1 to 10 of 16 Results
Feb 19, 2024
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... |
Jan 18, 2024
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... |
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... |
Dec 13, 2023
Vallejo Orti, Miguel; Negussie, Kaleb; Corral, Eva; Höfle, Bernhard; Bubenzer, Olaf, 2023, "Gully detection with Inverse Morphological Reconstruction Algorithm [data]", https://doi.org/10.11588/data/PXDR4M, heiDATA, V1
Characterization of micro-terrain features has been explored to detect gully objects in the terrain. An adaptation to the morphological reconstruction operator is implemented to detect gullies instead of buildings or other man-made structures. This operator can be configured to d... |
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... |
Jul 11, 2023
Vallejo Orti, Miguel; Negussie, Kaleb; Corral, Eva; Höfle, Bernhard; Bubenzer, Olaf, 2023, "Multi Profile Curvature Analysis (MPCA) algorithm for gully detection using TanDEM X Digital elevation model.", https://doi.org/10.11588/data/A4KGYJ, heiDATA, V1
Characterization of micro-terrain features has been explored to detect convex and concave features in the terrain. The analysis of first and second derivatives of a function fitted to the terrain is a frequently used resource to describe terrain characteristics and to undertake G... |
Feb 1, 2023
Winiwarter, Lukas; Anders, Katharina; Battuvshin, Guyen; Menzel, Lucas; Höfle, Bernhard, 2023, "UAV laser scanning and terrestrial laser scanning point clouds of snow-on and snow-off conditions of a forest plot in the black forest at Hundseck, Baden-Württemberg, Germany [data]", https://doi.org/10.11588/data/UCPTP1, heiDATA, V1
This dataset consists of 3D point clouds acquired via UAV laser scanning (ULS) of a forest plot in the Black Forest in Hundseck, Germany. The plot was captured under snow conditions in January 2021, and the acquisition was repeated under snow-off conditions in February 2021. Addi... |
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... |
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... |
Dec 9, 2021
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... |
