Project Grant 2509680
- This Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (CFDA 47.084) program aims to enhance disaster response and recovery through improved capabilities of machine learning and autonomous systems for rapid damage assessment. The $458,784 award to Lehigh University will develop a smart technology that leverages big data, autonomous systems, and physics-informed models to address complex problems of hazard projection and real-time...
- The National Science Foundation (NSF) awarded a $1,500,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of North Texas Research and Innovation Division. The grant funding aims to develop a comprehensive framework to streamline diverse processes and integrate advanced technological solutions into disaster relief operations, enhancing the overall effectiveness and speed of emergency response efforts. The key objectives are to...
- The National Science Foundation (NSF) awarded a $190,402 Project Grant under the Engineering program (CFDA 47.041) to The Trustees of the Stevens Institute of Technology in Hoboken, New Jersey. This collaborative research project develops a novel adaptive data collection framework to enable reliable data collection under severe time and resource constraints for automated post-disaster rapid damage assessment. The system leverages a Bayesian probabilistic modeling approach to dynamically...
- This Project Grant award from the National Science Foundation's Engineering program (CFDA 47.041) provides $100,000 to support the planning and development of a new shared-use, community-scale testbed for research on climate adaptation and disaster resilience. The testbed will serve as a cyber-physical environment representative of real-world community systems, enabling policy design, model integration, and theory development related to climate change impacts and natural hazards. The planning...
- This $144,887 federal Project Grant was awarded by the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (CFDA 47.084) program. The grant supports an interdisciplinary research project at the University of Texas at Austin to develop a dynamic sensemaking framework for understanding decision-making processes during rapid-onset disasters like catastrophic flash flooding events. Key components of the project include: Capturing ephemeral data through...
- This $165,000 Project Grant award from the National Science Foundation (NSF) Engineering (CFDA 47.041) program supports the development of a novel adaptive data collection framework for automated post-disaster rapid damage assessment. The project at Arizona State University's Orspa division will create a Bayesian-based system that analyzes recent observations to dynamically update the trajectories of data collector agents toward areas with the greatest potential for information gain. This will...
- This $383,591 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) supports the SCC-CIVIC-FA Track B: Community-Centric Pre-Disaster Mitigation with Unmanned Aerial and Marine Systems project. The Texas A&M Engineering Experiment Station will utilize unmanned aerial and marine systems to develop community-centric pre-disaster mitigation strategies with a sub-award provided to Texas A & M University at Galveston for...
- This $399,933 Project Grant awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) to the University of California, Los Angeles (UCLA) will support the development of new artificial intelligence-based infrastructure damage prediction models for enhanced resilience and emergency response planning. The 3-year project aims to advance the state-of-the-art in multimodal data integration and analytics to enable near real-time assessments of physical damage to...
- This $999,664 project grant awarded by the National Science Foundation (NSF) under the Computer and Information Science and Engineering program (CFDA 47.070) supports the development of a novel framework for extracting spatio-temporal features from integrated remote sensing data to create a comprehensive knowledge base for monitoring and mapping flood events. The research aims to enable rapid response and recovery efforts by providing real-time communication between human and robot agents...
- This $400,000 Project Grant was awarded by the National Science Foundation (NSF) under the Social, Behavioral, and Economic Sciences (CFDA 47.075) program. The grant supports a 3-year research project that examines the impacts of natural disasters on community resilience and spatial disparities. The project has three key components: 1) analyzing disruptions to resident mobility patterns using mobile phone data, 2) investigating spatial variation in requests for government assistance, and 3)...
This Project Grant award, valued at $200,000.00 and issued by the National Science Foundation (NSF) under the CFDA program 47.084 "NSF Technology, Innovation, and Partnerships", will fund the development of TRACE (Testbed for Disaster Resilience Auditing and Crisis Evaluation). TRACE is an innovative cross-cutting platform that integrates social cyber-physical systems, internet of things, robotics, and AI/ML technologies. The goal is to assist multidisciplinary search and rescue teams in accelerating mission-critical response and recovery operations in post-disaster scenarios caused by natural hazards like fires, earthquakes, floods, hurricanes, and tornadoes. The project, to be executed by the University of Maryland Baltimore County, aims to build a scalable, multidimensional, and resilient AI-ready testbed to assess the effects of these disasters on the built environment, infrastructure, and communities. By combining TRACE with scalable AI/ML algorithms, the project will support community-level disaster resilience through characterization of risks and vulnerabilities, anticipation of failures and losses, and data-driven planning and decision-making.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $200.0k | 8/5/25 |