Integration Point Cloud from Aerial Photogrammetry and Action Video Camera as a Solution for 3D Mapping Technology in Urban Area

Resy Meilani, Tedy Imanuel Selan, Nida Ummatun Nadiyah, Siti Trisuci Putri, Irwan Gumilar, Dhota Pradipta, Budhy Soeksmantono, Agung Budi Harto

Abstract


The rapid development of urban areas presents complex challenges in achieving efficient space utilization, compliance with spatial planning, and sustainable development. This study emphasizes the need for urban three-dimensional mapping to accurately represent terrestrial objects for effective city planning. By employing advanced technologies, including aerial photogrammetry and action video cameras, this study integrates point cloud data from various sources to produce high-resolution 3D spatial information. The integration process is validated through position and dimension validation methods. Position validation uses Global Navigation Satellite Systems (GNSS) with the RTK-NTRIP method and Continuously Operating Reference Stations (CORS). The Root Mean Square Error (RMSE) is calculated based on the differences in X, Y, and Z coordinates between GNSS measurements and the point cloud, resulting in a total RMSE of 0.528 meters, in accordance with the Level of Detail (LoD) 2 standards set by the Open Geospatial Consortium. Dimension validation is conducted using a distometer, comparing the measured object sizes with those in the point cloud, yielding an RMSE of 0.578 meters. These findings indicate that this integration represents efficient and accurate technology for 3D urban mapping.


Keywords


3D Urban Mapping; Integration Mapping Technology; Point Cloud Extraction; Point Cloud Integration

References


Babahajiani, P., Fan, L., Kämäräinen, J. K., & Gabbouj, M. (2017). Urban 3D Segmentation and Modelling from Street View Images and LiDAR Point Clouds. Machine Vision and Applications, 28(7), 679–694. https://doi.org/10.1007/s00138-017-0845-3

Blaschke, T. (2010). Object Based Image Analysis for Remote Sensing. In ISPRS Journal of Photogrammetry and Remote Sensing (Vol. 65, Issue 1, pp. 2–16). https://doi.org/10.1016/j.isprsjprs.2009.06.004

Che Ku Abdullah, C. K. A. F., Baharuddin, N. Z. S., Ariff, M. F. M., Majid, Z., Lau, C. L., Yusoff, A. R., Idris, K. M., & Aspuri, A. (2017). Integration of Point Clouds Dataset from Different Sensors. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2W3), 9–15. https://doi.org/10.5194/isprs-archives-XLII-2-W3-9-2017

Gonçalves, J. A., & Pinhal, A. (2018). Mobile Mapping System Based on Action Cameras. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(1), 167–171. https://doi.org/10.5194/isprs-archives-XLII-1-167-2018

Gu, X., Wang, X., & Guo, Y. (2020). A Review of Research on Point Cloud Registration Methods. IOP Conference Series: Materials Science and Engineering, 782(2). https://doi.org/10.1088/1757-899X/782/2/022070

Haala, N., & Kada, M. (2010). An Update on Automatic 3D Building Reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 570–580. https://doi.org/10.1016/j.isprsjprs.2010.09.006

Hapriansyah, S. A. (2021). Analisis Perbandingan Ketelitian Hasil Orthomosaic Menggunakan Perangkat Lunak Komersial Pix4Dmapper dan Open Source WebODM Drone. Jurnal Teknis ITS, 10.

Li, D., He, K., Wang, L., & Zhang, D. (2022). Local Feature Extraction Network with High Correspondences for 3d Point Cloud Registration. Applied Intelligence, 52(9), 9638–9649. https://doi.org/10.1007/s10489-021-03055-1

Li, P., Wang, R., Wang, Y., & Tao, W. (2020). Evaluation of the ICP Algorithm in 3D Point Cloud Registration. IEEE Access, 8, 68030–68048. https://doi.org/10.1109/ACCESS.2020.2986470

Open Geospatial Consortium. (2012). Open Geospatial Consortium OGC City Geography Markup Language ( CityGML ) En- Coding Standard.

Pollefeys, M., Koch, R., Vergauwen, M., & Van Gool, L. (2000). Automated reconstruction of 3D scenes from sequences of images. In ISPRS Journal of Photogrammetry & Remote Sensing (Vol. 55). www.elsevier.nlrlocaterisprsjprs

Schnabel, R., Wahl, R., & Klein, R. (2007). Efficient RANSAC for Point Cloud Shape Detection. Journal Compilation The Eurographics Association and Blackwell Publishing, 26(2), 214–226.

Shahzad, M., & Zhu, X. X. (2015). Robust Reconstruction of Building Facades for Large Areas Using Spaceborne TomoSAR Point Clouds. IEEE Transactions on Geoscience and Remote Sensing, 53(2), 752–769. https://doi.org/10.1109/TGRS.2014.2327391

Sirmacek, B., & Lindenbergh, R. (2014). Accuracy Assessment of Building Point Clouds Automatically Generated from Iphone Images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(5), 547–552. https://doi.org/10.5194/isprsarchives-XL-5-547-2014

Šiško, D. (2022). Application of 3D City Model in Spatial Planning of the City of Zagreb. FIG Congress.

Steinhage, V., Behley, J., Meisel, S., & Cremers, A. B. (2010). Automated Updating and Maintenance of 3D City Models. ISPRS, XXXVIII, 4–8. http://ivs.informatik.uni-bonn.de/

Suveg, I., & Vosselman, G. (2004). Reconstruction of 3D Building Models from Aerial Images and Maps. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 202–224. https://doi.org/10.1016/j.isprsjprs.2003.09.006

Suziedelyte Visockiene, J., Brucas, D., & Ragauskas, U. (2014). Comparison of UAV Images Processing Softwares. Journal of Measurement in Engineering, 2(2).

Tomljenovic, I., Höfle, B., Tiede, D., & Blaschke, T. (2015). Building Extraction from Airborne Laser Scanning Data: An Analysis of the State of The Art. In Remote Sensing (Vol. 7, Issue 4, pp. 3826–3862). MDPI AG. https://doi.org/10.3390/rs70403826

Yang, Z., Wang, X., & Hou, J. (2021). A 4PCS Coarse Registration Algorithm Based on ISS Feature Points. Chinese Control Conference, CCC, 2021-July, 7371–7375. https://doi.org/10.23919/CCC52363.2021.9549486

Zhu, X. X., & Shahzad, M. (2014). Facade Reconstruction Using Multiview Spaceborne TomoSAR Point Clouds. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3541–3552. https://doi.org/10.1109/TGRS.2013.2273619




DOI: https://doi.org/10.17509/gea.v25i2.81474

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