  We demonstrate high fidelity enhancement of planetary digital elevation
models (DEMs) using optical images and deep learning with convolutional neural
networks. Enhancement can be applied recursively to the limit of available
optical data, representing a 90x resolution improvement in global Mars DEMs.
Deep learning-based photoclinometry robustly recovers features obscured by
non-ideal lighting conditions. Method can be automated at global scale.
Analysis shows enhanced DEM slope errors are comparable with high resolution
maps using conventional, labor intensive methods.
