Simulating rover imagery to train deep learning systems for scientific target selection

The remarkable success of deep learning (DL) for object and pattern recognition suggests its application to autonomic target selection of future Martian rover missions, to support planetary scientists and also exploring robots in preselecting possibly interesting regions in imagery, increase the overall scientific discoveries, and to speed-up the strategic decision-making. Deep learning requires large amounts of training data to work reliably. Many different geologic features are to be detected and to be trained for in a DL-systems. Past and ongoing missions such as the Mars Science Laboratory (MSL) do neither provide the necessary volume of training data nor existing “ground truth”. Therefore, realistic simulations are required. The scheme and workflow of the Mars-DL simulation is depicted in Figure 1. Realistic simulations are based on accurate 3D reconstructions of the Martian surface, which are obtained by the photogrammetric processing pipeline PRoVIP [1]. The resulting 3D terrain models can be virtually viewed from different angles and hence can be used to obtain large volumes of training data.