AI World Models for Real-Time Disaster Risk Assessment Using Satellite Imagery and Infrastructure Data

Abstract: Natural disasters present a growing challenge to societies worldwide, exacerbated by climate change and rapid urbanization. Traditional disaster risk assessment methods often rely on delayed reporting and static data. This research explores the integration of AI-based world models that utilize satellite imagery and infrastructure data to enable real-time disaster risk assessment. By leveraging deep learning, geospatial analysis, and predictive modeling, the proposed framework can detect patterns, forecast hazard trajectories, and prioritize response efforts. The paper highlights how these AI models enhance situational awareness, improve resilience planning, and offer scalable applications in flood, earthquake, wildfire, and landslide scenarios. Case studies, algorithmic approaches, and system architecture are presented to demonstrate the feasibility and transformative potential of AI-driven risk intelligence systems. Keywords: Artificial Intelligence (AI), World Models, Real-Time Disaster Risk Assessment, Satellite Imagery, Infrastructure Data, Geospatial Artificial Intelligence (GeoAI), Remote Sensing, Machine Learning, Deep Learning, Disaster Prediction, Damage Assessment, Early Warning Systems, Multimodal Data Fusion, Earth Observation, Critical Infrastructure Monitoring.