Advanced Information Systems to Fill NASA Surface Topography and Vegetation (STV) Mission Gaps: Next-Generation Stereo+Lidar Fusion and Sensor Technology

This NASA technology/science crossover project focuses on the following components for the NASA Surface Topography and Vegetation (STV) Mission under the Decadal Survey Incubation (DSI) program:

• Develop novel information systems and on-board algorithms that can deliver the precise pointing knowledge needed for next-generation lidar and TDI linescan image sensors. These subsystems will solve the notorious “jitter” problem responsible for large residual geolocation uncertainty in commercial stereo images.

• Develop information systems that use cutting-edge, multi-sensor deep learning fusion techniques to improve the horizontal resolution, vertical accuracy/precision, and quality of stereo+lidar datasets for priority STV targets (vegetation, ice/snow).

• Develop stereo photogrammetry information systems with robust joint optimization routines, rigorous uncertainty metrics, and stereo+lidar fusion alignment to support next generation stereo imaging rigs and constellations Leverage state-of-the-art radiative transfer model simulations (DART) and existing/new on-orbit and airborne datasets to support development activities and evaluate key stereo acquisition parameters, which will complement STV OSSE efforts to define STV instrument requirements.

For more information, please visit

• https://esto.nasa.gov/project-selections-for-dsi-21/#Shean
• https://science.nasa.gov/earth-science/decadal-stv