Three-dimensional, km-scale hyperspectral data of a well-exposed Zn-Pb mineral exploration target at Black Angel Mountain, Greenland

DOI

Hyperspectral imaging is a most promising innovative technology for non-invasive material mapping and is starting to be adapted in a wide range of applications, including geosciences, ag-riculture, and food quality control. Novel processing workflows have revolutionized the way we can correct, interpret, and integrate hyperspectral data in the past decade. The reprojection of planar hyperspectral scans to real 3D point cloud representations (“hyperclouds”) has opened up new possibilities for the mapping of large and topographically complex targets. So far, only a few tools have been developed to process and visualize this kind of data. In this contribution we pre-sent an open-source hypercloud dataset capturing complex but spectacularly well exposed geolo-gy from the Black Angel Mountain in Maarmorilik, West Greenland, alongside a detailed and interactive tutorial documenting the workflow that was used to create it.

Identifier
DOI https://doi.org/10.14278/rodare.1643
Related Identifier https://www.hzdr.de/publications/Publ-34711
Related Identifier https://www.hzdr.de/publications/Publ-34996
Related Identifier https://doi.org/10.14278/rodare.1642
Related Identifier https://rodare.hzdr.de/communities/rodare
Metadata Access https://rodare.hzdr.de/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:rodare.hzdr.de:1643
Provenance
Creator Lorenz, Sandra ORCID logo; Thiele, Samuel Thomas ORCID logo; Kirsch, Moritz ORCID logo; Unger, Gabriel; Zimmermann, Robert ORCID logo; Guarnieri, Pierpaolo; Baker, Nigel; Vest Sørensen, Erik; Rosa, Diogo; Gloaguen, Richard (ORCID: 0000-0002-4383-473X)
Publisher Rodare
Publication Year 2022
Rights Creative Commons Attribution 4.0 International; Open Access; https://creativecommons.org/licenses/by/4.0/legalcode; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://rodare.hzdr.de/support
Representation
Language English
Resource Type Dataset
Discipline Life Sciences; Natural Sciences; Engineering Sciences