Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data
Abstract
:1. Introduction
2. Research Objectives
- Improvement of the method for determining the axis of buildings represented by solids of revolution;
- Introduction of a new approach for the automatic generation of building cross-sections and a gap-filling strategy when a complete set of points is not available;
- Evaluation and interpretation of deviated data points (outliers) in the process of incorporating these data into the developed model.
3. Datasets
4. Method
4.1. Improve Vertical Cross-Section Point Cloud
4.2. Gap Analysis and Filling
4.3. Integrating Deviated Points in the Calculated Model
5. Discussion
5.1. Performance of the Method
- Undesirable distortions may appear in the constructed model when the input point cloud has inconsistent quality regarding the point density, distribution regularity, and homogeneity. Certain levels of balance may be desired that can comprise the data volume, level of details for presentation, and the accuracy of the model;
- Like many other methods, the developed method can only reconstruct buildings that meet certain assumptions, which in this case are rotating surfaces. Small attachments or decorations of the main surface need to be treated separately. A promising effort is to extend and/or integrate this method with other methods to handle complex and diverse buildings.
5.2. Modeling Accuracy
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Richa, J.P.; Deschaud, J.-E.; Goulette, F.; Dalmasso, N. AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-Time High-Fidelity LiDAR Simulation. Remote Sens. 2022, 14, 6262. [Google Scholar] [CrossRef]
- Beil, C.; Ruhdorfer, R.; Coduro, T.; Kolbe, T.H. Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model. ISPRS Int. J. Geo-Inf. 2020, 9, 603. [Google Scholar] [CrossRef]
- Biljecki, F.; Lim, J.; Crawford, J.; Moraru, D.; Tauscher, H.; Konde, A.; Adouane, K.; Lawrence, S.; Janssen, P.; Stouffs, R. Extending CityGML for IFC-sourced 3D city models. Autom. Constr. 2021, 121, 103440. [Google Scholar] [CrossRef]
- Jayaraj, P.; Ramiya, A.M. 3D CityGML building modelling from lidar point cloud data. In The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences; Gottingen Tom XLII-5; Copernicus GmbH: Gottingen, Germany, 2018; pp. 175–180. [Google Scholar] [CrossRef] [Green Version]
- Xu, Y.; Stilla, U. Towards Building and Civil Infrastructure Reconstruction From Point Clouds: A Review on Data and Key Techniques. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 2857–2885. [Google Scholar] [CrossRef]
- Tarsha Kurdi, F.; Awrangjeb, M.; Liew, A.W.-C. Automated Building Footprint and 3D Building Model Generation from Lidar Point Cloud Data. In Proceedings of the 2019 Digital Image Computing: Techniques and Applications (DICTA), Perth, Australia, 2–4 December 2019; pp. 1–8. [Google Scholar] [CrossRef]
- Tarsha Kurdi, F.; Gharineiat, Z.; Campbell, G.; Dey, E.K.; Awrangjeb, M. Full Series Algorithm of Automatic Building Extraction and Modelling from LiDAR Data. In Proceedings of the 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia, 29 November–1 December 2021; pp. 1–8. [Google Scholar] [CrossRef]
- Labetski, A.; Vitalis, S.; Biljecki, F.; Ohori, K.A.; Stoter, J. 3D building metrics for urban morphology. Int. J. Geogr. Inf. Sci. 2023, 37, 36–67. [Google Scholar] [CrossRef]
- Pfeifer, N.; Rutzinger, M.; Rottensteiner, F.; Muecke, W.; Hollaus, M. Extraction of Building Footprints from Airborne Laser Scanning: Comparison and Validation Techniques. In Proceedings of the Joint IEEE-GRSS/ISPRS Workshop on Remote Sensing and Data Fusion over Urban Areas, Urban 2007, Paris, France, 11–13 April 2007. [Google Scholar] [CrossRef]
- Wang, X.; Luo, Y.-P.; Jiang, T.; Gong, H.; Luo, S.; Zhang, X.-W. A New Classification Method for LIDAR Data Based on Unbalanced Support Vector Machine. In Proceedings of the 2011 International Symposium on Image and Data Fusion, Tengchong, China, 9–11 August 2011; pp. 1–4. [Google Scholar] [CrossRef]
- Chen, D.; Zhang, L.; Mathiopoulos, P.; Huang, X. A Methodology for Automated Segmentation and Reconstruction of Urban 3-D Buildings from ALS Point Clouds. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 4199–4217. [Google Scholar] [CrossRef]
- Sampath, A.; Shan, J. Building Boundary Tracing and Regularization from Airborne Lidar Point Clouds. Photogramm. Eng. Remote Sens. 2007, 73, 805–812. [Google Scholar] [CrossRef] [Green Version]
- Gilani, S.A.N.; Awrangjeb, M.; Lu, G. Segmentation of Airborne Point Cloud Data for Automatic Building Roof Extraction. GIScience Remote Sens. 2017, 55, 63–89. [Google Scholar] [CrossRef] [Green Version]
- Jung, J.; Sohn, G. Progressive modeling of 3D building rooftops from airborne Lidar and imagery. In Topographic Laser Ranging and Scanning: Principles and Processing, 2nd ed.; Shan, J., Toth, C.K., Eds.; Taylor & Francis Group; CRC Press: Boca Raton, FL, USA, 2018; pp. 523–562. Available online: https://www.taylorfrancis.com/chapters/edit/10.1201/9781315154381-17/progressive-modeling-3d-building-rooftops-airborne-lidar-imagery-jaewook-jung-gunho-sohn (accessed on 25 June 2023).
