Advancing Disaster Management by Integrating Geospatial Data and AI Methods

Natural disasters, such as wildfires, hurricanes, floods, earthquakes, and winter storms, pose significant threats to people and society. Effective disaster management is critical for mitigating the negative impacts of disasters and increasing community resilience. The availability of various geospatial data (e.g., satellite images, weather and climate data, social media data, and mobile phone location data) and artificial intelligence (AI) methods provides opportunities for advancing disaster management and understanding human-environment interactions under disaster contexts. In this talk, I will share some recent work from my group that integrates geospatial data and AI, along with GIS and statistical methods, for advancing disaster management and informing policies.

Dr. Yingjie Hu is an Associate Professor in the Department of Geography at the University at Buffalo. He is also an Adjunct Professor in the Department of Computer Science and Engineering and an Affiliate Faculty in the UB Center for Geological and Climate Hazards and the AI and Data Science Institute. His research integrates geospatial data, GIS, and AI methods to understand human-environment interactions under disaster contexts and to address disaster-related challenges. He is the author of over 80 peer-reviewed articles in top journals and conferences.

He and his work have been recognized by awards such as the UB Exceptional Scholar Award and the Emerging Scholar Award by the American Association of Geographers. His research has received over $1.5 million in funding support from agencies such as the NSF and NASA, and has also been covered by major news media such as CNN, NPR, and Reuters. He is passionate about doing research, training the next generation's scholars, and serving communities.