Deep Neural Strategies for Uncovering Climbing Elements and Mapping Route Patterns

Abstract

This work presents a new deep learning architecture specifically designed to extract and structure climbing features in indoor settings. By building on two neural models, one pair wise similarity and one triplet comparison, the approach is learned to discriminate between climbing holds belonging to the same route and those that do not. In contrast with more conventional color clustering methods, our method is superior in accuracy and robustness, albeit with the requirements of complex manual annotation and higher computational demands. The findings emphasize the promise of deep neural networks to transform automated route mapping within climbing environments.