Page 655 - NGTU_paper_withoutVideo

Modern Geomatics Technologies and Applications

The proposed method was evaluated in an urban area which includes various objects such as densely trees, cars, traffic
signs with various shapes and sizes. Among of these challengeable objects, we were capable of detecting buildings with more
than 98% accuracy. This in itself shows high performance of the proposed algorithm and robustness of it. Notably, the
algorithm detected and modeled more than 500 buildings that a great kind of them were available at the selected area.

5. Conclusion and Future works
This paper presented an approach for building modeling from ALS and MLS point clouds which at the first divides the
data into smaller sections, then removing noisy and ground points from each section. DSM is used to recognize location of
buildings by roofs. Ransac algorithm is utilized to detect and segment walls. The results showed accuracy in buildings
modeling.

6. References
1. Verma, V., R. Kumar, and S. Hsu. 3d building detection and modeling from aerial lidar data. in 2006 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition (CVPR'06). 2006. IEEE.
2. Ochmann, S., R. Vock, and R. Klein, Automatic reconstruction of fully volumetric 3D building models from oriented
point clouds. ISPRS journal of photogrammetry and remote sensing, 2019. 151: p. 251-262.
3. Shokri, D., et al., Utility Poles Etraction from Mobile LiDAR Data in Urban Area Basd on Density Information. Int.
Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 2019. XLII-4/W18: p. 1001-1007.
4. Brown, P.E., Y. Kanza, and V. Kounev. Height and Facet Extraction from LiDAR Point Cloud for Automatic
Creation of 3D Building Models. in Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in
Geographic Information Systems. 2019.
5. Rastiveis, H., et al., Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy
inference. ISPRS Journal of Photogrammetry and Remote Sensing, 2020. 160: p. 149-166.
6. Khodaverdi, N., H. Rastiveis, and A. Jouybari, Combination of Post-Earthquake LiDAR Data and Satellite Imagery
for Buildings Damage detection. Earth Observation and Geomatics Engineering, 2019. 3(1): p. 12-20.
7. Zolanvari, S.I., D.F. Laefer, and A.S. Natanzi, Three-dimensional building façade segmentation and opening area
detection from point clouds. ISPRS journal of photogrammetry and remote sensing, 2018. 143: p. 134-149.
8. Ramiya, A.M., R.R. Nidamanuri, and R. Krishnan, Segmentation based building detection approach from LiDAR
point cloud. The Egyptian Journal of Remote Sensing and Space Science, 2017. 20(1): p. 71-77.
9. Lafarge, F., et al., Automatic building extraction from DEMs using an object approach and application to the 3D-city
modeling. ISPRS Journal of photogrammetry and remote sensing, 2008. 63(3): p. 365-381.
10. Kedzierski, M. and A. Fryskowska, Terrestrial and aerial laser scanning data integration using wavelet analysis for the
purpose of 3D building modeling. Sensors, 2014. 14(7): p. 12070-12092.
11. Biljecki, F., H. Ledoux, and J. Stoter, An improved LOD specification for 3D building models. Computers,
Environment and Urban Systems, 2016. 59: p. 25-37.
12. Tang, L., et al., A Full Level-of-Detail Specification for 3D Building Models Combining Indoor and Outdoor Scenes.
ISPRS International Journal of Geo-Information, 2018. 7(11): p. 419.
13. Lee, S.C. and R. Nevatia. Interactive 3D building modeling using a hierarchical representation. in First IEEE
International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis, 2003. HLK 2003. 2003. IEEE.
14. Wu, B., et al., A graph-based approach for 3D building model reconstruction from airborne LiDAR point clouds.
Remote Sensing, 2017. 9(1): p. 92.
15. Vosselman, G. and S. Dijkman, 3D building model reconstruction from point clouds and ground plans. International
archives of photogrammetry remote sensing and spatial information sciences, 2001. 34(3/W4): p. 37-44.
16. Orthuber, E. and J. Avbelj. 3D building reconstruction from lidar point clouds by adaptive dual contouring. in ISPRS
Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, PIA15+ HRIGI15–Joint ISPRS conference
2015. 2015. Copernicus Publication.
17. Rusu, R.B., et al., Towards 3D Point cloud based object maps for household environments. Robotics and Autonomous
Systems, 2008. 56(11): p. 927-941.
18. Lafarge, F., et al. Building reconstruction from a single DEM. in 2008 IEEE Conference on Computer Vision and
Pattern Recognition. 2008. IEEE.
19. Tseng, Y.-H. and H. Hsiao-Chu, Extraction of building boundary lines from airborne LIDAR point clouds. The
International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016. 41: p. 957.

10