3D Spatial Data Processing

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61 to 70 of 121 Results

nkd_fpl_slope_LT.json Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard JSON - 300 B - MD5: 1fe264da9d9691837fb718f99cf96803 working_data Filter pipeline for segments of class "slope" and "ridge".
nkd_fpl_valley_TF.json Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard JSON - 563 B - MD5: 5f845e8634511fea8aa1cde9d3ad288f working_data Filter pipeline for segments of class "valley".
nkd_pcl_epsg6670.laz Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard Unknown - 77.7 MB - MD5: e9b080ab871e5a96a31c73179235dd9e input_dataoriginal_data Input data: 3D point cloud from the sample region to which the filters are applied with the AFwizard.
nkd_sgm_assigned_TF.geojson Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard GeoJSON - 18.4 KB - MD5: cb34ffb4e3214f613330c72a9093c120 working_data Segmentation of the sample area with segment classes and the adapted filter pipelines assigned.
nkd_sgm.geojson Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard GeoJSON - 26.4 KB - MD5: f352d25ba4ca4139a94d478d0fdfa6a5 auxiliary_datainput_data Input data: Segmentation of the sample area with segment classes but without filter pipelines assigned.
sample_pcl_paddy.las Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard Unknown - 4.0 MB - MD5: ecb287c259ef82ed4e449f718abf8abf working_data 3D point cloud of a sub-area that represents segments of class "paddy".
sample_pcl_ridge.las Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard Unknown - 2.1 MB - MD5: 2a372cd47af207796c29f06ce650f381 working_data 3D point cloud of a sub-area that represents segments of class "ridge".
sample_pcl_slope.las Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard Unknown - 2.0 MB - MD5: 5a90a1674a763f4520c5ec166edb9f4f working_data 3D point cloud of a sub-area that represents segments of class "slope".
sample_pcl_valley.las Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard Unknown - 3.0 MB - MD5: b196cd45a6bdedf9ab47d01fa830c825 working_data 3D point cloud of a sub-area that represents segments of class "valley".
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...

nkd_fpl_slope_LT.json

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

JSON - 300 B -

working_data

Filter pipeline for segments of class "slope" and "ridge".

nkd_fpl_valley_TF.json

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

JSON - 563 B -

working_data

Filter pipeline for segments of class "valley".

nkd_pcl_epsg6670.laz

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

Unknown - 77.7 MB -

input_dataoriginal_data

Input data: 3D point cloud from the sample region to which the filters are applied with the AFwizard.

nkd_sgm_assigned_TF.geojson

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

GeoJSON - 18.4 KB -

working_data

Segmentation of the sample area with segment classes and the adapted filter pipelines assigned.

nkd_sgm.geojson

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

GeoJSON - 26.4 KB -

auxiliary_datainput_data

Input data: Segmentation of the sample area with segment classes but without filter pipelines assigned.

sample_pcl_paddy.las

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

Unknown - 4.0 MB -

working_data

3D point cloud of a sub-area that represents segments of class "paddy".

sample_pcl_ridge.las

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

Unknown - 2.1 MB -

working_data

3D point cloud of a sub-area that represents segments of class "ridge".

sample_pcl_slope.las

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

Unknown - 2.0 MB -

working_data

3D point cloud of a sub-area that represents segments of class "slope".

sample_pcl_valley.las

Jul 5, 2022 - 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

Unknown - 3.0 MB -

working_data

3D point cloud of a sub-area that represents segments of class "valley".

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...

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