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Methodology of real-time 3D point cloud mapping with UAV lidar

Yıl 2023, , 301 - 309, 15.10.2023
https://doi.org/10.26833/ijeg.1178260

Öz

Accurate and timely availability of LiDAR data is vital in some cases. To facilitate monitoring of any environmental changes, LiDAR systems can be designed, and carried by UAV platforms that can take off without major preparation. In this study, the methodology of the real-time LiDAR mapping system was developed in the laboratory. The designed system shortens the target-based flight planning and post-flight data processing. In this system, the data is taken instantly and thus the change in the mapping area can be detected quickly. The simulation system, produce 3D point cloud, and data was stored in a database for later analysis. The 3D visualization of the data obtained from our developed UAV-LiDAR system was carried out with a platform-independent interface designed as web-based. The X3D file format used in the study to produce 3D point data provide an infrastructure for AI and ML-based systems in identifying urban objects in systems containing big data such as LiDAR.

Destekleyen Kurum

Kocaeli University Scientific Research Projects Coordination Unit

Proje Numarası

2561

Kaynakça

  • Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 22-36. https://doi.org/10.1016/j.isprsjprs.2015.10.004
  • Lechner, A. M., Foody, G. M., & Boyd, D. S. (2020). Applications in remote sensing to forest ecology and management. One Earth, 2(5), 405-412. https://doi.org/10.1016/J.ONEEAR.2020.05.001
  • Levin, N., Kyba, C. C., Zhang, Q., de Miguel, A. S., Román, M. O., Li, X., ... & Elvidge, C. D. (2020). Remote sensing of night lights: A review and an outlook for the future. Remote Sensing of Environment, 237, 111443. https://doi.org/10.1016/J.RSE.2019.111443
  • Diaz, B. S., Mata-Zayas, E. E., Gama-Campillo, L. M., Rincon-Ramirez, J. A., Vidal-Garcia, F., Rullan-Silva, C. D., & Sanchez-Gutierrez, F. (2022). LiDAR modeling to determine the height of shade canopy tree in cocoa agrosystems as available habitat for wildlife. International Journal of Engineering and Geosciences, 7(3), 283-293. https://doi.org/10.26833/ijeg.978990
  • Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/RS12193136
  • Ørka, H. O., Jutras-Perreault, M. C., Næsset, E., & Gobakken, T. (2022). A framework for a forest ecological base map–An example from Norway. Ecological Indicators, 136, 108636. https://doi.org/10.1016/j.ecolind.2022.108636
  • Calera, A., Campos, I., Osann, A., D’Urso, G., & Menenti, M. (2017). Remote sensing for crop water management: From ET modelling to services for the end users. Sensors, 17(5), 1104. https://doi.org/10.3390/S17051104
  • Jiang, D., & Wang, K. (2019). The role of satellite-based remote sensing in improving simulated streamflow: A review. Water, 11(8), 1615. https://doi.org/10.3390/W11081615
  • Keleş, M. D., & Aydın, C. C. (2020). Mobil Lidar Verisi ile Kent Ölçeğinde Cadde Bazlı Envanter Çalışması ve Coğrafi Sistemleri Entegrasyonu-Ankara Örneği. Geomatik, 5(3), 193-200. https://doi.org/10.29128/geomatik.643569
  • Awad, M. M. (2017). Toward robust segmentation results based on fusion methods for very high resolution optical image and lidar data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2067-2076. https://doi.org/10.1109/JSTARS.2017.2653061
  • Yao, H., Qin, R., & Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications—A review. Remote Sensing, 11(12), 1443. https://doi.org/10.3390/rs11121443
