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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21 to 30 of 121 Results

clustering.py Jul 20, 2023 - Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data [Data and Source Code] Python Source Code - 2.4 KB - MD5: d7f5b012010384fc2a57e6a90e3d7382 Code
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
Code.zip Dec 2, 2021 - Correspondence-driven plane-based M3C2 for quantification of 3D topographic change with lower uncertainty [Data and Source Code] ZIP Archive - 8.5 KB - MD5: b9f528563a1081e7e716d2e3ef1bd033 Code
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
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
Correspondence-driven plane-based M3C2 for quantification of 3D topographic change with lower uncertainty [Data and Source Code] 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...
Cross_Training_Integration.zip Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning ZIP Archive - 1.9 MB - MD5: 4833cdc615e69607bd51b49cfff7ea9d
damage_classification.py Jul 20, 2023 - Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data [Data and Source Code] Python Source Code - 3.8 KB - MD5: eae6256b9fc16f6a58f68bee45db4f63 Code
FagSyl_BR01_01_2019-07-08_q1_TLS-on_c.laz Jan 18, 2024 - Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation Unknown - 127.3 MB - MD5: 770c0976020ac2767478aa41a8f929b7
FagSyl_BR05_P8T4_2019-07-10_q2_TLS-on_c.laz Jan 18, 2024 - Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation Unknown - 35.5 MB - MD5: 1dcd54f0c35163b525766500f2e37a36

clustering.py

Jul 20, 2023 - Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data [Data and Source Code]

Python Source Code - 2.4 KB -

Code

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

Code.zip

Dec 2, 2021 - Correspondence-driven plane-based M3C2 for quantification of 3D topographic change with lower uncertainty [Data and Source Code]

ZIP Archive - 8.5 KB -

Code

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

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 -

Correspondence-driven plane-based M3C2 for quantification of 3D topographic change with lower uncertainty [Data and Source Code]

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...

Cross_Training_Integration.zip

Feb 19, 2024 - Integrating VGI contributions for gully mapping using Kalman filter and machine learning

ZIP Archive - 1.9 MB -

damage_classification.py

Python Source Code - 3.8 KB -

Code

FagSyl_BR01_01_2019-07-08_q1_TLS-on_c.laz

Jan 18, 2024 - Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation

Unknown - 127.3 MB -

FagSyl_BR05_P8T4_2019-07-10_q2_TLS-on_c.laz

Jan 18, 2024 - Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation

Unknown - 35.5 MB -

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