Authors

Affiliations

1 Hanoi University of Mining and Geology, Hanoi, Vietnam; lethithuha@humg.edu.vn

2 Geomatics in Earth Sciences Research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam; lethithuha@humg.edu.vn

*Corresponding author: lethithuha@humg.edu.vn; Tel.: +84–983115967

Abstracts

Three-dimensional building models play a crucial role in urban planning, emergency response, disaster management, and decision-making. Multi-sensor data fusion has recently attracted significant interest in the Geomatics research community. This approach addresses the limitations of individual sensors, allowing for the creation of comprehensive 3D models of structures and improving object classification. This study focuses on developing approaches that combine various geospatial technologies to produce a complete 3D model of common urban architectures, including high building, neighboring villa, and individual home. This research used flexibly employ UAV aerial imagery, ground photography, and terrestrial LiDAR scanning to collect the necessary information for constructing complete 3D models of each characteristic urban structure. Different point cloud datasets will be processed, merged, and used to generate the competely 3D models. The experimental results have produced complete 3D models with accuracy achieved with Δx; mΔy; mΔz all below 10 cm for the experimental buildings. With the accuracy of the 3D models, it is entirely possible to achieve accuracy in horizontal plane and height for the 1:500 scale terrain map.

Keywords

Cite this paper

Ha, L.T.T. Multi-sensor points cloud data fusion for 3D building models: A case study in Halong City, VietnamJ. Hydro-Meteorol. 202420, 24-36.

References

1. Bouziani, M.; Chaaba, H.; Ettarid, M. Evaluation of 3D building model using terrestrial laser scanning and drone photogrammetry. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2021, XLVI-4/W4-2021, 39–42.

2. Nex, F.; Remondino, F. UAV for 3D mapping applications: a review. Appl. Geomatics 2013, 6(1), 1–15.

3. Böhm, J.; Brédif, M.; Gierlinger, T.; Krämer, M.; Lindenberg, R.; Liu, K.; Michel, F.; Sirmacek, B. Te IQmulus urban showcase: automatic tree classifcation and identifcation in huge mobile mapping point clouds. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2016, XLI-B3, 301–307.

4. Kaartinen, H.; Hyyppä, J.; Kukko, A.; Jaakkola, A.; Hyyppä, H. Benchmarking the performance of mobile laser scanning systems using a permanent test field. Sensors 2012, 12(12), 12814–12835.

5. Früh, C.; Zakhor, A. An automated method for large-scale, ground-based city model acquisition. Int. J. Comput. Vision 2004, 60(1), 5–24.

6. Tack, F.; Buyuksalih, G.; Goossens, R. 3D building reconstruction based on given ground plan information and surface models extracted from spaceborne imagery. ISPRS J. Photogramm. Remote Sens. 2012, 67, 52–64.

7. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79–97.

8. Fischer, A.; Kolbe, T.H.; Lang, F.; Cremers, A.B.; Förstner, W.; Plümer, L.; Steinhage, V. Extracting buildings from aerial images using hierarchical aggregation in 2D and 3D. Comput. Vision Image Understanding 1998, 72(2), 185–203.

9. Stilla, U.; Soergel, U.; Toennessen, U. Potential and limits of InSAR data for building reconstruction in built-up areas. ISPRS J. Photogramm. Remote Sens. 2003, 58(1-2), 113–123.

10. Wang, Y.; Huang, X.; Gao, M. 3D model of building based on multi-source data fusion. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2022, XLVIII-3/W2-2022, 73–78. https://doi.org/10.5194/isprs-archives-XLVIII-3-W2-2022-73-2022.

11. Whitehead, K.; Hugenholtz, C.H. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: A review of progress and challenges. J. Unmanned Vehicle Syst. 2014, 2, 69–85. https://doi.org/10.1139/juvs-2014-0006.

12. Ha, L.T.T.; Long, N.Q. Combination of UAV image and terrestrial photogrammetry to build 3D geospatial data for smart cities. J. Hydro-Meteorol. 2013, 749, 21–31. https://doi.org/10.36335/VNJHM.2023(749).21-31.

13. Barnhart, T.B.; Crosby, B.T. Comparing two methods of surface change detection on an evolving thermokarst using high-temporal-frequency terrestrial laser scanning, Selawik River. Alaska Rem. Sens. 2013, 5, 2813–2837. https://doi.org/10.3390/rs5062813.

14. Erdélyi, J.; Kopácˇik, A.; Lipták, I.; Kyrinovicˇ, P.; Automation of point cloud processing to increase the deformation monitoring accuracy. Appl. Geomat. 2017, 9(2), 105–113. https://doi.org/10.1007/s12518-017-0186-y.

15. 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. 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. 2019, 11(6), 625. https://doi.org/10.3390/rs11060625.

16. Xu, Z.; Xu, E.; Wu, L.; Liu, S.; Mao, Y. Registration of terrestrial laser scanning surveys using terrain-invariant regions for measuring exploitative volumes over open-pit mines. Rem. Sens. 2019, 11(6), 606. https://doi.org/10.3390/rs11060606.

17. Harmening, C.; Neuner, H. A spatio-temporal deformation model for laser scanning point clouds. J. Geod. 2020, 94, 1–25. https://doi.org/10.1007/s00190-020-
01352-0.

18. Whitehead, K.; Hugenholtz, C.H. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: A review of progress and challenges. J. Unmanned Vehicle Syst. 2014, 2, 69–85. https://doi.org/10.1139/juvs-2014-0006.

19. Sayab, M.; Aerden, D.; Paananen, M.; Saarela, P. 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. 2018, 10, 1–17. https://doi.org/10.3390/rs10081296.

20. Chhatkuli, S.; Satoh, T.; Tachibana, K. Multi sensor data integration for an accurate 3D model generation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2015, XL-4/W5, 103–106. https://doi.org/10.5194/isprsarchives-XL-4-W5-103-2015.

21. Hannes, P.; Martin, S.; Henri, E. A 3-D model of castle Landenberg (CH) from combined photogrammetric processing of terrestrial and UAV based images. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2008, 37, 93–98.

22. Long, V.P.; et al. Flying and taking photos with an unmanned aerial vehicle (UAV) creates a 3-dimensional (3D) spatial map. J. Surv. Mapp. Sci. 2017, 31, 23–28.

23. Duy, B.T. Constructing a three-dimensional model of Hanoi National University using handheld cameras and its applications in this three-dimensional model, Project code: QC.05.02. Summary report of the national university-level scientific research project conducted by the University of Technology and Management. 2011, pp. 60.

24. Hu, Z. ICP algorithm for 3D surface registration. Highlights Sci. Eng. Technol. 2022, 24, 94–98.