In recent decades, significant advances have improved the modelling techniques for predicting slope failure. Predicting failures is vital in open pit mining due to the significant risk posed to personnel and equipment from unstable pit walls. Failures not only cause harm and damage to people and equipment but also cause delays to production and subsequently increase operational costs. Coupled with improvements in modelling techniques, utilisation of advanced scanning systems, such as high-definition imagery from UAVs has enabled faster and more precise mapping and modelling. However, one key element required for accurate slope stability modelling and monitoring is the ability for mine geotechnical engineers to have an accurate understanding of the mineralogy and moisture content of slopes, in real-time. To address this challenge, research has been undertaken to utilise hyperspectral imaging across a broad spectrum, combined with high-definition imagery, and LiDAR to scan slopes for minerals and moisture content. In addition to accurately mapping mineralogy and moisture, the data must be collected autonomously and remotely to ensure ongoing safety. The results show that there are many direct and indirect benefits from this new technology, including the ability to monitor slopes at a safe distance and in real-time, improved accuracy of modelling, detection of risks, and improving the automation workflow for geotechnical engineers. The paper presents recommendations for the implementation of the technology in open cut mining environments.