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Performance evaluation of different selected UAV image processing software on building volume estimation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of this research is to evaluate the performance of four UAV image processing software for the automatic estimation of volumes based on estimated volume accuracy, spatial accuracy, and execution time, with and without Ground Control Points (GCPs). A total of 52 images of a building were captured using a DJI Mavic Air UAV at 60m altitude and 80% forward and side overlap. The dataset was processed with and without GCPs using Pix4DMapper, Agisoft Metashape Pro, Reality Capture, and 3DF Zephyr. The UAV-based estimated volume generated from the software was compared with the true volume of the building generated from its as-built 3D building information modeled in Revit 2018 environment. The resulting percentage difference was computed. The average volumes estimated from the four software with the use of GCPs were 4757.448 m3 (3.87%), 4728.1 m3 (2.54%), 4291.561 m3 (11.5%), and 4154.938 m3 (14.35%), respectively. Similarly, when GCPs were not used for the image processing, average volumes of 4631.385 m3 (4.52%), 4773.025 m3 (1.6%), 4617.899 m3 (4.89%), and 4420.403 m3 (8.92%) were obtained in the same order. In addition to the volume estimation analysis, other parameters, including execution time, positional RMSE, and spatial resolution, were evaluated. Based on these parameters, Agisoft Metashape Pro proved to be more accurate, time-efficient, and reliable for volumetric estimations from UAV images compared to the other investigated software. The findings of this study can guide decision-making in selecting the appropriate software for UAV-based volume estimation in different applications.
Rocznik
Strony
art. no. e39, 2023
Opis fizyczny
Bibliogr. 30 poz., rys., tab., wykr.
Twórcy
  • Namibia University of Science and Technology, Windhoek, Namibia
  • Federal University of Technology, Minna, Nigeria
  • Federal University of Technology, Minna, Nigeria
  • Federal University of Technology, Minna, Nigeria
Bibliografia
  • 1. 3DF Zephyr (2021). 3DF Zephyr User Manual. Retrieved August 29, 2021 from http://www.3dflow.net/zephyr-doc/en/Theboundingbox.html.
  • 2. Ab-Rahman, A.A., Abdul-Maulud, K.N., Mohd, F.A. et al. (2017). Volumetric calculation using a low-cost unmanned aerial vehicle (UAV) approach. In IOP Conf. Series: Materials Science and Engineering, 3–5.
  • 3. Agisoft (2016). Agisoft forum. Retrieved August 25, 2021 from https://www.agisoft.com/forum/index.php?topic=6268.0;prev_next=prev{#}new.
  • 4. Agisoft (2021). Agisoft metashape User Manual, 96. Retrieved August 25, 2021 from https://www.agisoft.com.
  • 5. Ajayi, O.G, Palmer, M., and Salubi, AA. (2018). Modelling farmland topography for suitable site selection of dam construction using unmanned aerial vehicle (UAV) photogrammetry. Remote Sens. Appl.: Soc. Environ., 220–230. DOI: 10.1016/j.rsase.2018.07.007.
  • 6. Ajayi, O.G., and Palmer, M. (2020). Modelling 3D Topography by comparing airborne LiDAR data with Unmanned Aerial System (UAV) photogrammetry under multiple imaging conditions. Geoplanning: Journal of Geomatics and Planning, 6(2) 123–138. DOI: 10.14710/geoplanning.6.2.122-138.
  • 7. Ajeeth, C. (2015). Aerial 3D imaging and monitoring of quarries with small drones. In the United Arab Emirates Ministry of Environment and Water.
  • 8. Akwaowo, U.E, Aniekan, E.E., and Okon, U. (2019). A comparative analysis of volumetric stockpile from UAV photogrammetry and total station data. SSRG Int. J. Geoinf. Geolog. Sci., 6(2).
  • 9. Baiocchi, V., Dominici, D., and Mormile M. (2013). UAV application in the post-seismic environment. ISPRS Archives, 21–25. DOI: 10.5194/isprsarchives-XL-1-W2-21-2013.
  • 10. CIOB (2018). BIM for construction. Retrieved August 27, 2021 from https://www.ciob.org/bim-construc tion.
  • 11. Datumate (2017). Stockpile Volume calculation without Ground Control Points. Datumate: Construction Dana Analytics.
