Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland

• Published in: Data
• Main Authors: Sandra Lorenz, Sam T. Thiele, Moritz Kirsch, Gabriel Unger, Robert Zimmermann, Pierpaolo Guarnieri, Nigel Baker, Erik Vest Sørensen, Diogo Rosa, Richard Gloaguen
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: open-source dataset; hyperspectral data; spectral imaging; 3D hyperclouds; photogrammetry; mineral mapping; Bibliography. Library science. Information resources; Z; Greenland; Maarmorilik
• Online Access: https://doi.org/10.3390/data7080104; https://doaj.org/article/bc56703443924433aceee81619432080

Hyperspectral imaging is an innovative technology for non-invasive mapping, with increasing applications in many sectors. As with any novel technology, robust processing workflows are required to ensure a wide use. We present an open-source hypercloud dataset capturing the complex but spectacularly well exposed geology from the Black Angel Mountain in Maarmorilik, West Greenland, alongside a detailed and interactive tutorial documenting relevant processing workflows. This contribution relies on very recent progress made on the correction, interpretation and integration of hyperspectral data in earth sciences. The possibility to fuse hyperspectral scans with 3D point cloud representations (hyperclouds) has opened up new possibilities for the mapping of complex natural targets. Spectroscopic and machine learning tools allow or the rapid and accurate characterization of geological structures in a 3D environment. Potential users can use this exemplary dataset and the associated tools to train themselves or test new algorithms. As the data and the tools have a wide range of application, we expect this contribution to benefit the scientific community at large.