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Commercial mountaineering has gained widespread popularity in recent decades. Global mountaineering challenges – e.g., the Seven Summits challenge to climb the highest summit of each continent – amplify this process, and also raise the profile of individual destinations. The highest volcano on the Earth, the Ojos del Salado in the Dry Andes (Chile/Argentina) is featured in two of the major challenges (Seven Second Summits, Volcanic Seven Summits). Thus, it is a prime extreme outdoor tourism destination. The relative ease of access and the non-technical nature of the ascent have also contributed to the increasing volume of tourism. However, our observations about commercial mountaineering practices reveal surprisingly low success rates on the summit. Based on data from our decade-long environmental monitoring programme and our field experiences,, we attribute this to the extreme environment and landscape of the mountain (e.g., cold and dry climate, strong winds, topographical situation, loose surface material), scarce mountaineering facilities, and potential misjudgements by inexperienced climbers.
Commercial mountaineering has gained widespread popularity in recent decades. Global mountaineering challenges – e.g., the Seven Summits challenge to climb the highest summit of each continent – amplify this process, and also raise the profile of individual destinations. The highest volcano on the Earth, the Ojos del Salado in the Dry Andes (Chile/Argentina) is featured in two of the major challenges (Seven Second Summits, Volcanic Seven Summits). Thus, it is a prime extreme outdoor tourism destination. The relative ease of access and the non-technical nature of the ascent have also contributed to the increasing volume of tourism. However, our observations about commercial mountaineering practices reveal surprisingly low success rates on the summit. Based on data from our decade-long environmental monitoring programme and our field experiences,, we attribute this to the extreme environment and landscape of the mountain (e.g., cold and dry climate, strong winds, topographical situation, loose surface material), scarce mountaineering facilities, and potential misjudgements by inexperienced climbers.
Data analysis methods have scarcely kept pace with the rapid increase in Earth observations, spurring the development of novel algorithms, storage methods, and computational techniques. For scientists interested in Mars, the problem is always the same: there is simultaneously never enough of the right data and an overwhelming amount of data in total. Finding sufficient data needles in a haystack to test a hypothesis requires hours of manual data screening, and more needles and hay are added constantly. To date, the vast majority of Martian research has been focused on either one-off local/regional studies or on hugely time-consuming manual global studies. Machine learning in its numerous forms can be helpful for future such work. Machine learning has the potential to help map and classify a large variety of both features and properties on the surface of Mars and to aid in the planning and execution of future missions. Here, we outline the current extent of machine learning as applied to Mars, summarize why machine learning should be an important tool for planetary geomorphology in particular, and suggest numerous research avenues and funding priorities for future efforts. We conclude that: (1) moving toward methods that require less human input (i.e., self- or semi-supervised) is an important paradigm shift for Martian applications, (2) new robust methods using generative adversarial networks to generate synthetic high-resolution digital terrain models represent an exciting new avenue for Martian geomorphologists, (3) more effort and money must be directed toward developing standardized datasets and benchmark tests, and (4) the community needs a large-scale, generalized, and programmatically accessible geographic information system (GIS).
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