3D Spatial Data Processing

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11 to 17 of 17 Results

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...
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...
Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data] Jan 16, 2024 Vallejo Orti, Miguel; Castillo, Carlos; Zahs, Vivien; Bubenzer, Olaf; Höfle, Bernhard, 2023, "Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]", https://doi.org/10.11588/data/NSMM6P, heiDATA, V2, UNF:6:KVUhApCn+Ker99oncknXzA== [fileUNF] This code implements the TimeSeriesForest algorithm to classify different types of changes in gully environments. i)gully topographical change, ii)no change outside gully, iii) no change inside gully, and iv) non-topographical change. The algorithm is specifically designed for ti...
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 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...
3D-MAPP: 3D-MicroMapping of Big 3D Geo-Datasets in the Web Aug 4, 2017 Herfort, Benjamin; Eberlein, Stefan, 2017, "3D-MAPP: 3D-MicroMapping of Big 3D Geo-Datasets in the Web", https://doi.org/10.11588/data/Y4V85F, heiDATA, V1 The research project 3D-MAPP develops a web-based methodology to obtain digital geodata via the combination of data analysis by human and machine. Through a quick and easy-to-use 3D Web visualization users are able – in a few seconds – to solve 3D micro mapping tasks, which can h...
3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard Jul 21, 2022 Shinoto, Maria; Doneus, Michael; Haijima, Hideyuki; Weiser, Hannah; Zahs, Vivien; Kempf, Dominic; Daskalakis, Gwydion; Höfle, Bernhard; Nakamura, Naoko, 2022, "3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard", https://doi.org/10.11588/data/TJNQZG, heiDATA, V2 This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data...
3D Micro-Mapping of Subsidence Stations [Source Code and Data] Jan 15, 2020 Herfort, Benjamin; Anders, Katharina; Marx, Sabrina; Eberlein, Stefan; Höfle, Bernhard, 2020, "3D Micro-Mapping of Subsidence Stations [Source Code and Data]", https://doi.org/10.11588/data/OU8YA1, heiDATA, V1 This dataset comprises the source code to reproduce the 3D micro-mapping tool for plane adjustment at subsidence stations. In this project, users adjust a plane (height and orientation) at the positions of fixed poles, so-called subsidence stations, to provide information on the...

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...

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...

Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]

Jan 16, 2024

Vallejo Orti, Miguel; Castillo, Carlos; Zahs, Vivien; Bubenzer, Olaf; Höfle, Bernhard, 2023, "Classification of Types of Changes in Gully Environments Using Time Series Forest Algorithm [data]", https://doi.org/10.11588/data/NSMM6P, heiDATA, V2, UNF:6:KVUhApCn+Ker99oncknXzA== [fileUNF]

This code implements the TimeSeriesForest algorithm to classify different types of changes in gully environments. i)gully topographical change, ii)no change outside gully, iii) no change inside gully, and iv) non-topographical change. The algorithm is specifically designed for ti...

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

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...

3D-MAPP: 3D-MicroMapping of Big 3D Geo-Datasets in the Web

Aug 4, 2017

Herfort, Benjamin; Eberlein, Stefan, 2017, "3D-MAPP: 3D-MicroMapping of Big 3D Geo-Datasets in the Web", https://doi.org/10.11588/data/Y4V85F, heiDATA, V1

The research project 3D-MAPP develops a web-based methodology to obtain digital geodata via the combination of data analysis by human and machine. Through a quick and easy-to-use 3D Web visualization users are able – in a few seconds – to solve 3D micro mapping tasks, which can h...

3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

Jul 21, 2022

Shinoto, Maria; Doneus, Michael; Haijima, Hideyuki; Weiser, Hannah; Zahs, Vivien; Kempf, Dominic; Daskalakis, Gwydion; Höfle, Bernhard; Nakamura, Naoko, 2022, "3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard", https://doi.org/10.11588/data/TJNQZG, heiDATA, V2

This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data...

3D Micro-Mapping of Subsidence Stations [Source Code and Data]

Jan 15, 2020

Herfort, Benjamin; Anders, Katharina; Marx, Sabrina; Eberlein, Stefan; Höfle, Bernhard, 2020, "3D Micro-Mapping of Subsidence Stations [Source Code and Data]", https://doi.org/10.11588/data/OU8YA1, heiDATA, V1

This dataset comprises the source code to reproduce the 3D micro-mapping tool for plane adjustment at subsidence stations. In this project, users adjust a plane (height and orientation) at the positions of fixed poles, so-called subsidence stations, to provide information on the...

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