Geospatial AI and data analytics for satellite-based disaster prediction and risk assessment

Abstract

The integration of Geospatial Artificial Intelligence (GeoAI) and data analytics with satellite technology offers transformative potential in disaster prediction and risk assessment. This paper explores the role of GeoAI in analyzing diverse geospatial datasets, such as optical, radar, and thermal satellite imagery, to predict and monitor disasters, including floods, wildfires, earthquakes, and landslides. Key applications of GeoAI include early warning systems, real-time hazard detection, and long-term resilience planning, enabling proactive decision-making and resource optimization. The paper also examines the benefits of predictive capabilities in minimizing disaster impacts, enhancing disaster preparedness, and reducing vulnerabilities. Furthermore, it addresses the challenges of handling complex geospatial data, ethical considerations, and the need for inclusive and transparent GeoAI frameworks. Recommendations for improving GeoAI approaches, such as enhancing data integration, advancing algorithms, and fostering public engagement, are provided. The findings underscore GeoAI's critical role in building disaster-resilient societies and highlight the need for continued innovation, collaboration, and ethical practices in its deployment.