Learn how CPU-GPU parallelization is used for high-throughput 3D surface point cloud generation from Earth-observing satellites. Stereo photogrammetry, used in computer vision applications, analyzes the parallax between image pairs to estimate depth. However, extending this workflow to satellite imagery presents computational challenges; notably, near-continuous streams of gigapixel-sized images. We leverage multicore and multiple Tesla K80 GPUs to assemble a fully automated pipeline capable of rapidly processing large image streams. Initial timings demonstrated an 89x (~10x over OpenMP, multicore scaling) performance improvement over its publicly available version. We'll share lessons learned in extending stereo reconstruction algorithms into satellite imaging, at scale.