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1 to 8 of 8 Results
Aug 18, 2021 - 3D Spatial Data Processing
Weiser, Hannah; Winiwarter, Lukas; Anders, Katharina; Fassnacht, Fabian Ewald; Höfle, Bernhard, 2021, "Opaque Voxel-based Tree Models for Virtual Laser Scanning in Forestry Applications [Research Data and Source Code]", https://doi.org/10.11588/data/MZBO7T, heiDATA, V1
Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is as a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key...
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...
Dec 9, 2021 - 3D Spatial Data Processing
Anders, Katharina; Winiwarter, Lukas; Höfle, Bernhard, 2021, "Improving change analysis from near-continuous 3D time series by considering full temporal information [Data and Source Code]", https://doi.org/10.11588/data/1L11SQ, heiDATA, V1
This dataset comprises the source code (Python scripts) and data to perform spatiotemporal segmentation in time series of surface change data for a (i) synthetic dataset and (ii) hourly snow cover changes acquired by terrestrial laser scanning. Further details are given in the co...
Apr 26, 2021 - Hydrogeochemie und Hydrogeologie
Ritter, Simon; Leberecht, Kerstin; Eschenröder, Julian; Scholz, Christian, 2021, "Hells Bells project - results of sampling campaign in February 2020", https://doi.org/10.11588/data/GYLDH5, heiDATA, V2, UNF:6:1Ua7KESTWxO3lRrcac/mWQ== [fileUNF]
This data set compiles the results of water chemical analyses as well as bulk chemical analyses of the particles suspended in the water columns of cenotes (sinkholes) obtained during a sampling campaign in Mexico in February 2020. Three stratified cenotes from North-Eastern Yucat...
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...
Mar 2, 2021 - Hydrology and Climatology
Tijdeman, Erik; Menzel, Lucas, 2021, "Daily gridded soil moisture simulations on a 1 km resolution grid covering Baden-Württemberg", https://doi.org/10.11588/data/PRXZAS, heiDATA, V1
The dataset contains gridded daily soil moisture simulations for Baden-Württemberg. The simulations were caried out with the hydroloigcal model TRAIN. The TRAIN model was set up for a 1 km resolution grid over the study region, which encompasses a variety of different soil, land...
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...
Oct 12, 2021 - GIScience / Geoinformatics Research Group
Li, Hao; Zech, Johannes; Ludwig, Christina; Fendrich, Sascha; Shapiro, Aurelie; Schultz, Michael; Zipf, Alexander, 2021, "Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning [Research Data]", https://doi.org/10.11588/data/AAKAF9, heiDATA, V1
DATASET FOR JOURNAL PAPER (https://doi.org/10.1016/j.jag.2021.102571) Large-scale mapping activities can benefit from the vastly increasing availability of earth observation (EO) data, especially when combined with volunteered geographical information (VGI) using machine learning...
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