  • Yang, B., & Chen, C. (2015). Automatic registration of UAV-borne sequent images and LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 262-274. https://doi.org/10.1016/j.isprsjprs.2014.12.025
  • Li, J., Yang, B., Chen, C., & Habib, A. (2019). NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 123-145. https://doi.org/10.1016/j.isprsjprs.2019.10.009
  • Jiang, S., Jiang, W., Huang, W., & Yang, L. (2017). UAV-based oblique photogrammetry for outdoor data acquisition and offsite visual inspection of transmission line. Remote Sensing, 9(3), 278. https://doi.org/10.3390/rs9030278
  • Fuad, N. A., Ismail, Z., Majid, Z., Darwin, N., Ariff, M. F. M., Idris, K. M., & Yusoff, A. R. (2018, June). Accuracy evaluation of digital terrain model based on different flying altitudes and conditional of terrain using UAV LiDAR technology. In IOP conference series: earth and environmental science (Vol. 169, No. 1, p. 012100). IOP Publishing. https://doi.org/10.1088/1755-1315/169/1/012100
  • Sofonia, J. J., Phinn, S., Roelfsema, C., Kendoul, F., & Rist, Y. (2019). Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning. ISPRS journal of photogrammetry and remote sensing, 149, 105-118. https://doi.org/10.1016/j.isprsjprs.2019.01.020
  • Jiang, S., Jiang, C., & Jiang, W. (2020). Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 230-251. https://doi.org/10.1016/j.isprsjprs.2020.04.016
  • Awrangjeb, M. (2015). Effective generation and update of a building map database through automatic building change detection from LiDAR point cloud data. Remote Sensing, 7(10), 14119-14150. https://doi.org/10.3390/RS71014119
  • He, M., Zhu, Q., Du, Z., Hu, H., Ding, Y., & Chen, M. (2016). A 3D shape descriptor based on contour clusters for damaged roof detection using airborne LiDAR point clouds. Remote Sensing, 8(3), 189. https://doi.org/10.3390/rs8030189
  • Meng, X., Currit, N., & Zhao, K. (2010). Ground filtering algorithms for airborne LiDAR data: A review of critical issues. Remote Sensing, 2(3), 833-860. https://doi.org/10.3390/rs2030833
  • Tulldahl, H. M., Bissmarck, F., Larsson, H., Grönwall, C., & Tolt, G. (2015, October). Accuracy evaluation of 3D lidar data from small UAV. In Electro-Optical Remote Sensing, Photonic Technologies, and Applications IX (Vol. 9649, p. 964903). SPIE. https://doi.org/10.1117/12.2194508
  • Thiel, C., & Schmullius, C. (2017). Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective. International Journal of Remote Sensing, 38(8-10), 2411-2426. https://doi.org/10.1080/01431161.2016.1225181
  • Zhou, S., & Wu, Z. (2013). Social Media Retrieval and Mining. IOP Conference Series: Earth and Environmental Science (Vol. 387). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41629-3
  • Sun, Y., & Polys, N. (2020, November). The Scalability of X3D4 PointProperties: Benchmarks on WWW Performance. In The 25th International Conference on 3D Web Technology (pp. 1-8). https://doi.org/10.1145/3424616.3424707
  • Yoo, B., & Brutzman, D. (2009, June). X3D earth terrain-tile production chain for georeferenced simulation. In Proceedings of the 14th international conference on 3D Web technology (pp. 159-166). https://doi.org/10.1145/1559764.1559791
  • Han, S., Brutzman, D., Lee, J., Yoo, K. H., Marchetti, V., Mouton, C., ... & Jia, J. (Eds.). (2020). The 25th International Conference on 3D Web Technology. ACM.
  • Kim, J. S., Polys, N., & Sforza, P. (2015, June). Preparing and evaluating geospatial data models using X3D encodings for web 3D geovisualization services. In Proceedings of the 20th International Conference on 3D Web Technology (pp. 55-63). https://doi.org/10.1145/2775292.2775304
Yıl 2023, , 301 - 309, 15.10.2023
https://doi.org/10.26833/ijeg.1178260

