SCC-CIVIC-PG Track B: Community-Centric Pre-Disaster Mitigation with Unmanned Aerial and Marine Systems

This project will establish a community-centric emergency management consortium to pilot a program intended to increase community resilience to chronic major disasters: flooding, wildfires, hurricanes, tornadoes, and tropical storms. One key to improving resilience is to document and understand the current conditions through comprehensive environmental assessments and continuous surveillance prior to a disaster.

While small unmanned aerial and marine robots combined with advances in artificial intelligence (AI) and geo-spatial information systems (GIS) offer great promise for these assessments, agencies do not have sufficient personnel to cover vulnerable areas. Texas A&M will lead the pilot program where the Texas Department of Emergency Management and Texas Forestry Service will determine the pre-disaster mitigation needs for three communities with different vulnerabilities, demographics, and land use: Bastrop, Houston, Galveston.

At-risk high school students in these communities will be trained to use unmanned systems with AI and GIS tools to collect and process the data, then integrate the results back into the emergency management framework. Thus the resilience of the communities will be improved while filling the STEM pipeline, increasing economic competitiveness, and creating career paths for a skilled emergency response workforce that is savvy with innovative and emerging technologies.

This project will use open source datasets to further improve and automate pre-disaster mitigation assessments. The labeled longitudinal aerial and underwater datasets will enable fundamental research and new advances in CV/ML as well as disaster science. The project will establish the trustworthiness of CV/ML develop new algorithms for recognition of vulnerabilities during different seasons and weather conditions, and further fundamental understanding of transfer learning from one disaster to another.

The data on the frequency of surveying with aerial and marine assets will lead to an informatics-based model of sampling that captures the tradeoffs between accuracy, resolution, and frequency on identifying objects and scene understanding. This project is in response to the Civic Innovation Challenge program, Track B—Resilience to Natural Disasters—and is a collaboration between NSF and the Department of Homeland Security.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.