CityJSON Building Generation from Airborne LiDAR 3D Point Clouds
Abstract
:1. Introduction
2. Related Works
2.1. Building-Generation Methods
2.2. CityGML and CityJSON
3. Methodology
3.1. Introductory Comments
3.2. Point-Cloud Segmentation
3.3. Step-By-Step Geometric Modeling
3.3.1. Topology Generation
- O+ planes have normal vectors that when projected, are orthogonal and point away from each other.
- O- planes have normal vectors that when projected, are orthogonal and point towards each other.
- S+ planes have normal vectors that when projected, are parallel and point away from each other.
- N = no constraint.
- An adjacency matrix (i, j), which contains the ID of j planes connected to the i plane.
- A relationship matrix, which contains the nature of the connectivity between the i and j planes.
3.3.2. Roof Generation
3.3.3. Wall Generation
3.3.4. Building Assembly
3.4. CityJSON Model Building
4. Discussion
4.1. CityJSON Improvements
4.2. Format Compliance
4.3. Quality Control
4.3.1. Spatio-Semantic Evaluation
4.3.2. Geometric Evaluation
- Snap tolerance: 0.001 m—if two points are closer than this value, then they are assumed to be the same.
- Planarity tolerance: 0.05 m—the maximum distance between a point and a fitted plane.
- Overlap tolerance: 0.01 m—the tolerance used to validate adjacency between different solids.
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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City (Method) | Size | Buildings | Valid | Primitives | Valid |
---|---|---|---|---|---|
Berlin | 933 MB | 22.771 | 74% | 89.736 | 90% |
DenHaag | 22 MB | 844 | 61% | 1.990 | 85% |
Montréal | 125 MB | 581 | 76% | 1.744 | 88% |
NRW | 16 MB | 797 | 83% | 928 | 77% |
Theux (UR) | 689 KB | 420 | 92% | 1.198 | 97% |
Theux (RG) | 656 KB | 400 | 93% | 1053 | 96% |
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Nys, G.-A.; Poux, F.; Billen, R. CityJSON Building Generation from Airborne LiDAR 3D Point Clouds. ISPRS Int. J. Geo-Inf. 2020, 9, 521. https://doi.org/10.3390/ijgi9090521
Nys G-A, Poux F, Billen R. CityJSON Building Generation from Airborne LiDAR 3D Point Clouds. ISPRS International Journal of Geo-Information. 2020; 9(9):521. https://doi.org/10.3390/ijgi9090521
Chicago/Turabian StyleNys, Gilles-Antoine, Florent Poux, and Roland Billen. 2020. "CityJSON Building Generation from Airborne LiDAR 3D Point Clouds" ISPRS International Journal of Geo-Information 9, no. 9: 521. https://doi.org/10.3390/ijgi9090521
APA StyleNys, G.-A., Poux, F., & Billen, R. (2020). CityJSON Building Generation from Airborne LiDAR 3D Point Clouds. ISPRS International Journal of Geo-Information, 9(9), 521. https://doi.org/10.3390/ijgi9090521