InSAR-based deformation-risk analysis for power transmission infrastructure on the U.S. Gulf Coast
Coastal subsidence and sea-level rise threaten the electrical grid of one of the country's most critical energy hubs. This project quantifies that threat by combining satellite-measured ground deformation with environmental hazards to score the risk to individual transmission towers and substations along the Gulf Coast.
It ingests NASA/JPL OPERA Sentinel-1 surface-displacement products, converts them to georeferenced velocity and coherence fields, samples the deformation in the immediate neighborhood of each piece of infrastructure, and fuses those signals with sea-level-rise projections and storm-surge inundation to produce actionable, per-asset risk classifications and maps for grid-resilience planning.
- End-to-end InSAR pipeline — downloads raw OPERA displacement (HDF5), processes it into georeferenced GeoTIFF velocity/coherence, and extracts per-site time series.
- Asset-level sampling — analyzes a 3×3-pixel neighborhood around each tower and substation to capture localized vertical land motion.
- Multi-hazard fusion — combines displacement velocity, coherence, storm-surge inundation, and sea-level-rise projections into a single risk view.
- Risk-classification engine — synthesizes the hazard indicators (including PCA) into ranked risk levels.
- Batch processing — parallel, county-level runs with step control and resumption.
- Deliverables — risk maps, statistical reports, and time-series CSV/Excel outputs with quality metrics.
Developed at the University of Houston, Department of Civil and Environmental Engineering as part of the Milillo Lab. The canonical lab repository, including the full collaboration history, lives at Milillo-lab/Transmission_Lines.
Python · InSAR (OPERA / Sentinel-1) · rasterio / GDAL · geospatial analysis · PCA / machine-learning risk scoring.