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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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Bibliographische Detailangaben
Hauptverfasser: Weiser, Hannah (VerfasserIn) , Ulrich, Veit (VerfasserIn) , Winiwarter, Lukas (VerfasserIn) , Esmorís Pena, Alberto M. (VerfasserIn) , Höfle, Bernhard (VerfasserIn)
Dokumenttyp: Datenbank Forschungsdaten
Sprache:Englisch
Veröffentlicht: Heidelberg Universität 2024-01-18
DOI:10.11588/data/UUMEDI
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Forschungsdaten
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Online-Zugang:Resolving-System, kostenfrei, Volltext: https://doi.org/10.11588/data/UUMEDI
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/UUMEDI
Volltext
Verfasserangaben:Hannah Weiser, Veit Ulrich, Lukas Winiwarter, Alberto M. Esmorís, Bernhard Höfle
Beschreibung
Zusammenfassung: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)
Beschreibung:Finanziert durch: Deutsche Forschungsgemeinschaft
Gesehen am 05.02.2024
Beschreibung:Online Resource
DOI:10.11588/data/UUMEDI

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