ORCIBS Seminar -Saman Ghaffarian

Recent advancements in technology have significantly improved the field of disaster risk management (DRM), with a particular emphasis on the development and use of artificial intelligence (AI) and remote sensing-based methods. Remote sensing has emerged as a versatile and powerful tool for collecting data from a distance, employing various sensors and platforms such as satellites and drones. It enables the acquisition of high-resolution imagery, 3D data, and other geospatial information, providing valuable insights for DRM. At the same time, AI techniques, including advanced machine learning algorithms, have made significant progress in analysing and interpreting extensive datasets, including remote sensing data. The integration of AI and remote sensing has greatly enhanced DRM practices. By leveraging remote sensing data, AI algorithms facilitate swift identification and evaluation of affected areas for post-disaster damage assessments. These technologies also enable monitoring of recovery progress and the identification of vulnerable regions that are prone to future disasters. Additionally, AI-based tools have improved the efficiency of disaster early warning systems, evacuation modelling, and planning by enabling effective and rapid hazard forecasting and exposure mapping. In this talk, I will present my previous and ongoing studies on the exclusive and collaborative use of advanced AI tools and methods, such as deep learning, explainable AI, and digital twins, in conjunction with remote sensing for various aspects of DRM. The talk will demonstrate how AI, remote sensing, and their integration have enhanced these areas of study, providing accurate and timely information for effective decision-making in DRM.