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1 to 4 of 4 Results
An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]
May 13, 2024 - 3D Spatial Data Processing
Vallejo-Orti, Miguel; Winiwarter, Lukas; Corral, Eva; Williams, Jack; Bubenzer, Olaf; Höfle, Bernhard, 2024, "An Automatic Iterative Random Forest approach to derive gully activity maps in large areas with training data scarcity [Data and Source Code]", https://doi.org/10.11588/data/WGAU4Q, heiDATA, V1
Gullies are landforms with specific patterns of shape, topography, hydrology, vegetation, and soil characteristics. Remote sensing products (TanDEM-X, Sentinel-1, and Sentinel-2) serve as inputs into an iterative algorithm, initialized using a micro-mapping simulation as training...
OpenStreetMap land use for Europe "Research Data"
Apr 26, 2024 - GIScience / Geoinformatics Research Group
Schultz, Michael; Li, Hao; Wu, Zhaoyhan; Wiell, Daniel; Auer, Michael; Zipf, Alexander, 2024, "OpenStreetMap land use for Europe "Research Data"", https://doi.org/10.11588/data/IUTCDN, heiDATA, V1
OSMLanduse data is a scientific dataset generated within the scope of the Horizon 2020 - LandSense project. It is a classification of Sentinel-2 imagery using a deep learning model trained on OSM landuse and landcover features. The data might contain errorneous classifications. T...
Integrating VGI contributions for gully mapping using Kalman filter and machine learning
Feb 19, 2024 - 3D Spatial Data Processing
Vallejo Orti, Miguel; Anders, Katharina; Ajali, Oliubikum; Bubenzer, Olaf; Höfle, Bernhard, 2024, "Integrating VGI contributions for gully mapping using Kalman filter and machine learning", https://doi.org/10.11588/data/UHSQG0, heiDATA, V1, UNF:6:dbfZe/C8CmWXcBEZJg2RPw== [fileUNF]
The codes and datsets included are related to experiments and results conducted to integrate different lines digitized by volunteers using Kalman filter with changing amount of input lines. Three approaches are included: i) Kalman filtering integration to investigate the role of...
Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation
Jan 18, 2024 - 3D Spatial Data Processing
Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", https://doi.org/10.11588/data/UUMEDI, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]
This dataset contains 11 terrestrial laser scanning (TLS) tree point clouds (in .LAZ format v1.4) of 7 different species, which have been manually labeled into leaf and wood points. The labels are contained in the Classification field (0 = wood, 1 = leaf). The point clouds have a...
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