NDT Webinar | Challenges in DRR: Is AI the Ultimate Solution?
the chair
6th Webinar on NDT
Challenges in DRR: Is AI the Ultimate Solution?
Artificial intelligence (AI) and remote sensing (RS) offer transformative potential for disaster risk reduction (DRR), yet their implementation faces significant hurdles. Key challenges include the scarcity of high-quality labeled datasets for training AI models, particularly for infrequent but high-impact disasters. Remote sensing data, while valuable, often suffers from inconsistencies due to cloud cover, sensor limitations, or low temporal resolution, which can affect real-time monitoring and emergency response.
In this webinar, Dr. David Daou will reflect on a central question: Is AI the ultimate solution for DRR? His presentation will explore the limitations of AI-driven predictive models, including issues with generalizability across diverse geographic and climatic conditions and the computational bottlenecks that can delay decision-making during fast-evolving disaster scenarios.
Further challenges include the technical complexity of integrating multi-modal RS data—such as thermal, radar, and hyperspectral imagery—requiring advanced data-fusion techniques. Ethical and governance concerns, including algorithmic bias and data privacy, also pose serious considerations. Finally, the lack of technical infrastructure and expertise in many high-risk regions limits the effective deployment of these advanced tools.
This session will highlight the need for collaborative efforts focused on enhancing data accessibility, developing adaptive AI systems, and strengthening local capacities—key steps toward bridging the gap between innovation and practical disaster resilience.
Date: 14 July 2025
Time: 9:00 am EDT | 3:00 pm CEST | 9:00 pm CST Asia
Webinar ID: 824 7503 5782
Webinar Secretariat: journal.webinar@mdpi.com
Meet the Event Chair
Meet Our Speakers
Dr. David Daou
Institute for Environment and Human Security, United Nations University, Bonn, Germany;
Dr. David Daou leads the AI and remote sensing team at UNU-EHS; he is the UNU-EHS AI representative and is a member of the scientific committee of UNU AI Global Network. His research interests include AI and climate risk modelling, the development of new algorithms, and the synergy of AI and remote sensing development applied to climate change, climate adaptation, and disasters risks. He has authored/co-authored over 50 research publication records and delivered numerous keynotes and invited lectures. He has worked with several space agencies, developed algorithms for the satellite EarthCARE ATLID lidar, and spent many years of his scientific career working on developing inversion methods for satellite and ground-based lidar. He particularly worked with AEROCAN, EARLINet, and CORALNet. Currently, at UNU-EHS, he is focusing more on serving the people by combining AI, remote sensing, and social media to understand and improve early-warning systems.
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