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Quality assessment of 3D point cloud of industrial buildings from imagery acquired by oblique and nadir UAV flights

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Category: Content №5 2021

Last Updated on 29 October 2021

Published on 30 November -0001

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Authors:

Cao Xuan Cuong, orcid.org/0000-0002-7405-9668, Hanoi University of Mining and Geology, Hanoi, the Socialist Republic of Vietnam, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Le Van Canh, orcid.org/0000-0002-8113-9949, Hanoi University of Mining and Geology, Hanoi, the Socialist Republic of Vietnam, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Pham Van Chung, orcid.org/0000-0002-6446-7860, Hanoi University of Mining and Geology, Hanoi, the Socialist Republic of Vietnam, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Le Duc Tinh, orcid.org/0000-0002-0022-3453, Hanoi University of Mining and Geology, Hanoi, the Socialist Republic of Vietnam, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Pham Trung Dung, orcid.org/0000-0002-9474-3723, Hanoi University of Mining and Geology, Hanoi, the Socialist Republic of Vietnam, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ngo Sy Cuong, orcid.org/0000-0002-9466-7564, Vietnam Natural Resources and Environment Corporation, Hanoi, the Socialist Republic of Vietnam

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2021, (5): 131 - 139

https://doi.org/10.33271/nvngu/2021-5/131

Abstract:

Purpose. The main objective of this paper is to assess the quality of the 3D model of industrial buildings generated from Unmanned Aerial Vehicle (UAV) imagery datasets, including nadir (N), oblique (O), and Nadir and Oblique (N+O) UAV datasets.

Methodology. The quality of a 3D model is defined by the accuracy and density of point clouds created from UAV images. For this purpose, the UAV was deployed to acquire images with both O and N flight modes over an industrial mining area containing a mine shaft tower, factory housing and office buildings. The quality assessment was conducted for the 3D point cloud model of three main objects such as roofs, facades, and ground surfaces using CheckPoints (CPs) and terrestrial laser scanning (TLS) point clouds as the reference datasets. The Root Mean Square Errors (RMSE) were calculated using CP coordinates, and cloud to cloud distances were computed using TLS point clouds, which were used for the accuracy assessment.

Findings. The results showed that the point cloud model generated by the N flight mode was the most accurate but least dense, whereas that of the O mode was the least accurate but most detailed level in comparison with the others. Also, the combination of O and N datasets takes advantages of individual mode as the point clouds accuracy is higher than that of case O, and its density is much higher than that of case N. Therefore, it is optimal to build exceptional accurate and dense point clouds of buildings.

Originality. The paper provides a comparative analysis in quality of point cloud of roofs and facades generated from UAV photogrammetry for mining industrial buildings.

Practical value. Findings of the study can be used as references for both UAV survey practices and applications of UAV point cloud. The paper provides useful information for making UAV flight planning, or which UAV points should be integrated into TLS points to have the best point cloud.

Keywords: UAV, Oblique, Nadir, 3D modelling, terrestrial laser scanning, quality assessment

References.

1. Cilek, A., Donmez, C., & nal, M. (2020). Generation of High-Resolution 3-D Maps for Landscape Planning and Design Using UAV Technologies. Journal of Digital Landscape Architecture, 5(1), 275-284.

2. La, H.P. (2019). Webbased visualization of 3D city model using open source tools for urban planning (in Vietnamese). Journal of Mining and Earth Sciences, 60(2), 77-87.

3. Kalinichenko, V., Dolgikh, O., Dolgikh, L., & Pysmennyi, S. (2020). Choosing a camera for mine surveying of mining enterprise facilities using unmanned aerial vehicles. Mining of Mineral Deposits, 14(4), 31-39. https://doi.org/10.33271/mining14.04.031.

4. Urech, P.R.W., Dissegna, M.A., Girot, C., & Grt-Regamey, A. (2020). Point cloud modeling as a bridge between landscape design and planning. Landscape and Urban Planning, 203, 103903. https://doi.org/10.1016/j.landurbplan.2020.103903.

5. Van anh, L., Xuan Cuong, C., Quoc Long, N., Thi Thu Ha, L., Trung Anh, T., & Bui, X.N. (2020). Experimental Investigation on the Performance of DJI Phantom 4 RTK in the PPK Mode for 3D Mapping Open-Pit Mines. Inynieria Mineralna, 1(2), 65-74. https://doi.org/10.29227/IM-2020-02-10.

6. Long, N., Nam, B., Cuong, C., & Canh, L. (2019). An approach of mapping quarries in Vietnam using low-cost Unmanned Aerial Vehicles. Sustainable Development of Mountain Territories, 11(2), 199-210. https://doi.org/10.21177/1998-4502-2019-11-2-199-210.

7. Long, N.Q., Buczek, M.M., Hien, L.P., Szlapiska, S.A., Nam,B.X., Nghia, N.V., & Cuong, C.X. (2018). Accuracy assessment of mine walls surface models derived from terrestrial laser scanning. International Journal of Coal Science & Technology, 5(3), 328-338. https://doi.org/10.1007/s40789-018-0218-1.

8. Nguyen, L.Q. (2021). Accuracy assessment of open pit mines digital surface models generated using photos captured by Unmanned Aerial Vehicles in the post - processing kinematic mode. Journal of Mining and Earth Sciences, 62(4), 38-47. https://doi.org/10.46326/JMES.2021.62(4).05.

