GIScience and 3D Spatial Data Processing

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1 to 10 of 127 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...
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
validation_material.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 - 6.0 GB - MD5: 9e2891179c4e7dcbcdcb75719676cf84 Data
validation_result.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 - 1.1 KB - MD5: 4487e9753808165b4f0f6b0630a56225 Data
Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code Dec 15, 2020 Ludwig, Christina; Hecht, Robert; Lautenbach, Sven; Schorcht, Martin; Zipf, Alexander, 2020, "Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code", https://doi.org/10.11588/data/UYSAA5, heiDATA, V1, UNF:6:+pceldpLQoaQQqPk4/t1VQ== [fileUNF] Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data...
01_Greenness_Basic_Probability_Assignment.ipynb Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code Jupyter Notebook - 184.8 KB - MD5: 99f2ba63e6a7dde837fdec4976df4dad
02_Greenness_Fusion_and_Validation.ipynb Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code Jupyter Notebook - 404.1 KB - MD5: 0a2cf024c3da9737f016a427c1523c29
03_Public_Accessibility_Model.ipynb Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code Jupyter Notebook - 712.0 KB - MD5: 1d6de57d17c8bf61e872a21031e455cd
04_Green_Public_Fusion.ipynb Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code Jupyter Notebook - 921.4 KB - MD5: c96cff3181dd05797f2104b4afb6877f
accuracy_vs_uncertainty.pdf Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code Adobe PDF - 14.8 KB - MD5: a9c8d1829dc139b55828beae21b44f98

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...

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

validation_material.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 - 6.0 GB - MD5: 9e2891179c4e7dcbcdcb75719676cf84

Data

validation_result.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 - 1.1 KB - MD5: 4487e9753808165b4f0f6b0630a56225

Data

Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code

Dec 15, 2020

Ludwig, Christina; Hecht, Robert; Lautenbach, Sven; Schorcht, Martin; Zipf, Alexander, 2020, "Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code", https://doi.org/10.11588/data/UYSAA5, heiDATA, V1, UNF:6:+pceldpLQoaQQqPk4/t1VQ== [fileUNF]

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data...

01_Greenness_Basic_Probability_Assignment.ipynb

Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code

Jupyter Notebook - 184.8 KB - MD5: 99f2ba63e6a7dde837fdec4976df4dad

02_Greenness_Fusion_and_Validation.ipynb

Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code

Jupyter Notebook - 404.1 KB - MD5: 0a2cf024c3da9737f016a427c1523c29

03_Public_Accessibility_Model.ipynb

Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code

Jupyter Notebook - 712.0 KB - MD5: 1d6de57d17c8bf61e872a21031e455cd

04_Green_Public_Fusion.ipynb

Dec 15, 2020 - Mapping Public Urban Green Spaces based on OpenStreetMap and Sentinel-2 imagery using Belief Functions: Data and Source Code

Jupyter Notebook - 921.4 KB - MD5: c96cff3181dd05797f2104b4afb6877f