3D LoD3 Modeling of High Building Using Terrestrial Laser Scanning and Unmanned Aerial Vehicle: A Case Study in Halong City, Vietnam

• Autorzy: Le Thi Thu Ha , Nguyen Quoc Long

Identyfikatory

DOI

10.29227/IM-2023-02-45

Warianty tytułu

PL

Modelowanie 3d LoD3 budynku wysokiego z wykorzystaniem naziemnego skanowania laserowego i dronów: studium przypadku z miasta Halong w Wietnamie

• Konferencja: POL-VIET 2023 — the 7th International Conference POL-VIET
• Języki publikacji: EN

Abstrakty

EN

3D urban building models play an important role in the association, convergence and integration of economic and social urban data. 3D building reconstruction can be done from both the lidar and image-based point clouds, however, the lidar point clouds has dominated the research giving the 3D buildings reconstruction from aerial images point clouds less attention. The UAV images can be acquired at low cost, the workflow can be automated with minimal technical knowhow limitation. This promotes the necessity to understand and question to what extent the 3D buildings from UAV point clouds are complete and correct from data processing to parameter settings. This study focuses on proposing a process for building 3D geospatial data for a smart city using geospatial data collected by UAV and Terrestrial Laser Scanner. The experimental results have produced 3D geospatial data of high building in LoD3, with the root mean square error of the received test points mΔx=3.8 cm, mΔy=3.1 cm, and mΔH=7.5 cm.

Słowa kluczowe

EN

3D LoD3 model of high building; Unmanned Aerial Vehicle (UAV); Terrestrial Laser Scanners (TLS)

PL

UAV; skaning laserowy; Wietnam

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2023
• Tom: no. 2, vol. 1
• Strony: 303--310
• Opis fizyczny: Bibliogr. 27 poz., tab.

Twórcy

autor

Le Thi Thu Ha

• Hanoi university of Mining and Geology, 18 Vien Str., Duc Thang Ward, Hanoi 100000, Vietnam
• Geomatics in Earth Sciences Research Group, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Hanoi 100000, Vietnam

autor

Nguyen Quoc Long

• Hanoi university of Mining and Geology, 18 Vien Str., Duc Thang Ward, Hanoi 100000, Vietnam
• Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Hanoi 100000, Vietnam

