Utilization of satellite imagery and artificial intelligence for disaster management: Approaches and case studies

The advent of advanced satellite observations and the rapid evolution of Artificial Intelligence (AI) technologies have led to a fundamental shift in disaster management. These technologies enhance precise prediction, closer monitoring, and more efficient and effective responses to natural disasters. This study introduces AI-based satellite image analysis solutions throughout the disaster management cycle: prevention, preparedness, response, and recovery. Satellite imagery, captured through various channels, resolutions, and orbits, plays a crucial role throughout the entire disaster management cycle. This is because satellites have the advantage of capturing broad areas and can also image disaster regions that are inaccessible to humans due to secondary risks. We utilize high-resolution geostationary satellite imagery for real-time hazard monitoring and forecasting and synthetic Electro-Optical (EO) satellite imagery derived from Synthetic Aperture Radar (SAR) observations for monitoring flooded areas under cloudy conditions. EO satellite imagery is photographic-like images of Earth's surface using visible and infrared sensors, enabling detailed observation and analysis for applications such as mapping, surveillance, and disaster monitoring. And SAR gives high-resolution images using radar signals, capable of operating in all weather conditions and through cloud cover or darkness, making it ideal for monitoring and mapping. Additionally, AI-based damage assessment solutions facilitate the rapid detection and classification of building damage, enabling a quick response and reconstruction. Considering these technologies by government agencies, NGOs, and other stakeholders is essential, particularly in developing countries with limited surface observation capabilities and specialists. With these advanced technologies, AI-based disaster management solutions are expected to contribute significantly to the Early Warning for All initiative.