  • 12. Hirschmugl, M., Flatscher, T., Gikovski, L. et al. (2020). Unmanned Aerial Vehicle-based photogrammetry for construction planning and monitoring. J. Comput. Civ. Eng., 34(5), 04020052. DOI: 10.1061/(asce)cp.1943-5487.0000953.
  • 13. Höhle, J. (2008). Photogrammetric measurements in oblique aerial images. Photogrammetrie, Fernerkundung, Geoinformation, 1, 7-14.
  • 14. Jones, K. (2019). How Technology Is Reshaping the Construction Industry. Retrieved August 25, 2021 from http://www.constructconnect.com/blog/construction-technology-reshaping-construction-industry/.
  • 15. Kim, S., Kim, J., and Lee, D. (2019). Unmanned Aerial Vehicle for building inspection and maintenance. Sustainability, 11(16), 4389. DOI: 10.3390/su11164389.
  • 16. Kuo, Y.-H., and Chen, C.-Y. (2019). Accuracy comparison of LiDAR and photogrammetry-based software for building volume estimation using images. Remote Sens., 11(17), 2024. DOI: 10.3390/rs11172024.
  • 17. Peterson, F., Hartmann, T., Fruchter, R. et al. (2011). Teaching construction project management with BIM support: Experience and lessons learned.
  • 18. Pix4D (2021). Pix4dmapper. Retrieved August 29, 2021 from https://www.pix4d.com/product/pix4dmapper- photogrammetry-software.
  • 19. Pix4DSupport (2020). How to improve the outputs of dense vegetation areas? Retrieved August 29, 2021 from https://support.pix4d.com/hc/en-us/articles/202560159-How-to-improve-the-outputs-of-dense-vegetation-areas.
  • 20. Propeller (2019). How to stockpile volume measurement works in drone surveying with a propeller.
  • 21. Retrieved August 30, 2021 from www.propelleraero.com/blog/how-stockpile-volume-measurement-works-in-drone-surveying.
  • 22. Raeva, P.L., Filipova, S.L., and Filipov, D.G. (2016). Volume computation of a stockpile – A study case comparing GPS and UAV measurements in an open pit quarry. ISPRS Archives, 12. DOI: 10.5194/isprs-archives-XLI-B1-999-2016.
  • 23. RealityCapture (2020). Reality Capture User Manual. 10. Retrieved August 29, 2021 from RevitSupport (2021). RevitSupport. Retrieved August 30, 2021 from https://knowledge.autodesk.com/support/revit/learn-explore/caas/CloudHelp/cloudhelp/2019/ENU/Revit-Model/files/GUID-32EEA5DC-381A-4BCF-A898-F15E98BFC63F-htm.html.
  • 24. Stalin, L.J., and Gnanaprakasam, R.P.C. (2017). Volume Calculation from UAV-based DEM. Int. J. Eng. Adv. Technol., 127. DOI: 10.17577/IJERTV6IS060076.
  • 25. Szeliski, R. (2010). Algorithms and applications. Springer Science & Business Media.
  • 26. Volk, R., Stengel, J., and Schultmann, F. (2014). Building Information Modeling (BIM) for existing buildings Literature review and future need. Automation in Construction, 109.
  • 27. Wu, B., Yao, X., and Guo, H. (2019). A novel method for building volume estimation from UAV images without ground control points. Remote Sens., 11(15), 1819. DOI: 10.3390/rs13152849.
  • 28. Yan, H., Ren, C., Wang, W. et al. (2021). Building volumetric analysis based on Unmanned Aerial Vehicles images and learning. Remote Sens., 13(2), 261. DOI: 10.3390/rs13020261.
  • 29. Yuan, Y., Zhang, C., Liu, H. et al. (2018). Building volume estimation from UAV images without ground control points: A comparative study of structure-from-motion and stereo matching. Remote Sens., 10(2), 303. DOI: 10.3390/rs10020303.
  • 30. Zhang, H., Lu, X., and Chen, Y. (2019). A comprehensive study of building volume estimation from UAV images without ground control points. Int. J. Remote Sens., 40(10), 3915–3934. DOI: 0.1080/01431161.2018.1565912.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0e604727-0a6c-4594-b566-1f41aaf48fcd
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