- Dey, E.K.; Tarsha Kurdi, F.; Awrangjeb, M.; Stantic, B. Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data. Remote Sens. 2021, 13, 1520. [Google Scholar] [CrossRef]
- Dong, Y.; Hou, M.; Xu, B.; Li, Y.; Ji, Y. Ming and Qing Dynasty Official-Style Architecture Roof Types Classification Based on the 3D Point Cloud. ISPRS Int. J. Geo-Inf. 2021, 10, 650. [Google Scholar] [CrossRef]
- Tarsha Kurdi, F.; Awrangjeb, M.; Munir, N. Automatic filtering and 2D modeling of LiDAR building point cloud. Trans. GIS 2020, 25, 164–188. [Google Scholar] [CrossRef]
- Mahphood, A.; Arefi, H. Density-based method for building detection from LiDAR point cloud. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2023, X-4/W1-2022, 423–428. [Google Scholar] [CrossRef]
- Park, S.-Y.; Lee, D.G.; Yoo, E.J.; Lee, D.-C. Segmentation of Lidar Data Using Multilevel Cube Code. J. Sens. 2019, 2019, 4098413. [Google Scholar] [CrossRef]
- Cheng, L.; Zhang, W.; Zhong, L.; Du, P.; Li, M. Framework for Evaluating Visual and Geometric Quality of Three-Dimensional Models. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 8, 1281–1294. [Google Scholar] [CrossRef]
- Ostrowski, W.; Pilarska, M.; Charyton, J.; Bakuła, K. Analysis of 3D building models accuracy based on the airborne laser scanning point clouds. In International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences; ISPRS: Vienna, Austria, 2018; p. 42. Available online: https://ui.adsabs.harvard.edu/link_gateway/2018ISPAr.422..797O/ (accessed on 25 June 2023). [CrossRef] [Green Version]
- Tarsha Kurdi, F.; Awrangjeb, M. Comparison of LiDAR Building Point Cloud with Reference Model for Deep Comprehension of Cloud Structure. Can. J. Remote Sens. 2020, 46, 603–621. [Google Scholar] [CrossRef]
- Tarsha Kurdi, F.; Gharineiat, Z.; Campbell, G.; Awrangjeb, M.; Dey, E.K. Automatic Filtering of Lidar Building Point Cloud in Case of Trees Associated to Building Roof. Remote Sens. 2022, 14, 430. [Google Scholar] [CrossRef]
- Gharineiat, Z.; Tarsha Kurdi, F.; Campbell, G. Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques. Remote Sens. 2022, 14, 4685. [Google Scholar] [CrossRef]
- Adeleke, A.K.; Smit, J.L. Building roof extraction as data for suitability analysis. Appl. Geomat. 2020, 12, 455–466. [Google Scholar] [CrossRef]
- Yang, W.; Liu, X.; Zhang, Y.; Wan, Y.; Ji, Z. Object-based building instance segmentation from airborne LiDAR point clouds. Int. J. Remote Sens. 2022, 43, 6783–6808. [Google Scholar] [CrossRef]
- Axel, C.; Van Aardt, J. Building damage assessment using airborne lidar. J. Appl. Remote Sens. 2017, 11, 046024. [Google Scholar] [CrossRef] [Green Version]
- Dorninger, P.; Pfeifer, N. A Comprehensive Automated 3D Approach for Building Extraction, Reconstruction, and Regularization from Airborne Laser Scanning Point Clouds. Sensors 2008, 8, 7323–7343. [Google Scholar] [CrossRef] [Green Version]
- Liu, X.; Zhang, Y.; Ling, X.; Wan, Y.; Liu, L.; Li, Q. TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives. Remote Sens. 2019, 11, 1372. [Google Scholar] [CrossRef] [Green Version]
- Li, Z.; Shan, J. RANSAC-based multi primitive building reconstruction from 3D point clouds. ISPRS J. Photogramm. Remote Sens. 2022, 185, 247–260. [Google Scholar] [CrossRef]
- Liu, X.; Zhu, X.; Zhang, Y.; Wang, S.; Jia, C. Generation of concise 3D building model from dense meshes by extracting and completing planar primitives. Photogramm. Rec. 2023, 38, 22–46. [Google Scholar] [CrossRef]
- Matikainen, L.; Hyyppä, J.; Hyyppä, H. Automatic detection of buildings from laser scanner data for map updating. In International Archives of the Photogrammetry and Remote Sensing, XXXIV, 3/W13; ISPRS: Dresden, Germany, 2003; Available online: https://www.isprs.org/proceedings/xxxiv/3-W13/papers/Matikainen_ALSDD2003.pdf (accessed on 25 June 2023).
