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1 to 5 of 5 Results

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] Jul 20, 2023 - 3D Spatial Data Processing Zahs, Vivien; Anders, Katharina; Kohns, Julia; Stark, Alexander; Höfle, Bernhard, 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]", https://doi.org/10.11588/data/D3WZID, heiDATA, V1 Automatic damage assessment by analysing UAV-derived 3D point clouds provides fast information on the damage situation after an earthquake. However, the assessment of different damage grades is challenging given the variety in damage characteristics and limited transferability of...
Correspondence-driven plane-based M3C2 for quantification of 3D topographic change with lower uncertainty [Data and Source Code] Jan 25, 2022 - 3D Spatial Data Processing 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...
M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code] Jun 15, 2021 - 3D Spatial Data Processing 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...
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 - 3D Spatial Data Processing 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...
HELIOS full-waveform laser scanning simulation framework. Source code, precompiled version, example files for study of understory tree height scanning and respective output. May 22, 2017 - 3D Spatial Data Processing Hämmerle, Martin; Lukač, Niko; Chen, Kuei-Chia; Koma, Zsófia; Wang, Chi-Kuei; Anders, Katharina; Höfle, Bernhard, 2017, "HELIOS full-waveform laser scanning simulation framework. Source code, precompiled version, example files for study of understory tree height scanning and respective output.", https://doi.org/10.11588/data/10101, heiDATA, V1 This data collection enables any user to reproduce the study Hämmerle et al. (2017). It provides the source code to compile the applied simulation framework. Furthermore, a precompiled version of the software including the necessary files are provided so that a direct start of th...

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]

Jul 20, 2023 - 3D Spatial Data Processing

Zahs, Vivien; Anders, Katharina; Kohns, Julia; Stark, Alexander; Höfle, Bernhard, 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]", https://doi.org/10.11588/data/D3WZID, heiDATA, V1

Automatic damage assessment by analysing UAV-derived 3D point clouds provides fast information on the damage situation after an earthquake. However, the assessment of different damage grades is challenging given the variety in damage characteristics and limited transferability of...

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

Jan 25, 2022 - 3D Spatial Data Processing

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...

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

Jun 15, 2021 - 3D Spatial Data Processing

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...

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 - 3D Spatial Data Processing

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...

HELIOS full-waveform laser scanning simulation framework. Source code, precompiled version, example files for study of understory tree height scanning and respective output.

May 22, 2017 - 3D Spatial Data Processing

Hämmerle, Martin; Lukač, Niko; Chen, Kuei-Chia; Koma, Zsófia; Wang, Chi-Kuei; Anders, Katharina; Höfle, Bernhard, 2017, "HELIOS full-waveform laser scanning simulation framework. Source code, precompiled version, example files for study of understory tree height scanning and respective output.", https://doi.org/10.11588/data/10101, heiDATA, V1

This data collection enables any user to reproduce the study Hämmerle et al. (2017). It provides the source code to compile the applied simulation framework. Furthermore, a precompiled version of the software including the necessary files are provided so that a direct start of th...

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