Bibliografia

• 1. Biljecki F, Ledoux H, Stoter J. 2014. Redefning the Level of Detail for 3D models. GIM International, 28(11): 21–23.
• 2. MLTM. 2009. 3D Spatial Information Construction for Ubiquitous National Administration, 2009.
• 3. Biljecki, F.; Ledoux, H.; Stoter, J. 2016. An improved LOD specification for 3D building models. Comput. Environ. Urban Syst. 59, 25–37.
• 4. M. Bouziani, H. Chaaba, M. Ettarid, 2021. Evaluation of 3D building model using terrestrial laser scanning and drone photogrammetry. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVI-4/W4-2021 16th 3D GeoInfo Conference 2021, 11–14 October 2021, New York City, USA.
• 5. Tack F, Buyuksalih G, Goossens R 2012. 3D building reconstruction based on given ground plan information and surface models extracted from spaceborne imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 67: 52–64.
• 6. Colomina I, Molina P. 2014. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92: 79–97.
• 7. Nex F, Remondino F. 2013. UAV for 3D mapping applications: a review. Applied Geomatics, 6(1): 1–15.
• 8. Böhm J, Brédif M, Gierlinger T, Krämer M, Lindenberg R, Liu K, Michel F, Sirmacek B. 2016. Te IQmulus urban showcase: automatic tree classifcation and identifcation in huge mobile mapping point clouds. Int. Arch. Photogramm.Remote Sens. Spatial Inf. Sci., XLI-B3: 301–307.
• 9. Kaartinen H, Hyyppä J, Kukko A, Jaakkola A, Hyyppä H. 2012. Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field. Sensors, 12(12): 12814–12835.
• 10. Früh C, Zakhor A. 2004. An Automated Method for Large-Scale, Ground-Based City Model Acquisition. International Journal of Computer Vision, 60(1): 5–24.
• 11. Rosser J, Morley J, Smith G. 2015. Modelling of Building Interiors with Mobile Phone Sensor Data. ISPRS International Journal of GeoInformation, 4(2): 989–1012.
• 12. Sirmacek B, Lindenbergh R. 2014. Accuracy assessment of building point clouds automatically generated from iphone images. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL- 5: 547–552
• 13. Hartmann W, Havlena M, Schindler K. 2016. Towards complete, geo-referenced 3D models from crowd-sourced amateur images. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3: 51–58.
• 14. Stilla U, Soergel U, Toennessen U. 2003. Potential and limits of InSAR data for building reconstruction in built-up areas. ISPRS Journal of Photogrammetry and Remote Sensing, 58(1-2): 113–123.
• 15. Shahzad M, Zhu XX. 2015. Robust Reconstruction of Building Facades for Large Areas Using Spaceborne TomoSAR Point Clouds. IEEE Transactions on Geoscience and Remote Sensing, 53(2): 752–769.
• 16. Fischer A, Kolbe TH, Lang F, Cremers AB, Förstner W, Plümer L, Steinhage V. 1998. Extracting Buildings from Aerial Images Using Hierarchical Aggregation in 2D and 3D. Computer Vision and Image Understanding, 72(2): 185– 203
• 17. Barnhart, T.B., Crosby, B.T. 2013. Comparing two methods of surface change detection on an evolving thermokarst using high-temporal-frequency terrestrial laser scanning, Selawik River. Alaska Rem. Sens. 5, 2813–2837.https://doi.org/10.3390/rs5062813
• 18. Erdélyi, J., Kopácˇik, A., Lipták, I., Kyrinovicˇ, P., 2017. Automation of point cloud processing to increase the deformation monitoring accuracy. Appl. Geomat. 9 (2), 105–113. https://doi.org/10.1007/s12518-017-0186-y.
• 19. Fan, J., Wang, Q., Liu, G., Zhang, L.u., Guo, Z., Tong, L., Peng, J., Yuan, W., Zhou,W., Yan, J., Perski, Z., Sousa, J., 2019. Monitoring and analyzing mountain glacier surface movement using SAR data and a terrestrial laser scanner: a case study of the Himalayas North Slope Glacier Area. Rem. Sens. 11 (6), 625. https://doi.org/10.3390/rs11060625.
• 20. Xu, Z., Xu, E., Wu, L., Liu, S., Mao, Y., 2019. Registration of terrestrial laser scanning surveys using terrain-invariant regions for measuring exploitative volumes over open-pit mines. Rem. Sens. 11 (6), 606. https://doi.org/10.3390/rs11060606
• 21. Harmening, C., Neuner, H., 2020. A spatio-temporal deformation model for laser scanning point clouds. J. Geod. 94, 1–25. https://doi.org/10.1007/s00190-020-01352-0.
• 22. Whitehead, K., Hugenholtz, C.H., 2014. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges. J. Unmanned Vehicle Syst. 02, 69–85. doi:10.1139/juvs-2014-0006
• 23. Sayab, M., Aerden, D., Paananen, M., Saarela, P., 2018. Virtual structural analysis of Jokisivu open pit using ’structure-from-motion’ Unmanned Aerial Vehicles (UAV) photogrammetry: Implications for structurally-controlled gold deposits in Southwest Finland. Rem. Sens. 10, 1–17. https://doi.org/10.3390/rs10081296.
• 24. Chakra, C., Gascoin, S., Somma, J., Fanise, P., Drapeau, L., 2019. Monitoring the snowpack volume in a sinkhole on mount Lebanon using time lapse photogrammetry. Sensors (Switzerland) 19 (18), 3890. https://doi.org/10.3390/s19183890.
• 25. Díaz, G.M., Mohr-Bell, D., Garrett, M., Muñoz, L., Lencinas, J.D., 2020. Customizing unmanned aircraft systems to reduce forest inventory costs: can oblique images substantially improve the 3D reconstruction of the canopy? Int. J. Rem. Sens. 41 (9), 3480–3510. https://doi.org/10.1080/01431161.2019.1706200.
• 26. S. Chhatkuli, T. Satoh, K. Tachibana, 2015. Multi sensor data integration for an accurate 3D model generation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-4/W5, 2015 Indoor-Outdoor Seamless Modelling, Mapping and Navigation, 21–22 May 2015, Tokyo, Japan.
• 27. Allysa Mat Adnan, Norhadija Darwin, Mohd Farid Mohd Ariff, Zulkepli Majid, Khairulnizam M Idris, 2019. Integration between unmanned aerial vehicle and terrestrial laser scanner in producing 3d model. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W16, 2019 6th International Conference on Geomatics and Geospatial Technology (GGT 2019), 1–3 October 2019, Kuala Lumpur, Malaysia.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)