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Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation

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...

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Bibliographic Details
Main Authors: Weiser, Hannah (Author) , Ulrich, Veit (Author) , Winiwarter, Lukas (Author) , Esmorís Pena, Alberto M. (Author) , Höfle, Bernhard (Author)
Format: Database Research Data
Language:English
Published: Heidelberg Universität 2024-01-18
DOI:10.11588/data/UUMEDI
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Author Notes:Hannah Weiser, Veit Ulrich, Lukas Winiwarter, Alberto M. Esmorís, Bernhard Höfle
Description
Summary: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)
Item Description:Finanziert durch: Deutsche Forschungsgemeinschaft
Gesehen am 05.02.2024
Physical Description:Online Resource
DOI:10.11588/data/UUMEDI

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