Advanced search
Publication Cover
International Journal of Remote Sensing Volume 41, 2020 - Issue 17
Submit an article Journal homepage
223
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Urban vehicle extraction from aerial laser scanning point cloud data

Tonggang Zhanga Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China;b State-Province Joint Engineering Laboratory in Spatial Information Technology for High-Speed Railway Safety, Chengdu, ChinaCorrespondence[email protected] [email protected]
ORCID Iconhttps://orcid.org/0000-0002-0332-5930View further author information
ORCID Icon,
Yuhui Kana Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, ChinaView further author information
,
Hailong Jiac China Railway Tunnel Group Sanchu Co., LTD, Shenzhen, ChinaView further author information
,
Chuan Dengc China Railway Tunnel Group Sanchu Co., LTD, Shenzhen, ChinaView further author information
&
Tingsong Xingc China Railway Tunnel Group Sanchu Co., LTD, Shenzhen, ChinaView further author information
Pages 6664-6697 | Received 01 Nov 2019, Accepted 02 Mar 2020, Published online: 17 Jun 2020

References

  • Chen, Z., B. Gao, and B. Devereux. 2017. “State-of-the-art: DTM Generation Using Airborne Lidar Data.” Sensors 17 (1): 150. doi:10.3390/s17010150.
  • Eum, J., M. Bae, J. Jeon, H. Lee, S. Oh, and M. Lee. 2017. “Vehicle Detection from Airborne LiDAR Point Clouds Based on a Decision Tree Algorithm with Horizontal and Vertical Features.” Remote Sensing Letters 8 (5): 409–418. doi:10.1080/2150704X.2016.1278310.
  • Goutte, C., and E. Gaussier. 2005. “A Probabilistic Interpretation of Precision, Recall and F-score, with Implication for Evaluation.” In 27th European Conference on IR Research, Santiago De Compostella. Spain, March 21–23. doi:10.1080/09553008714551221.
  • Huang, J., and S. You. 2016. “Vehicle Detection in Urban Point Clouds with Orthogonal-view Convolutional Neural Network.” In IEEE International Conference on Image Processing (ICIP). Arizona, USA, Sept 25–28. doi:10.1109/ICIP.2016.7532828.
  • Iglesias, F., and W. Kastner. 2013. “Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns.” Energies 6 (2): 579–597. doi:10.3390/en6020579.
  • Keogh, E., and C. A. Ratanamahatana. 2005. “Exact Indexing of Dynamic Time Warping.” Knowledge and Information Systems 7 (3): 358–386. doi:10.1007/s10115-004-0154-9.
  • Lafarge, F., and C. Mallet. 2012. “Creating Large-scale City Models from 3D-point Clouds: A Robust Approach with Hybrid Representation.” International Journal of Computer Vision 99 (1): 69–85. doi:10.1007/s11263-012-0517-8.
  • Liu, Y., S. T. Monteiro, and E. Saber. 2016. “Vehicle Detection from Aerial Color Imagery and Airborne LiDAR Data.” In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Prague, Czech, July 10–15. doi:10.1109/igarss.2016.7729354.
  • Lovas, T., C. K. Toth, and A. Barsi. 2004. “Model-based Vehicle Detection from Lidar Data.” In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Istanbul, Turkey.Istanbul, July 12–23, B4, 134–138
  • Paska, E. P. 2009. State-of-the-art Remote Sensing Geospatial Technologies in Support of Transportation Monitoring and Management. Ohio State University.
  • Rakusz, Á., T. Lovas, and Á. Barsi. 2004. “Lidar-based Vehicle Segmentation.” In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Istanbul, Turkey, July 12–23.
  • Xiao, W., B. Vallet, K. Schindler, and N. Paparoditis. 2016. “Street-side Vehicle Detection, Classification and Change Detection Using Mobile Laser Scanning Data.” ISPRS Journal of Photogrammetry and Remote Sensing 114 (4): 166–178. doi:10.1016/j.isprsjprs.2016.02.007.
  • Yang, B., R. Huang, Z. Dong, Y. Zang, and J. Li. 2016. “Two-step Adaptive Extraction Method for Ground Points and Breaklines from Lidar Point Clouds.” ISPRS Journal of Photogrammetry and Remote Sensing 119: 373–389. doi:10.1016/j.isprsjprs.2016.07.002.
  • Yao, W., S. Hinz, and U. Stilla. 2010. “3D Object-based Classification for Vehicle Extraction from Airborne LiDAR Data by Combining Point Shape Information with Spatial Edge.” In IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS). Istanbul, Aug 22. doi:10.1109/PRRS.2010.5742804.
  • Yao, W., S. Hinz, and U. Stilla. 2011. “Extraction and Motion Estimation of Vehicles in Single-pass Airborne LiDAR Data Towards Urban Traffic Analysis.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 260–271. doi:10.1016/j.isprsjprs.2010.10.005.
  • Yao, W., and U. Stilla. 2011. “Comparison of Two Methods for Vehicle Extraction from Airborne LiDAR Data toward Motion Analysis.” IEEE Geoscience and Remote Sensing Letters 8 (4): 607–611. doi:10.1109/lgrs.2010.2097239.
  • Zhang, J., M. Duan, Q. Yan, and X. Lin. 2014. “Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-based Point Cloud Analysis Method.” Remote Sensing 6 (9): 8405–8423. doi:10.3390/rs6098405.
  • Zhang, W., J. Qi, P. Wan, H. Wang, D. Xie, X. Wang, and G. Yan. 2016. “An Easy-to-use Airborne LiDAR Data Filtering Method Based on Cloth Simulation.” Remote Sensing 8 (6): 501. doi:10.3390/rs8060501.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.