- Vosselman, G.; Dijkman, S. 3D Building Model Reconstruction from Point Clouds and Ground Plans. In International Archives of the Photogrammetry and Remote Sensing, XXXIV, 3/W4; ISPRS: Annapolis, MA, USA, 2001; pp. 37–44. [Google Scholar]
- Wen, C.; Yang, L.; Li, X.; Peng, L.; Chi, T. Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification. ISPRS J. Photogramm. Remote Sens. 2020, 162, 50–62. [Google Scholar] [CrossRef] [Green Version]
- Maltezos, E.; Doulamis, A.; Doulamis, N.; Ioannidis, C. Building Extraction from LiDAR Data Applying Deep Convolutional Neural Networks. IEEE Geosci. Remote Sens. Lett. 2018, 16, 155–159. [Google Scholar] [CrossRef]
- Yuan, J. Learning Building Extraction in Aerial Scenes with Convolutional Networks. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 40, 2793–2798. [Google Scholar] [CrossRef] [PubMed]
- Kuras, A.; Brell, M.; Rizzi, J.; Burud, I. Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review. Remote Sens. 2021, 13, 3393. [Google Scholar] [CrossRef]
- Zhou, L.; Geng, J.; Jiang, W. Joint Classification of Hyperspectral and LiDAR Data Based on Position-Channel Cooperative Attention Network. Remote Sens. 2022, 14, 3247. [Google Scholar] [CrossRef]
- Pantoja-Rosero, B.; Achanta, R.; Kozinski, M.; Fua, P.; Perez-Cruz, F.; Beyer, K. Generating LOD3 building models from structure-from-motion and semantic segmentation. Autom. Constr. 2022, 141, 104430. [Google Scholar] [CrossRef]
- Fan, H.; Wang, Y.; Gong, J. Layout graph model for semantic façade reconstruction using laser point clouds. Geo. Spat. Inf. Sci. 2021, 24, 403–421. [Google Scholar] [CrossRef]
- Gui, S.; Qin, R. Automated LoD-2 model reconstruction from very-high-resolution satellite-derived digital surface model and orthophoto. ISPRS J. Photogramm. Remote Sens. 2021, 181, 1–19. [Google Scholar] [CrossRef]
- Peters, R.; Dukai, B.; Vitalis, S.; van Liempt, J.; Stoter, J. Automated 3D Reconstruction of LoD2 and LoD1 Models for All 10 Million Buildings of the Netherlands. Photogramm. Eng. Remote Sens. 2022, 88, 165–170. [Google Scholar] [CrossRef]
- Zhang, Z.; Qian, Z.; Zhong, T.; Chen, M.; Zhang, K.; Yang, Y.; Zhu, R.; Zhang, F.; Zhang, H.; Zhou, F.; et al. Vectorized rooftop area data for 90 cities in China. Sci. Data 2022, 9, 66. [Google Scholar] [CrossRef] [PubMed]
- Pang, H.E.; Biljecki, F. 3D building reconstruction from single street view images using deep learning. Int. J. Appl. Earth Obs. Geoinform. 2022, 112, 102859. [Google Scholar] [CrossRef]
- Lewandowicz, E.; Tarsha, K.F.; Gharineiat, Z. 3D LoD2 and LoD3 Modeling of Buildings with Ornamental Towers and Turrets Based on LiDAR Data. Remote Sens. 2022, 14, 4687. [Google Scholar] [CrossRef]
Building Number | Min CW (m) | Max CW (m) | Mean CW (m) | Min CH (m) | Max CH (m) | Mean CH (m) |
---|---|---|---|---|---|---|
1 | 0.01 | 1.36 | 0.81 | 0.01 | 0.20 | 0.02 |
2 | 0.01 | 4.55 | 2.79 | 0.01 | 0.20 | 0.01 |
3 | 0.02 | 0.66 | 0.40 | 0.01 | 0.20 | 0.07 |
4 | 0.01 | 0.88 | 0.53 | 0.01 | 0.20 | 0.08 |
5 | 0.01 | 0.49 | 0.26 | 0.01 | 0.20 | 0.04 |
6 | 0.01 | 1.33 | 0.75 | 0.01 | 0.14 | 0.02 |
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Tarsha Kurdi, F.; Lewandowicz, E.; Gharineiat, Z.; Shan, J. Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data. Remote Sens. 2023, 15, 3324. https://doi.org/10.3390/rs15133324
Tarsha Kurdi F, Lewandowicz E, Gharineiat Z, Shan J. Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data. Remote Sensing. 2023; 15(13):3324. https://doi.org/10.3390/rs15133324
Chicago/Turabian StyleTarsha Kurdi, Fayez, Elżbieta Lewandowicz, Zahra Gharineiat, and Jie Shan. 2023. "Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data" Remote Sensing 15, no. 13: 3324. https://doi.org/10.3390/rs15133324
APA StyleTarsha Kurdi, F., Lewandowicz, E., Gharineiat, Z., & Shan, J. (2023). Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data. Remote Sensing, 15(13), 3324. https://doi.org/10.3390/rs15133324