Enhanced digital outcrop models attributed with hyperspectral reflectance data, or hyperclouds, provide a flexible, three-dimensional medium for data-driven mapping of geological exposures, mine faces or cliffs. This approach facilitates the collection of spatially contiguous information on exposed mineralogy, and so helps to quantify mineralising processes, interpret 1-D drillhole data, and optimise mineral extraction. In this contribution we present an open-source python workflow, hylite, for creating hyperclouds by seamlessly fusing geometric information with data from a variety of hyperspectral imaging sensors and applying necessary atmospheric and illumination corrections. These rich datasets can be analysed using a variety of techniques, including minimum wavelength mapping and spectral indices, to accurately map geological objects from a distance. Reference spectra from spectral libraries, ground or laboratory measurements can also be included to derive supervised classifications using machine learning techniques. We demonstrate the potential of the hypercloud approach by integrating hyperspectral data from laboratory, tripod and unmanned aerial vehicle acquisitions to automatically map relevant lithologies and alterations associated with volcanic hosted massive sulphide (VHMS) mineralisation in the Corta Atalaya open-pit, Spain. These analyses allow quantitative and objective mineral mapping at the outcrop and open-pit scale, facilitating quantitative research and smart-mining approaches. Our results highlight the seamless sensor integration made possible with hylite and the power of data-driven mapping methods applied to hyperclouds. Significantly, we also show that random forests (RF) trained only on laboratory data from labelled hand-samples can be used to map appropriately corrected outcrop scale data.
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