3D LoD2 modelling of Halong City based on UAV point cloud

  • Affiliations:

    1 Hanoi University of Mining and Geology, Hanoi, Vietnam
    2 Hanoi University of Natural Resources & Environment, Hanoi, Vietnam
    3 College of Electro - Mechanics, Construction and Agro - Forestry in Central, Binh Dinh, Vietnam
    4 Binh Tri Dong B Ward, Binh Tan District, Hochiminh, Vietnam
    5 Dong Hai Surveying Company Limited, Hochiminh, Vietnam

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  • Received: 30th-Dec-2023
  • Revised: 24th-Apr-2024
  • Accepted: 18th-July-2024
  • Online: 1st-Aug-2024
Pages: 1 - 8
Views: 1127
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Abstract:

3D urban building models are crucial for linking, converging, and integrating economic and social urban data. They are extensively utilized in numerous domains such as smart city development, comprehensive social management, and emergency decision-making. Advanced technologies like affordable UAV (Unmanned Aerial Systems) imagery enable a greater level of automation in data acquisition compared to traditional digital photogrammetry methods. The main objective of this research is to build a 3D LoD2 CityGML model with dense point clouds from UAV images. This study presents a completed workflow for generating 3D CityGML models of the city at LoD2 using UAV data. The use of dense point cloud data from UAV technology in the experimental area has been performed using a DJI Phantom 4 Pro. The original point clouds should be denoised using the statistical outlier remover (SOR), the main goal is to reduce noise while preserving the building's geometry. After that, the point clouds of object features were vectored for the level of detail 2 (LoD2) of the object's 3D volume corresponding to its actual height, and objects belonging to the Feature Class 3D layer will represent LoD2 on SketchUp Pro 2021 software to generate a highly accurate 3D model. The evaluation results show that the square errors calculated from the test points for the three axes X, Y, Z are 1.4 cm, 1.6 cm, 1.7 cm, respectively. Conducting research to choose the UAV photography method aims to offer an efficient and cost-effective solution, saving time and human resources, to address the 3D mapping challenges in urban areas across Vietnam.

How to Cite
Le, H.Thu Thi, Nguyen, N.Viet, Nguyen, T.Van, Nguyen, H.Le Thi, Phan, L.Thi, Pham, C.Dinh, Nguyen, H.Duy, Tran, H.Thanh, Nguyen, T.Thanh, Do, D.Trong, Dinh, T.Thanh and Nguyen, L.Huu 2024. 3D LoD2 modelling of Halong City based on UAV point cloud. Journal of Mining and Earth Sciences. 65, 4 (Aug, 2024), 1-8. DOI:https://doi.org/10.46326/JMES.2024.65(4).01.
References

Abdelazeem, M., Elamin, A., Afifi, A., El-Rabbany, A. (2021). Multi-sensor point cloud data fusion for precise 3D mapping. The Egyptian Journal of Remote Sensing and Space Sciences. 1-10.

Arnadi, M., Mirza, V., Deni, S., Budhy, S., Agung, B.H. (2020). Automatic Workflow for Roof Extraction and Generation of 3D CityGML Models from Low-Cost UAV Image-Derived Point Clouds. ISPRS Int. J. Geo-Inf., 9, 1-18.

Bui T.D., (2021). Constructing a three-dimensional model of Hanoi National University campus using handheld cameras and 3D modeling applications. Code: QC.05.02, 60 pages.

Biljecki, F., Ledoux, H., Stoter, J. (2014). Redefining the Level of Detail for 3D models. GIM International, 28(11): 21-23.

Biljecki, F., Ledoux, H., Stoter, J. (2016). An improved LOD specification for 3D building models. Comput. Environ.Urban Syst., 59, 25-37.

Biljecki, F. and Dehbi, Y. (2019). Raise the roof: Towards generating LOD2 models without aerial surveys using machine learning. ISPRS Ann. Photogramm. Remote. Sens. Spat. Inf. Sci. , IV-4/W8, 27-34.

Chhatkuli S., Satoh, T., Tachibana, K. (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, Indoor-Outdoor Seamless Modelling, Mapping and Navigation, 21-22 May 2015, Tokyo, Japan.

Dušan, J., Stevan, M., Igor, R., Miro, G., Dubravka, S., Aleksandra, R., Vladimir, P. (2020). Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad. ISPRS Int. J. Geo-Inf., 9, 1-24.

Dorninger, P. and Pfeifer, N. (2008). A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds. Sensors. 8(11), 7323-7343.

Gröger, G. and Plümer, L. (2012). CityGML—Interoperable Semantic 3D City Models. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 12-33.

Haala, N., and Brenner, C. (1997). Generation of 3D city models from airborne laser scanning data. In Proceedings EARSEL Workshop on LIDAR remote sensing on land and sea. Tallin, Estonia, 105-112.

La P. H. (2018). Applying certain open-source libraries to visually represent a 3D model of the city on the Web platform. Code: T18-13, 57 pages.

Zhao, Q., Zhou, L., Guonian, L. (2023). A 3D modeling method for buildings based on LiDAR point cloud and DLG. Computers, Environment and Urban Systems, 102.

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