Organiser:
Biography: Ari Aharari received his M.E. degree in Industrial Science and Technology from Niigata University in 2004 and his Ph.D. degree in Robotics from the Kyushu Institute of Technology in 2007. He joined Sojo University, Japan, in 2012 and is currently a Professor in the Department of Computer and Information Sciences. He has served as principal investigator for more than 40 research projects conducted in collaboration with over 50 Japanese companies, local government laboratories, and universities. His research activities have resulted in more than 120 publications in international journals and conference proceedings. His research interests encompass the Internet of Things, artificial intelligence, service robotics, disaster-prevention engineering, smart agriculture, and the practical deployment of intelligent technologies in industry and society. He is a Senior Member of the IEEE and a member of the IEEE Robotics and Automation Society. In addition to his academic work, he is the founder and a board member of FusionTech Inc., Japan, and serves as Chief Technology Officer and a board member of Bosai Tech Inc., Japan, where he contributes to the development and implementation of AI- and IoT-based solutions for industry, disaster resilience, and sustainable communities.
Introduction:
Artificial intelligence offers significant opportunities to build sustainable societies that are inclusive, resilient, and accessible to all. However, realizing these opportunities requires approaches that integrate environmental sustainability, social equity, public safety, and disaster resilience throughout the full disaster-management cycle. This special session will provide an interdisciplinary forum for presenting innovative AI methods and practical applications that advance a universal sustainable society while addressing challenges before, during, and after disasters.
The session will examine how machine learning, generative AI, digital twins, robotics, remote sensing, and data-driven decision-support systems can improve risk assessment, disaster prediction, early warning, preparedness, emergency response, damage evaluation, recovery, and resilient reconstruction. Particular attention will be given to vulnerable populations, accessibility, regional and socioeconomic disparities, ethical data use, trustworthy AI, and solutions suitable for resource-constrained communities.
By bringing together researchers, engineers, policymakers, disaster-management professionals, and community stakeholders, the session will connect advances in AI with the Sustainable Development Goals and real-world resilience needs. It will encourage technically rigorous and socially responsible contributions, including methodological studies, applied research, field demonstrations, datasets, evaluation frameworks, and lessons from actual disasters. The session is needed to establish a dedicated bridge between AI innovation, universal sustainability, and end-to-end disaster resilience—an interdisciplinary area not fully addressed by conventional AI tracks.
Topics:
Related topics for this special
session (but not limited to) :
• AI for sustainable, inclusive, and
resilient societies
• AI applications supporting the Sustainable
Development Goals
• Disaster-risk prediction, mapping, and
assessment
• Multi-hazard modeling and cascading-risk
analysis
• Climate-change adaptation and resilience
planning
• Pre-disaster prevention, mitigation, and
preparedness
• AI-based forecasting and early-warning
systems
• Remote sensing, satellite imagery, drones,
and geospatial AI
• Digital twins and simulations for
resilient cities and communities
• Smart infrastructure monitoring and
predictive maintenance
• Disaster-response planning and real-time
decision support
• Robotics and autonomous systems for
search, rescue, and relief
• Emergency communications and
crisis-information management
• Generative AI and large language models
for disaster management
• Post-disaster damage and needs assessment
• AI-assisted recovery, reconstruction, and
“build back better” strategies
• Sustainable energy, water, food,
transportation, and healthcare systems
• Humanitarian logistics and
resource-allocation optimization
• Community-centered and participatory AI
• Accessibility and assistance for
vulnerable populations
• Low-resource, rural, remote, and
developing-region applications
• Public health emergencies, epidemics, and
disaster medicine
• Trustworthy, explainable, ethical, safe,
and privacy-preserving AI
• Data quality, interoperability, open
datasets, and benchmarking
• Human–AI collaboration in high-risk
decision-making
• AI governance, policy, standards, and
international cooperation
• Field studies, pilot projects, and lessons
learned from real disasters