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The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem
We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodive...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10043017/ https://www.ncbi.nlm.nih.gov/pubmed/36973316 http://dx.doi.org/10.1038/s41597-023-02010-8 |
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author | Milz, Stefan Wäldchen, Jana Abouee, Amin Ravichandran, Ashwanth A. Schall, Peter Hagen, Chris Borer, John Lewandowski, Benjamin Wittich, Hans-Christian Mäder, Patrick |
author_facet | Milz, Stefan Wäldchen, Jana Abouee, Amin Ravichandran, Ashwanth A. Schall, Peter Hagen, Chris Borer, John Lewandowski, Benjamin Wittich, Hans-Christian Mäder, Patrick |
author_sort | Milz, Stefan |
collection | PubMed |
description | We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation for robotics. |
format | Online Article Text |
id | pubmed-10043017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100430172023-03-29 The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem Milz, Stefan Wäldchen, Jana Abouee, Amin Ravichandran, Ashwanth A. Schall, Peter Hagen, Chris Borer, John Lewandowski, Benjamin Wittich, Hans-Christian Mäder, Patrick Sci Data Data Descriptor We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation for robotics. Nature Publishing Group UK 2023-03-27 /pmc/articles/PMC10043017/ /pubmed/36973316 http://dx.doi.org/10.1038/s41597-023-02010-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Milz, Stefan Wäldchen, Jana Abouee, Amin Ravichandran, Ashwanth A. Schall, Peter Hagen, Chris Borer, John Lewandowski, Benjamin Wittich, Hans-Christian Mäder, Patrick The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem |
title | The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem |
title_full | The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem |
title_fullStr | The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem |
title_full_unstemmed | The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem |
title_short | The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem |
title_sort | hainich: a multidisciplinary vision data-set for a better understanding of the forest ecosystem |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10043017/ https://www.ncbi.nlm.nih.gov/pubmed/36973316 http://dx.doi.org/10.1038/s41597-023-02010-8 |
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