Mars-DL: Demonstrating feasibility of a simulation-based training approach for autonomous Planetary science target selection

Planetary robotic missions contain vision instruments for various mission-related and science tasks such as 2D and 3D mapping, geologic characterization, atmospheric investigations, or spectroscopy for exobiology. One major application for computer vision is the characterization of scientific context, and the identification of scientific targets of interest (regions, objects, phenomena) for being investigated by other scientific instruments. Due to high variability of appearance of such potentially scientific targets it requires well-adapted yet flexible techniques, one of them being Deep Learning. Machine learning and in particular Deep Learning (DL) is a technique used in computer vision to recognize content in images, categorize it and find objects of specific semantics. In its default workflow, DL requires large sets of training data with known / manually annotated objects, regions or semantic content. Within Mars-DL (Planetary Scientific Target Detection via Deep Learning), training focuses on a simulation approach, by virtual placement of known targets in a true context environment.