Correspondence-driven plane-based M3C2 for quantification of 3D topographic change with lower uncertainty [Data and Source Code]

DOI

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 (M3C2) algorithm to consider the associated uncertainty. Change measured with the M3C2 approach, however, is difficult to interpret where 1) change occurs in directions different to the direction of change computation or 2) the quantified magnitudes of change are exceeded by the associated uncertainty due to a rough surface morphology. We present a correspondence-driven plane-based M3C2 approach that is tailored to quantifying small-magnitude (< 0.1 m) 3D topographic change of rough surfaces by reducing the uncertainty of quantified change. The approach 1) extracts planar surfaces in point clouds of successive epochs, 2) identifies corresponding planar surfaces between two point clouds using a binary random forest classification, and 3) calculates M3C2 distances and the associated uncertainty between the corresponding planar surfaces. This correspondence-driven plane-based M3C2 does not require recognition or reconstruction of geometrically complex objects but instead quantifies change between less complex, homologous planar surfaces. The approach further allows to relate change directly to a moving object. We apply our approach to a bi-weekly time series of terrestrial laser scanning point clouds acquired at a rock glacier in the Austrian Alps. The approach enables a sevenfold reduction in the uncertainty associated with topographic change compared to standard M3C2. Significant change is therefore detected in around 75% of the area of change analysis, whereas standard M3C2 detects significant change in only 16% (2-week timespan) to 59% (10-week timespan) of the same area. The correspondence-driven plane-based M3C2 complements 3D change analysis in applications that aim to quantify smallmagnitude topographic change in photogrammetric or laser scanning point clouds with low uncertainties in natural scenes which are characterised by overall rough surface morphology and by individual rigid objects with planar surfaces (e.g., rock glaciers, landslides, debris covered glaciers).

This dataset includes six point clouds acquired bi-weekly by terrestrial laser scanning in the summer of 2019. Point clouds have been preprocessed using the following workflow: 1) MSA coregistration within all epochs using RiSCAN PRO (v. 2.11), 2) cropping of point clouds to the area of the rock glacier using a region filter in the software OPALS, 3) tiling into 10 tiles using a region filter in OPALS, 4) point cloud filtering to remove statistical outlier points in CloudCompare (v. 2.11.1) according to https://pointclouds.org/documentation/classpcl_1_1_statistical_outlier_removal.html (parameters: number of points to use for mean distance estimation: 12; standard deviation multiplier threshold (nSigma): 1.00), 5) normal computation in OPALS (parameters: -NormalsAlg robustPlane -searchMode d3 -searchRad 0.5 -neigh 200 -selmode nearest -storeMetaInfo medium -direction upwards)

Based on these datasets, the extraction of planar areas and the identification of plane correspondences were performed as described in Zahs et al. (2021).

Approximated scan positions for the laser scans are: (X/Y/Z in UTM32N) SP1 = [652992.6490, 5189116.7022, 2526.2710], SP2 = [652949.7085, 5189182.2139, 2473.9986], SP3 = [652917.8332, 5189284.5585, 2423.7433], SP4 = [652804.5869, 5189190.5423, 2456.0348], SP5 = [652812.0904, 5189246.1069, 2433.7296], SP6 = [652831.6805, 5189073.5765, 2523.7454], SP7 = [652862.9167, 5189292.7994, 2403.6955]

Time period covered: Start: 2019-06-24; End: 2019-08-30

Dates of collection: Start: 2019-06-24; End: 2019-06-24 Start: 2019-07-06; End: 2019-07-06 Start: 2019-07-19; End: 2019-07-19 Start: 2019-08-03; End: 2019-08-03 Start: 2019-08-16; End: 2019-08-16 Start: 2019-08-30; End: 2019-08-30

Alignment error between point clouds of all dates in stable areas: 0.011-0.013 m.

Identifier
DOI https://doi.org/10.11588/data/TGSVUI
Related Identifier https://doi.org/10.1016/j.isprsjprs.2021.11.018
Metadata Access https://heidata.uni-heidelberg.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.11588/data/TGSVUI
Provenance
Creator Zahs, Vivien (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany); Winiwarter, Lukas (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany); Anders, Katharina (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany); Williams, Jack G. (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany); Rutzinger, Martin (Institute of Geography, University of Innsbruck, Austria); Bremer, Magnus (Institute of Geography, University of Innsbruck, Austria); Höfle, Bernhard (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany)
Publisher heiDATA
Contributor Zahs, Vivien; Höfle, Bernhard
Publication Year 2021
Rights Data is licensed under <a href='http://creativecommons.org/licenses/by/4.0/'>Creative Commons Attribution 4.0 International License &#160;<img src='https://i.creativecommons.org/l/by/4.0/80x15.png' alt='CC by' /></a>.<br /> ---<br /> Source code in Code.zip is licensed under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">General Public License v3 (GPL v3)</a>.<br /> ---<br /> outcrop.exe in Outcrop_segmentation.zip is licensed under the MIT License:<br /> <br /> Copyright (c) 2022 Heidelberg University, 3D Geospatial Data Processing Research Group.<br /> <br /> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:<br /> <br /> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.<br /> <br /> THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. <br /> <br /> outcrop.exe is dynamically linked to ANN.dll.<br /> ---<br /> ANN.dll. in Outcrop_segmentation.zip is provided under the LGPL license:<br /> <br /> Copyright (c) 1997 University of Maryland and Sunil Arya and David Mount.<br /> <br /> Source code is available here: <a href="http://www.cs.umd.edu/~mount/ANN/ ">http://www.cs.umd.edu/~mount/ANN/</a>.<br /> License details are available here: <a href="http://www.gnu.org/licenses/lgpl-3.0.html">http://www.gnu.org/licenses/lgpl-3.0.html</a>; info:eu-repo/semantics/openAccess
OpenAccess true
Contact Zahs, Vivien (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany); Höfle, Bernhard (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany)
Representation
Resource Type ASCII 3D point clouds with format: (X Y Z NormalX NormalY NormalZ NormalSigma0 NormalPtsGiven NormalPtsUsed NormalEigenvalue1 NormalEigenvalue2 NormalEigenvalue3).; Dataset
Format application/zip; text/plain
Size 8719; 7982107454; 7444144589; 7325598723; 8191102606; 7761377247; 7947205017; 5877; 385582
Version 2.0
Discipline Earth and Environmental Science