Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland
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...
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fttriple:oai:gotriple.eu:oai:doaj.org/article:bc56703443924433aceee81619432080 2023-05-15T16:27:40+02:00 Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland Sandra Lorenz Sam T. Thiele Moritz Kirsch Gabriel Unger Robert Zimmermann Pierpaolo Guarnieri Nigel Baker Erik Vest Sørensen Diogo Rosa Richard Gloaguen 2022-07-01 https://doi.org/10.3390/data7080104 https://doaj.org/article/bc56703443924433aceee81619432080 en eng MDPI AG doi:10.3390/data7080104 2306-5729 https://doaj.org/article/bc56703443924433aceee81619432080 undefined Data, Vol 7, Iss 104, p 104 (2022) open-source dataset hyperspectral data spectral imaging 3D hyperclouds photogrammetry mineral mapping geo Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2022 fttriple https://doi.org/10.3390/data7080104 2023-01-22T19:27:34Z 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. Article in Journal/Newspaper Greenland Unknown Greenland Maarmorilik ENVELOPE(-51.283,-51.283,71.133,71.133) Data 7 8 104 |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
open-source dataset hyperspectral data spectral imaging 3D hyperclouds photogrammetry mineral mapping geo |
spellingShingle |
open-source dataset hyperspectral data spectral imaging 3D hyperclouds photogrammetry mineral mapping geo Sandra Lorenz Sam T. Thiele Moritz Kirsch Gabriel Unger Robert Zimmermann Pierpaolo Guarnieri Nigel Baker Erik Vest Sørensen Diogo Rosa Richard Gloaguen Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland |
topic_facet |
open-source dataset hyperspectral data spectral imaging 3D hyperclouds photogrammetry mineral mapping geo |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Sandra Lorenz Sam T. Thiele Moritz Kirsch Gabriel Unger Robert Zimmermann Pierpaolo Guarnieri Nigel Baker Erik Vest Sørensen Diogo Rosa Richard Gloaguen |
author_facet |
Sandra Lorenz Sam T. Thiele Moritz Kirsch Gabriel Unger Robert Zimmermann Pierpaolo Guarnieri Nigel Baker Erik Vest Sørensen Diogo Rosa Richard Gloaguen |
author_sort |
Sandra Lorenz |
title |
Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland |
title_short |
Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland |
title_full |
Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland |
title_fullStr |
Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland |
title_full_unstemmed |
Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland |
title_sort |
three-dimensional, km-scale hyperspectral data of well-exposed zn–pb mineralization at black angel mountain, greenland |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/data7080104 https://doaj.org/article/bc56703443924433aceee81619432080 |
long_lat |
ENVELOPE(-51.283,-51.283,71.133,71.133) |
geographic |
Greenland Maarmorilik |
geographic_facet |
Greenland Maarmorilik |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
Data, Vol 7, Iss 104, p 104 (2022) |
op_relation |
doi:10.3390/data7080104 2306-5729 https://doaj.org/article/bc56703443924433aceee81619432080 |
op_rights |
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op_doi |
https://doi.org/10.3390/data7080104 |
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Data |
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7 |
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104 |
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1766017123574874112 |