9. Nguyen, Q.L., Le, T.T.H., Tong, S.S., & Kim, T.T.H. (2020). UAV Photogrammetry-Based For Open Pit Coal Mine Large Scale Mapping, Case Studies In Cam Pha City, Vietnam. Sustainable Development of Mountain Territories, 12(4), 501-509. https://doi.org/10.21177/1998-4502-2020-12-4-501-509.

10. Nguyen, Q.L., Ropesh, G., Bui, K.L., Le, V.C., Cao, X.C., Pham, V.C., Bui, N.Q., & Xuan-Nam, B. (2020). Inuence of Flight Height on The Accuracy of UAV Derived Digital Elevation Model at Complex Terrain. Inynieria Mineralna, 1(45), 179-186. https://doi.org/10.29227/IM- 2020-01-27.

11. Nguyen, Q.L., Ropesh, G., Bui, K.L, Cao, X.C., Le, V.C., Nguyen, Q.M., & Xuan-Nam, B. (2021). Optimal Choice of the Number of Ground Control Points for Developing Precise DSM Using Light-Weight UAV in Small and Medium-Sized Open-Pit Mine. Archives of Mining Sciences, 66(3), 369-384. https://doi.org/10.24425/ams.2021.138594.

12. Nesbit, P.R., & Hugenholtz, C.H. (2019). Enhancing UAVSfM 3D Model Accuracy in High-Relief Landscapes by Incorporating Oblique Images. Remote Sensing, 11(3), 239. https://doi.org/10.3390/rs11030239.

13. Vacca, G., Dess, A., & Sacco, A. (2017). The Use of Nadir and Oblique UAV Images for Building Knowledge. ISPRS International Journal of Geo-Information, 6(12), 393. https://doi.org/10.3390/ijgi6120393.

14. Aicardi, I., Chiabrando, F., Grasso, N., Lingua, A.M., Noardo,F., & Span, A. (2016). UAV photogrammetry with oblique images: first analysis on data acquisition and processing. International archives of the photogrammetry, remote sensing and spatial information sciences, XLI-B1, 835-842. https://doi.org/10.5194/isprsarchives-xli-b1-835-2016.

15. Bemis, S.P., Micklethwaite, S., Turner, D., James, M.R., Akciz,S., Thiele, S.T., & Bangash, H.A. (2014). Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology. Journal of Structural Geology, 69, 163-178. https://doi.org/10.1016/j.jsg.2014.10.007.

16. Markelin, L., Honkavaara, E., Nsi, R., Nurminen, K., & Hakala,T. (2014). Geometric processing workflow for vertical and oblique hyperspectral frame images collected using UAV. International archives of the photogrammetry, remote sensing and spatial information sciences, XL-3, 205-210. https://doi.org/10.5194/isprsarchives-xl-3-205-2014.

17. Harwin, S., Lucieer, A., & Osborn, J. (2015). The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis. Remote Sensing, 7(9), 11933-11953. https://doi.org/10.3390/rs70911933.

18. Lingua, A., Noardo, F., Span, A., Sanna, S., & Matrone, F. (2017). 3D model generation using oblique images acquired by UAV. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W2, 107-115. https://doi.org/10.5194/isprs-archives-xlii-4-w2-107-2017.

19. Russo, M., Carnevali, L., Russo, V., Savastano, D., & Taddia, Y. (2019). Modeling and deterioration mapping of faades in historical urban context by close-range ultra-lightweight UAVs photogrammetry. International journal of architectural heritage, 13(4), 549-568. https://doi.org/10.1080/15583058.2018.1440030.

20. Pepe, M., Fregonese, L., & Crocetto, N. (2019). Use of SfM-MVS approach to nadir and oblique images generated through aerial cameras to build 2.5D map and 3D models in urban areas. Geocarto International, 1-22. https://doi.org/10.1080/10106049.2019.1700558.

21. Marcisz, M., Probierz, K., & Ostrowska-ach, M. (2018). 3D representation of geological observations in underground mine workings of the Upper Silesian Coal Basin. Journal of Sustainable Mining, 17(1), 34-39. https://doi.org/10.1016/j.jsm.2018.01.001.

22. Lowe, D.G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), 1-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.

23. Furukawa, Y., & Ponce, J. (2010). Accurate, Dense, and Robust Multiview Stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1362-1376. https://doi.org/10.1109/TPAMI.2009.161.

24. Furukawa, Y., Curless, B., Seitz, S.M., & Szeliski, R. (2010). Towards Internet-scale multi-view stereo. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1434-1441. https://doi.org/10.1109/CVPR.2010.5539802.

25. Shi, X., Liu, T., & Han, X. (2020). Improved Iterative Closest Point (ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration. International Journal of Remote Sensing, 41(8), 3197-3220. https://doi.org/10.1080/01431161.2019.1701211.

26. Rossi, P., Mancini, F., Dubbini, M., Mazzone, F., & Capra, A. (2017). Combining nadir and oblique UAV imagery to reconstruct quarry topography: methodology and feasibility analysis. European Journal of Remote Sensing, 50(1), 211-221. https://doi.org/10.1080/22797254.2017.1313097.

27. Rittersbacher, A., Buckley, S.J., Howell, J.A., Hampson, G.J., & Vallet, J. (2014). Helicopter-based laser scanning: a method for quantitative analysis of large-scale sedimentary architecture. Geological Society, London, 185-202. https://doi.org/http://dx.doi.org/10.1144/SP387.3/.

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