3D Spatial Data Processing

Data publications of the 3D Spatial Data Processing Group at the Institute of Geography at Heidelberg University.

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101 to 110 of 126 Results

TLS_target_tree_point_clouds.zip Aug 18, 2021 - Opaque Voxel-based Tree Models for Virtual Laser Scanning in Forestry Applications [Research Data and Source Code] ZIP Archive - 1.4 GB - MD5: a23e4e72458e54955200082642d3ce4e
M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code] Jun 15, 2021 Winiwarter, Lukas; Anders, Katharina; Zahs, Vivien; Hämmerle, Martin; Höfle, Bernhard, 2021, "M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]", https://doi.org/10.11588/data/XHYB10, heiDATA, V1 The analysis of topographic time series is often based on bitemporal change detection and quantiﬁcation. For 3D point clouds, acquired using laser scanning or photogrammetry, random and systematic noise has to be separated from the signal of surface change by determining the mini...
2017.zip Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code] ZIP Archive - 3.6 GB - MD5: 7ad6b910c04e9700ff1c920a2b7ab46a
2018a.zip Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code] ZIP Archive - 6.1 GB - MD5: 39e2089bb27ea462e49499bf21809589
2018b.zip Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code] ZIP Archive - 4.8 GB - MD5: 0d7378b75e5ccf65d7ab1a0bf42174f3
code.zip Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code] ZIP Archive - 19.8 KB - MD5: 0c3a30fefb584e1c2e3a72477073679e Code
corepoints.zip Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code] ZIP Archive - 5.9 MB - MD5: fdd517bf9c48eede1c34e5c24e68886c
ICPout.zip Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code] ZIP Archive - 124.1 MB - MD5: 7a0261ca45cc4cdd84c7100d5094cb12
Fully Automatic Spatiotemporal Segmentation of 3D LiDAR Time Series for the Extraction of Natural Surface Changes [Source Code, Validation Material and Validation Results] Jan 13, 2021 Anders, Katharina; Winiwarter, Lukas; Mara, Hubert; Lindenbergh, Roderik; Vos, Sander E.; Höfle, Bernhard, 2021, "Fully Automatic Spatiotemporal Segmentation of 3D LiDAR Time Series for the Extraction of Natural Surface Changes [Source Code, Validation Material and Validation Results]", https://doi.org/10.11588/data/4HJHAA, heiDATA, V1 This dataset comprises the source code to perform fully automatic spatiotemporal segmentation in time series of topographic surface change data (Python scripts). Further provided is the validation material of the resulting extraction of 4D objects-by-change at the study site of a...
code_spatiotemporal_segmentation.zip Jan 13, 2021 - Fully Automatic Spatiotemporal Segmentation of 3D LiDAR Time Series for the Extraction of Natural Surface Changes [Source Code, Validation Material and Validation Results] ZIP Archive - 32.9 KB - MD5: e70398ca0e02a06ce70567fb9fcf15e9 Code

TLS_target_tree_point_clouds.zip

Aug 18, 2021 - Opaque Voxel-based Tree Models for Virtual Laser Scanning in Forestry Applications [Research Data and Source Code]

ZIP Archive - 1.4 GB -

M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]

Jun 15, 2021

Winiwarter, Lukas; Anders, Katharina; Zahs, Vivien; Hämmerle, Martin; Höfle, Bernhard, 2021, "M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]", https://doi.org/10.11588/data/XHYB10, heiDATA, V1

The analysis of topographic time series is often based on bitemporal change detection and quantiﬁcation. For 3D point clouds, acquired using laser scanning or photogrammetry, random and systematic noise has to be separated from the signal of surface change by determining the mini...

2017.zip

Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]

ZIP Archive - 3.6 GB -

2018a.zip

Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]

ZIP Archive - 6.1 GB -

2018b.zip

Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]

ZIP Archive - 4.8 GB -

code.zip

Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]

ZIP Archive - 19.8 KB -

Code

corepoints.zip

Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]

ZIP Archive - 5.9 MB -

ICPout.zip

Jun 15, 2021 - M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]

ZIP Archive - 124.1 MB -

Fully Automatic Spatiotemporal Segmentation of 3D LiDAR Time Series for the Extraction of Natural Surface Changes [Source Code, Validation Material and Validation Results]

Jan 13, 2021

Anders, Katharina; Winiwarter, Lukas; Mara, Hubert; Lindenbergh, Roderik; Vos, Sander E.; Höfle, Bernhard, 2021, "Fully Automatic Spatiotemporal Segmentation of 3D LiDAR Time Series for the Extraction of Natural Surface Changes [Source Code, Validation Material and Validation Results]", https://doi.org/10.11588/data/4HJHAA, heiDATA, V1

This dataset comprises the source code to perform fully automatic spatiotemporal segmentation in time series of topographic surface change data (Python scripts). Further provided is the validation material of the resulting extraction of 4D objects-by-change at the study site of a...

code_spatiotemporal_segmentation.zip

Jan 13, 2021 - Fully Automatic Spatiotemporal Segmentation of 3D LiDAR Time Series for the Extraction of Natural Surface Changes [Source Code, Validation Material and Validation Results]

ZIP Archive - 32.9 KB -

Code

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