Loading…
Loading grant details…
| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Texas A&M Engineering Experiment Station |
| Country | United States |
| Start Date | Feb 01, 2024 |
| End Date | Jan 31, 2026 |
| Duration | 730 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2403646 |
The broader impact/commercial potential of this I-Corps project is the development of a data-driven, analytics-based technology for automated flood risk mapping and rapid damage assessment. Flood hazards are the most prominent stressors for communities in the U.S. and across the world, causing dire physical, social, and economic hardships. Currently, city managers, planners, infrastructure owners and operators, emergency managers, and local and state public agencies are missing critical insights and foresights needed to mitigate, prepare for, respond to, and recover from flood hazards.
This innovation may form the basis of data products and analytics solutions to inform various flood resilience plans and actions at city, regional, and state levels. In addition, this technology may transform the ability of decision-makers, emergency managers, and flood managers to tailor their strategies and technologies to enhance intelligent resilience in dealing with flood hazards.
This I-Corps project is based on the development of an artificial intelligence (AI)-driven software system to reliably and accurately estimate spatial flood risk and property damage caused by flooding in a rapid and automated way. The technology augments the flood analytics capabilities of local/public agencies to enhance flood risk mapping, expedite flood claim processing, and enhance resource allocation.
In addition, the system is designed to automatically map the lowest flood elevation of properties from street view imagery and provide high-resolution and reliable flood risk maps based on examining various hydrological, land use, and built environment features and their interactions. This technology may improve the reliability and availability of flood risk mapping and also improve the speed and accuracy of damage assessment, enabling local and public agencies, emergency managers, flood managers, and insurance companies to make more informed decisions in their flood resilience efforts.
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.
Texas A&M Engineering Experiment Station
Complete our application form to express your interest and we'll guide you through the process.
Apply for This Grant