In this study, we present a prototype roadmap for integrating remotely sensed observations into decision-making frameworks. Using OPERA VLM products derived from Sentinel-1, we map VLM rates and uncertainties at ~30 m resolution across the Greater Houston-Galveston region.. Our analysis reveals widespread but spatially varying subsidence, with differing impacts on ASTs. We apply a metric to estimate that VLM from April 2016 to November 2023 is predominantly linear, allowing future extrapolation of VLM rates. Combining future sea-level rise (SLR) scenarios with VLM data, we estimate that by 2050, ASTs in the region will experience at least 26.1 cm of relative SLR, with 10 (14.9%) exposed to more than 60 cm. Finally, we illustrate the value of integrating hydrodynamic models with spatially varying relative SLR, finding that flooding hazards are substantially amplified during a simulated Hurricane Harvey–like event under future conditions. Overall, we demonstrate the importance of incorporating high-resolution VLM data into hazard assessments to support near- and long-term decision-making. Our approach provides actionable insights for enhancing resilience to subsidence, RSLR, and associated flooding risks in Greater Houston/Galveston and other vulnerable coastal areas. This dataset is not publicly accessible because: Non EPA generated data generated by NASA. It can be accessed through the following means: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. InSAR processing software is freely available on GitHub and archived on Zenodo. ISCE-2 is at https://github.com/isce-framework/isce2 and https://zenodo.org/record/8157051, FRInGE is at https://github.com/isce-framework/fringe/tree/main, and https://zenodo.org/record/8157065, and MintPy is at https://github.com/insarlab/MintPy and https://zenodo.org/record/7502839, GNSS data and MIDAS rates and uncertainties are available from the Nevada Geodetic Lab at http://geodesy.unr.edu. Sentinel-1 single look complex images (SLC) are available at the Alaska Satellite Facility Distributed Active Archive Center (https://asf.alaska.edu/). The rate and associated uncertainty map produced in this work are available in the supplementary files in both geotiff and kmz (Google Earth) format. The historical flood depth model prediction is available at https://datadryad.org/stash/dataset/doi:https://doi.org/10.7280/D1NX1W (H10.tif), and the future flood depth model prediction is available in the supplementary files in geotiff format. Format: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. InSAR processing software is freely available on GitHub and archived on Zenodo. ISCE-2 is at https://github.com/isce-framework/isce2 and https://zenodo.org/record/8157051, FRInGE is at https://github.com/isce-framework/fringe/tree/main, and https://zenodo.org/record/8157065, and MintPy is at https://github.com/insarlab/MintPy and https://zenodo.org/record/7502839, GNSS data and MIDAS rates and uncertainties are available from the Nevada Geodetic Lab at http://geodesy.unr.edu. Sentinel-1 single look complex images (SLC) are available at the Alaska Satellite Facility Distributed Active Archive Center (https://asf.alaska.edu/). The rate and associated uncertainty map produced in this work are available in the supplementary files in both geotiff and kmz (Google Earth) format. The historical flood depth model prediction is available at https://datadryad.org/stash/dataset/doi:https://doi.org/10.7280/D1NX1W (H10.tif), and the future flood depth model prediction is available in the supplementary files in geotiff format. This dataset is associated with the following publication: Buzzanga, B., M. Govorcin, F. Kremer, J.E. Schubert, D.P.S. Bekaert, B. Schaeffer, P. Milillio, A.J. Williams, B.F. Sanders, A.L. Handwerger, and S. Staniewicz. Satellite-based vertical land motion for infrastructure monitoring: a prototype roadmap in Greater Houston, Texas. NATURE. Nature Portfolio, Berlin, GERMANY, 17041, (2025).