Data for output replication
Data presented here are used to generate Figures presented in the research article "An evaluation of low-cost terrestrial LiDAR sensors for assessing geomorphic change". The data presented is used in conjunction with code publised at: CatchmentSci/livox-poc-article. Full descriptions of the workflow for replicating the research outputs is provided within the GitHub pages. Below we present a brief description of each of the datasets:
accelerometer_data.7z
A 7zip file containing a further 471.csv files. These contain the acceleration components [x,y,z] detected by the accelerometer housed within the enclosure containing the Livox Mid-40 sensors. This data is used to generate Figure 5 (A,D).
initial_icp_logs.7z
A 7zip file containing a further 2061.txt files. These contain the CloudCompare log files following alignment of scanner01 to scanner02 (for each epoch - e.g. "2022-01-29_12-57-43_initial_icp_logfile.txt"), and results of alignment of the merged scan (scanner01 and 02) for each epoch back to the reference scan undertaken on 29th January 2022 (e.g. "2022-01-29_14-57-42_scan02_CROPPED1_MERGED_logfile.txt"). This data is used to generate Figure 5 (A,D).
registration_matrix.7z
A 7zip file containing a further 2061.txt files. These contain the matrix transformations that are applied to the data for alignment purposes. To align scanner01 to scanner02 for each epoch the files have a format similar to "2022-04-01_23-59-05_scan01_trans_segmented_REGISTRATION_MATRIX.txt". For alignment of the merged scan (scanner01 and 02) for each epoch back to the reference scan undertaken on 29th January 2022 the files takes a format similar to "2022-04-01_23-59-05_scan02_CROPPED1_MERGED_REGISTRATION_MATRIX.txt". This data is used to generate Figure 5 (B,C,E,F).
goldrill_20211110_SfM_cloud - final align - clipped.txt
Colorised pointcloud used for visualisation purposes in Figure 6. The pointcloud was generated used Agisoft Metashape Professional following image acquisition with a DJI Phantom 4 Pro on 10th November 2021.
M3C2 lvx output.txt
Change detection conducted on Livox scanner data acquired on 29 Jan 2022 and 24 Mar 2022. Parameters used to generate this output can be found within Table 1 of the research article. This data is presented in Figure 6 (1A-3A).
M3C2 riegl output.txt
Change detection conducted on Riegl VZ-4000 scanner data acquired on 28 Jan 2022 and 24 Mar 2022. Parameters used to generate this output can be found within Table 1 of the research article. This data is presented in Figure 6 (1B - 3B).
m3c2_ecdf_data.mat
MATLAB data file consisting of empirical cumulative distribution function returns for change detection data for the Riegl and Livox scanners from within Zones 1-3 (see Figure 6). This data was generated through execution of 'cdf_zones_show.m' from within 'cloud_comparisons.m'. This data is used to generate outputs presented in Figure 7.
GaugingData.7z
A 7zip file containing a further 1016 .mat files. These files are MATLAB data files which are generated when extracting water surface elevations from the Livox data returns. This data is used to generate outputs presented in Figure 8.
Goldrill_level_data.xlsx
A Excel spreadsheet containing river level observations made 225m upstream of the Livox LiDAR system. The data was acquired using data acquired from a In-Situ Aqua TROLL 200 Data Logger housed within a stilling well. This data is used to generate outputs presented in Figure 8.
cross_section_outputs.7z
A 7z file containing of four MATLAB data files. Within each is cross-section data extracted from the Livox data between 18th–22th February 2022. Each file contains data from a given cross-section (b10, b14, b20, b24). In the research article these are referred to a T1-T4. b24 = T1, b20=T2, b14=T3, b10=T4. Data within these files was generated using 'section_analysis.m'.
