Advanced InSAR–UAV-LiDAR Flood-Deformation Risk Monitoring for Efficient Mobility
El Paso’s critical transportation corridors face compounding risks from ground deformation and flash flooding that can severely disrupt efficient mobility, impede traffic flow, and challenge infrastructure reliability. Such infrastructure disruptions compromise public safety by delaying emergency response access and increase collision risk on compromised roadways. Despite advances in satellite monitoring and hydrologic modeling, no integrated system currently provides transportation agencies with rapid and actionable, near-real-time alerts for combined flood-deformation hazards. This project is designed to support uninterrupted mobility directly by developing and demonstrating a unified monitoring framework that fuses millimeter-precision Interferometric Synthetic Aperture Radar (InSAR) deformation maps with Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR) terrain models and Synthetic Aperture Radar (SAR)-derived soil-moisture indices to deliver actionable risk assessments. The research addresses a core challenge in maintaining efficient mobility: predicting when and where infrastructure vulnerabilities will coincide with flood conditions. Using validated Persistent Scatterer (PS) and Small Baseline Subset (SBAS) InSAR processing chains, high-resolution UAV-LiDAR surveys, and machine learning algorithms trained on historical events, the proposed system will provide transportation agencies with advanced warning, which enables proactive response and traffic management. The project will produce a composite flood-deformation risk index with demonstrated 90% accuracy in hazard detection. An edge-computing prototype will be deployed in partnership with the Texas Department of Transportation (TxDOT) to operationalize the fusion algorithms, enabling 24-hour processing turnaround and secure web-based risk visualization. Through formal partnerships with TxDOT and El Paso Water, the system will integrate real-time flow gauge data and infrastructure databases to enhance model calibration and validation. The project includes comprehensive technology transfer components, such as Docker-containerized software, training workshops for state Department of Transportation (DOT) engineers, and a commercialization brief outlining licensing pathways for rapid deployment across additional corridors.
Language
- English
Project
- Status: Active
- Funding: $100,500.00
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Contract Numbers:
69A3552348329
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
1111 Rellis Parkway
Bryan, Texas United States 77807 -
Project Managers:
Ocon, Monica
- Performing Organizations: El Paso, TX United States
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Principal Investigators:
Kim, Youg Ye
- Start Date: 20260215
- Expected Completion Date: 20270715
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
- Source Data: 03-10-UTEP
Subject/Index Terms
- TRT Terms: Deformation; Drones; Floods; Interferometers; Laser radar; Machine learning; Monitoring; Risk assessment
- Geographic Terms: El Paso (Texas)
- Subject Areas: Data and Information Technology; Hydraulics and Hydrology; Planning and Forecasting; Security and Emergencies; Transportation (General);
Filing Info
- Accession Number: 01979475
- Record Type: Research project
- Source Agency: Center for Advancing Research in Transportation Emissions, Energy, and Health (CARTEEH)
- Contract Numbers: 69A3552348329
- Files: UTC, RIP
- Created Date: Feb 15 2026 4:40PM