Combining terrestrial scanned datasets with UAV point clouds for mining operations

Fotheringham, Megan and Paudyal, Dev Raj ORCID: https://orcid.org/0000-0003-1010-2118 (2021) Combining terrestrial scanned datasets with UAV point clouds for mining operations. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-4-2021. pp. 129-138. ISSN 2194-9042

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Abstract

Surveyors of open cut mining operations employ multiple data acquisition techniques such as the use of Unmanned Aerial Vehicles (UAV), Terrestrial Laser Scanning (TLS) and GNSS positioning for creating 3D surface models. Surveyors, mine planners and geologists are increasingly combining point cloud datasets to achieve more detailed surface models for the use of material reconciliation and volume calculations. Terrestrial Laser Scanning and UAV photogrammetry have enabled large, accurate and time effective data collection and increased computing capacity enables geospatial professionals to create 3D virtual surfaces, through merging UAV point clouds and TLS data combing with GNSS positioning. This research paper investigates the effects of combining data sets for creating 3D surface models from independent spatial data collection methods such as UAV, TLS and GNSS and assess their accuracy for the purpose of volume calculations in mining operation. 3D surface models provide important information for mining operations, planning of resources, material volumes calculation and financial calculations. A case study of two rehabilitation mine sites in Northern Victoria, Australia was selected for this study. Field data were collected using Terrestrial Laser Scanner and UAV. After each dataset was processed and filtered, the data were merged to create surface models. The accuracy of the combined model was assessed comparing height (Z) values using a fishnet point grid of the surfaces. Volumes between surfaces were calculated, and a cost applied to the results based on the current bulk cubic meter (BCM) haulage rates. The outputs from this study will provide scientific contributions to civil and mining industries where the computation of stockpile values is required.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Date Deposited: 24 Aug 2021 06:33
Last Modified: 24 Aug 2021 06:33
Uncontrolled Keywords: unmanned aerial vehicles (UAV), terrestrial laser scanning, spatial data integration, digital surface models, point cloud datasets, mining operations
Fields of Research (2008): 09 Engineering > 0909 Geomatic Engineering > 090906 Surveying (incl. Hydrographic Surveying)
09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
Fields of Research (2020): 40 ENGINEERING > 4013 Geomatic engineering > 401306 Surveying (incl. hydrographic surveying)
40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
D Environment > 96 Environment > 9612 Rehabilitation of Degraded Environments > 961205 Rehabilitation of Degraded Mining Environments
E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering
18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180604 Rehabilitation or conservation of terrestrial environments
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
Identification Number or DOI: https://doi.org/10.5194/isprs-annals-V-4-2021-129-2021
URI: http://eprints.usq.edu.au/id/eprint/43417

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