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

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. A morphological map of Marth Vallis, Mars, has been created, classifying variations in surface texture into 14 descriptive classes.A deep learning (DL) convolutional neural network was trained to predict these classes in further HiRISE images, which had not been used for training.The resulting classified rasters were orthorectified and mosaicked using ArcGIS.Appropriate classes from the resulting map were compared with manual digitisation of the spatial densities of Transverse Aeolian Ridges (TARs).This comparison highlights the different scales at which human and DL mapping takes place, and that the two datasets have different strengths and weaknesses.The speed at which the network can complete its task allows it to attempt a higher level of fidelity than is possible for a human.Derived maps of the density of boulders and TARs were also produced using both the DL and manual datasets. A morphological map of Marth Vallis, Mars, has been created, classifying variations in surface texture into 14 descriptive classes. A deep learning (DL) convolutional neural network was trained to predict these classes in further HiRISE images, which had not been used for training. The resulting classified rasters were orthorectified and mosaicked using ArcGIS. Appropriate classes from the resulting map were compared with manual digitisation of the spatial densities of Transverse Aeolian Ridges (TARs). This comparison highlights the different scales at which human and DL mapping takes place, and that the two datasets have different strengths and weaknesses. The speed at which the network can complete its task allows it to attempt a higher level of fidelity than is possible for a human. Derived maps of the density of boulders and TARs were also produced using both the DL and manual datasets.