Mawrth Vallis, Mars, classified using the NOAH-H deep-learning terrain classification system

Barrett, Alexander M.; Fawdon, Peter; Favaro, Elena A.; Balme, Matthew R.; Wright, Jack; Woods, Mark J.; Karachalios, Spyros; Bohacek, Eleni; Gerdes, Levin; Sefton-Nash, Elliot and Joudrier, Luc (2023). Mawrth Vallis, Mars, classified using the NOAH-H deep-learning terrain classification system. Journal of Maps, 19(1), article no. 2285480.

DOI: https://doi.org/10.1080/17445647.2023.2285480

Abstract

A deep learning (DL) terrain classification system, the Novelty and Anomaly Hunter – HiRISE (NOAH-H) was used to produce a terrain map of Mawrth Vallis, Mars. With it, we digitised the extent and distribution of transverse aeolian ridges (TARs), a common type of martian aeolian bedform. We present maps of the site, classifying terrain into descriptive classes and interpretive groups. TAR density maps are calculated, and the network output is compared to a manually produced map of TAR density, highlighting the differences in approach and results between these methods. Even when mapping on a small scale, humans must divide the terrain into coherent patches in order to map a large area in a reasonable time frame. Conversely, the speed of DL systems enables mapping on the pixel scale, producing a more detailed product, but one which is also “noisier”, and less immediately informative. There are pros and cons to both approaches.