1 to 10 of 38 Results
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
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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
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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
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Aug 18, 2021 -
Opaque Voxel-based Tree Models for Virtual Laser Scanning in Forestry Applications [Research Data and Source Code]
ZIP Archive - 5.5 KB -
MD5: 6c6372dbc0dd0f4ba82dcdac65cb2462
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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
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ZIP Archive - 8.5 KB -
MD5: b9f528563a1081e7e716d2e3ef1bd033
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ZIP Archive - 32.9 KB -
MD5: e70398ca0e02a06ce70567fb9fcf15e9
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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
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Jan 25, 2022
Zahs, Vivien; Winiwarter, Lukas; Anders, Katharina; Williams, Jack G.; Rutzinger, Martin; Bremer, Magnus; Höfle, Bernhard, 2021, "Correspondence-driven plane-based M3C2 for quantification of 3D topographic change with lower uncertainty [Data and Source Code]", https://doi.org/10.11588/data/TGSVUI, heiDATA, V2
The analysis and interpretation of 3D topographic change requires methods that achieve low uncertainties in change quantification. Many recent geoscientific studies that perform point cloud-based topographic change analysis have used the multi-scale-model-to-model-cloudcomparison... |
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... |