciplinary approach to disaster studies. This book addresses the growing complexity of 21st-century disasters through geospatial AI and intelligent technologies. It demonstrates how AI, machine learning, GIS, remote sensing, and IoT transform disaster risk reduction and resilience building, particularly in the Global South and India. This multidisciplinary approach to disaster studies includes AI-driven damage assessment, machine learning for poverty mapping, deep learning flood classification, predictive modeling of economic impacts, coastal vulnerability assessment, earthquake resilience analysis, glacier monitoring, IoT landslide systems, and AI-enhanced logistics for disaster response. Features Explores how machine learning and artificial intelligence can support disaster resilience for communities. Examines climate change impacts on increasing disaster frequency and societal vulnerability worldwide. Presents IoT-LoRaWAN systems for real-time landslide monitoring in vulnerable mountainous regions. Explores AI-driven last-mile logistics for improving emergency response and supply-chain efficiency. Emphasizes the importance of building resilient communities, a critical factor for long-term sustainability. This book is for emergency management professionals, disaster risk reduction experts, climate change specialists, urban planners and resilience officers, government agencies, NGOs, researchers, academics, and graduate students involved with disaster studies.