Oxia Planum, Mars, classified using the NOAH-H deep-learning terrain classification system

We present a map of Oxia Planum, Mars, the landing site for the ExoMars Rover. This shows surface texture and aeolian bedform distribution, classified using a deep learning (DL) system. A hierarchical classification scheme was developed, categorising the surface textures observed at the site. This was then used to train a DL network, the 'Novelty or Anomaly Hunter - HiRISE' (NOAH-H). The DL applied the classification scheme across a wider area than could have been mapped manually. The result showed strong agreement with human-mapped areas reserved for validation. The resulting product is presented in two ways, representing the two principle levels of the classification scheme. 'Descriptive classes' are purely textural in nature, making them compatible with a machine learning approach. These are then combined into 'interpretive groups', broader thematic classes, which provide an interpretation of the landscape. This step allows for a more intuitive analysis of the results by human users.

Automated summary

This study presents a DL-based map classification system for the landing site on Mars. By training a DL network with the developed hierarchical scheme, the system achieved high agreement with manually mapped areas. The resulting map is presented in both descriptive classes and interpretive groups, allowing for intuitive analysis by human users.