Persistent Identifier
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doi:10.11588/data/UUMEDI |
Publication Date
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2024-01-18 |
Title
| Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation |
Author
| Weiser, Hannah (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany) - ORCID: 0000-0003-3256-7311
Ulrich, Veit (Geoinformatics Research Group, Institute of Geography, Heidelberg University, Germany) - ORCID: 0000-0002-7058-6946
Winiwarter, Lukas (Research Unit Photogrammetry, Department of Geodesy and Geoinformation, TU Wien, Austria) - ORCID: 0000-0001-8229-1160
Esmorís, Alberto M. (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany)
Höfle, Bernhard (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany) - ORCID: 0000-0001-5849-1461 |
Point of Contact
|
Use email button above to contact.
Höfle, Bernhard (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany) |
Description
| This dataset contains 11 terrestrial laser scanning (TLS) tree point clouds (in .LAZ format v1.4) of 7 different species, which have been manually labeled into leaf and wood points. The labels are contained in the Classification field (0 = wood, 1 = leaf). The point clouds have additional attributes (Deviation, Reflectance, Amplitude, GpsTime, PointSourceId, NumberOfReturns, ReturnNumber). Before labeling, all point clouds were filtered by Deviation, discarding all points with a Deviation greater than 50. An ASCII file with tree species and tree positions (in ETRS89 / UTM zone 32N; EPSG:25832) is provided, which can be used to normalize and center the point clouds.
This dataset is intended to be used for training and validation of algorithms for semantic segmentation (leaf-wood separation) of TLS tree point clouds, as done by Esmorís et al. 2023 (Related Publication).
The point clouds are a subset of a larger dataset, which is available on PANGAEA (Weiser et al. 2022b, see Related Dataset). More details on data acquisition and processing, file formats, and quality assessments can be found in the corresponding data description paper (Weiser et al. 2022a, see Related Material). (2023-10-05) |
Subject
| Earth and Environmental Sciences |
Keyword
| semantic segmentation
3D
forestry
ecology |
Related Publication
| Esmorís, A.M., Weiser, H., Winiwarter, L., Cabaleiro, J.C. & Höfle, B. (2024): Deep learning with simulated laser scanning data for 3D point cloud classification. EarthArXiv. doi: https://doi.org/10.31223/X53Q3Q |
Funding Information
| Deutsche Forschungsgemeinschaft (DFG): 411263134
Deutsche Forschungsgemeinschaft (DFG): 496418931 |
Date of Collection
| Start Date: 2019-06-25 ; End Date: 2019-09-03 |
Related Material
| Weiser, H., Schäfer, J., Winiwarter, L., Krašovec, N., Fassnacht, F.E. & Höfle, B. (2022a): Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests. Earth System Science Data. Vol. 14 (7), pp. 2989-3012. DOI: https://doi.org/10.5194/essd-14-2989-2022 |
Related Dataset
| Weiser, H., Schäfer, J., Winiwarter, L., Krašovec, N., Seitz, C., Schimka, M., Anders, K., Baete, D., Braz, A.S., Brand, J., Debroize, D., Kuss, P., Martin, L.L., Mayer, A., Schrempp, T., Schwarz, L.-M., Ulrich, V., Fassnacht, F.E. & Höfle, B. (2022b): Terrestrial, UAV-borne, and airborne laser scanning point clouds of central European forest plots, Germany, with extracted individual trees and manual forest inventory measurements. PANGAEA. DOI:10.1594/PANGAEA.942856 |