Öz

Proje Numarası

2561

Kaynakça

  • Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 22-36. https://doi.org/10.1016/j.isprsjprs.2015.10.004
  • Lechner, A. M., Foody, G. M., & Boyd, D. S. (2020). Applications in remote sensing to forest ecology and management. One Earth, 2(5), 405-412. https://doi.org/10.1016/J.ONEEAR.2020.05.001
  • Levin, N., Kyba, C. C., Zhang, Q., de Miguel, A. S., Román, M. O., Li, X., ... & Elvidge, C. D. (2020). Remote sensing of night lights: A review and an outlook for the future. Remote Sensing of Environment, 237, 111443. https://doi.org/10.1016/J.RSE.2019.111443
  • Diaz, B. S., Mata-Zayas, E. E., Gama-Campillo, L. M., Rincon-Ramirez, J. A., Vidal-Garcia, F., Rullan-Silva, C. D., & Sanchez-Gutierrez, F. (2022). LiDAR modeling to determine the height of shade canopy tree in cocoa agrosystems as available habitat for wildlife. International Journal of Engineering and Geosciences, 7(3), 283-293. https://doi.org/10.26833/ijeg.978990
  • Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136. https://doi.org/10.3390/RS12193136
  • Ørka, H. O., Jutras-Perreault, M. C., Næsset, E., & Gobakken, T. (2022). A framework for a forest ecological base map–An example from Norway. Ecological Indicators, 136, 108636. https://doi.org/10.1016/j.ecolind.2022.108636
  • Calera, A., Campos, I., Osann, A., D’Urso, G., & Menenti, M. (2017). Remote sensing for crop water management: From ET modelling to services for the end users. Sensors, 17(5), 1104. https://doi.org/10.3390/S17051104
  • Jiang, D., & Wang, K. (2019). The role of satellite-based remote sensing in improving simulated streamflow: A review. Water, 11(8), 1615. https://doi.org/10.3390/W11081615
  • Keleş, M. D., & Aydın, C. C. (2020). Mobil Lidar Verisi ile Kent Ölçeğinde Cadde Bazlı Envanter Çalışması ve Coğrafi Sistemleri Entegrasyonu-Ankara Örneği. Geomatik, 5(3), 193-200. https://doi.org/10.29128/geomatik.643569
  • Awad, M. M. (2017). Toward robust segmentation results based on fusion methods for very high resolution optical image and lidar data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2067-2076. https://doi.org/10.1109/JSTARS.2017.2653061
  • Yao, H., Qin, R., & Chen, X. (2019). Unmanned aerial vehicle for remote sensing applications—A review. Remote Sensing, 11(12), 1443. https://doi.org/10.3390/rs11121443
  • Yang, B., & Chen, C. (2015). Automatic registration of UAV-borne sequent images and LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 262-274. https://doi.org/10.1016/j.isprsjprs.2014.12.025
  • Li, J., Yang, B., Chen, C., & Habib, A. (2019). NRLI-UAV: Non-rigid registration of sequential raw laser scans and images for low-cost UAV LiDAR point cloud quality improvement. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 123-145. https://doi.org/10.1016/j.isprsjprs.2019.10.009
  • Jiang, S., Jiang, W., Huang, W., & Yang, L. (2017). UAV-based oblique photogrammetry for outdoor data acquisition and offsite visual inspection of transmission line. Remote Sensing, 9(3), 278. https://doi.org/10.3390/rs9030278
  • Fuad, N. A., Ismail, Z., Majid, Z., Darwin, N., Ariff, M. F. M., Idris, K. M., & Yusoff, A. R. (2018, June). Accuracy evaluation of digital terrain model based on different flying altitudes and conditional of terrain using UAV LiDAR technology. In IOP conference series: earth and environmental science (Vol. 169, No. 1, p. 012100). IOP Publishing. https://doi.org/10.1088/1755-1315/169/1/012100
  • Sofonia, J. J., Phinn, S., Roelfsema, C., Kendoul, F., & Rist, Y. (2019). Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning. ISPRS journal of photogrammetry and remote sensing, 149, 105-118. https://doi.org/10.1016/j.isprsjprs.2019.01.020
  • Jiang, S., Jiang, C., & Jiang, W. (2020). Efficient structure from motion for large-scale UAV images: A review and a comparison of SfM tools. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 230-251. https://doi.org/10.1016/j.isprsjprs.2020.04.016
  • Awrangjeb, M. (2015). Effective generation and update of a building map database through automatic building change detection from LiDAR point cloud data. Remote Sensing, 7(10), 14119-14150. https://doi.org/10.3390/RS71014119
  • He, M., Zhu, Q., Du, Z., Hu, H., Ding, Y., & Chen, M. (2016). A 3D shape descriptor based on contour clusters for damaged roof detection using airborne LiDAR point clouds. Remote Sensing, 8(3), 189. https://doi.org/10.3390/rs8030189
  • Meng, X., Currit, N., & Zhao, K. (2010). Ground filtering algorithms for airborne LiDAR data: A review of critical issues. Remote Sensing, 2(3), 833-860. https://doi.org/10.3390/rs2030833
  • Tulldahl, H. M., Bissmarck, F., Larsson, H., Grönwall, C., & Tolt, G. (2015, October). Accuracy evaluation of 3D lidar data from small UAV. In Electro-Optical Remote Sensing, Photonic Technologies, and Applications IX (Vol. 9649, p. 964903). SPIE. https://doi.org/10.1117/12.2194508
  • Thiel, C., & Schmullius, C. (2017). Comparison of UAV photograph-based and airborne lidar-based point clouds over forest from a forestry application perspective. International Journal of Remote Sensing, 38(8-10), 2411-2426. https://doi.org/10.1080/01431161.2016.1225181
  • Zhou, S., & Wu, Z. (2013). Social Media Retrieval and Mining. IOP Conference Series: Earth and Environmental Science (Vol. 387). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41629-3
  • Sun, Y., & Polys, N. (2020, November). The Scalability of X3D4 PointProperties: Benchmarks on WWW Performance. In The 25th International Conference on 3D Web Technology (pp. 1-8). https://doi.org/10.1145/3424616.3424707
  • Yoo, B., & Brutzman, D. (2009, June). X3D earth terrain-tile production chain for georeferenced simulation. In Proceedings of the 14th international conference on 3D Web technology (pp. 159-166). https://doi.org/10.1145/1559764.1559791
  • Han, S., Brutzman, D., Lee, J., Yoo, K. H., Marchetti, V., Mouton, C., ... & Jia, J. (Eds.). (2020). The 25th International Conference on 3D Web Technology. ACM.
  • Kim, J. S., Polys, N., & Sforza, P. (2015, June). Preparing and evaluating geospatial data models using X3D encodings for web 3D geovisualization services. In Proceedings of the 20th International Conference on 3D Web Technology (pp. 55-63). https://doi.org/10.1145/2775292.2775304
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Levent Candan 0000-0002-4557-6498

Elif Kaçar 0000-0001-6682-0114

Proje Numarası 2561
Erken Görünüm Tarihi 8 Mayıs 2023
Yayımlanma Tarihi 15 Ekim 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Candan, L., & Kaçar, E. (2023). Methodology of real-time 3D point cloud mapping with UAV lidar. International Journal of Engineering and Geosciences, 8(3), 301-309. https://doi.org/10.26833/ijeg.1178260
AMA Candan L, Kaçar E. Methodology of real-time 3D point cloud mapping with UAV lidar. IJEG. Ekim 2023;8(3):301-309. doi:10.26833/ijeg.1178260
Chicago Candan, Levent, ve Elif Kaçar. “Methodology of Real-Time 3D Point Cloud Mapping With UAV Lidar”. International Journal of Engineering and Geosciences 8, sy. 3 (Ekim 2023): 301-9. https://doi.org/10.26833/ijeg.1178260.
EndNote Candan L, Kaçar E (01 Ekim 2023) Methodology of real-time 3D point cloud mapping with UAV lidar. International Journal of Engineering and Geosciences 8 3 301–309.
IEEE L. Candan ve E. Kaçar, “Methodology of real-time 3D point cloud mapping with UAV lidar”, IJEG, c. 8, sy. 3, ss. 301–309, 2023, doi: 10.26833/ijeg.1178260.
ISNAD Candan, Levent - Kaçar, Elif. “Methodology of Real-Time 3D Point Cloud Mapping With UAV Lidar”. International Journal of Engineering and Geosciences 8/3 (Ekim 2023), 301-309. https://doi.org/10.26833/ijeg.1178260.
JAMA Candan L, Kaçar E. Methodology of real-time 3D point cloud mapping with UAV lidar. IJEG. 2023;8:301–309.
MLA Candan, Levent ve Elif Kaçar. “Methodology of Real-Time 3D Point Cloud Mapping With UAV Lidar”. International Journal of Engineering and Geosciences, c. 8, sy. 3, 2023, ss. 301-9, doi:10.26833/ijeg.1178260.
Vancouver Candan L, Kaçar E. Methodology of real-time 3D point cloud mapping with UAV lidar. IJEG. 2023;8(3):301-9.