Comparative Evaluation of LiDAR systems for transport infrastructure: case studies and performance analysis

References

• Al-Durgham, K., Lichti, D. D., Kwak, E., & Dixon, R. (2021). Automated accuracy assessment of a mobile mapping system with lightweight laser scanning and mems sensors. Applied Sciences (Switzerland), 11(3), 1–17. https://doi.org/10.3390/app11031007
   Web of Science ®Google Scholar
• Annok, G., Tammin, V., & Liba, N. (2021). Mobile laser scanning elevation data accuracy in closed and partially open sky area. Geodesy and Cartography, 47(1), 21–26. https://doi.org/10.3846/gac.2021.12044
   Google Scholar
• Balado, J., Frías, E., González-Collazo, S. M., & Díaz-Vilariño, L. (2022). New trends in laser scanning for cultural heritage. Lecture Notes in Civil Engineering, 258. https://doi.org/10.1007/978-981-19-1894-0_10
   Google Scholar
• Balado, J., Martínez-Sánchez, J., Arias, P., & Novo, A. (2019). Road environment semantic segmentation with deep learning from MLS point cloud data. Sensors, 19(16), 3466. https://doi.org/10.3390/s19163466
   PubMed Web of Science ®Google Scholar
• Balado, J., Soilán, M., Díaz-Vilariño, L., & Van Oosterom, P. (2021). Segmentation of traffic signs from poles with mathematical morphology applied to point clouds. ISPRS Annals of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 5(2), 145–151. https://doi.org/10.5194/isprs-annals-V-2-2021-145-2021
   Google Scholar
• Bauwens, S., Bartholomeus, H., Calders, K., & Lejeune, P. (2016). Forest inventory with terrestrial LiDAR: A comparison of static and hand-held mobile laser scanning. Forests, 7(6), 127. https://doi.org/10.3390/f7060127
   Web of Science ®Google Scholar
• Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
   Web of Science ®Google Scholar
• Bobrowski, R., Winczek, M., Zięba-Kulawik, K., & Wężyk, P. (2023). Best practices to use the iPad pro LiDAR for some procedures of data acquisition in the urban forest. Urban Forestry & Urban Greening, 79, 127815. https://doi.org/10.1016/j.ufug.2022.127815
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• FARO Focus Laser Scanner| Hardware | LIGHTHOUSE. (n.d.). Retrieved December 27, 2022, from https://www.faro.com/es-MX/Products/Hardware/Focus-Laser-Scanners
   Google Scholar
• Fryskowska-Skibniewska, A., & Wróblewski, P. (2018). mobile laser scanning accuracy assessment for the purpose of base-map updating. Geodesy and Cartography, 67, 35–55. https://doi.org/10.24425/118701
   Google Scholar
• Gong, Z., Li, J., Luo, Z., Wen, C., Wang, C., & Zelek, J. (2021). Mapping and semantic modeling of underground parking lots using a backpack LiDAR system. IEEE Transactions on Intelligent Transportation Systems, 22(2), 734–746. https://doi.org/10.1109/TITS.2019.2955734
   Web of Science ®Google Scholar
• Habib, A., Bullock, D. M., Lin, Y.-C., Manish, R., & others. (2021). Road ditch line mapping with mobile LiDAR.
   Google Scholar
• He, X., Zhao, J., Sun, L., Huang, Y., Zhang, X., Li, J., & Ye, C. (2018). Automatic vector-based road structure mapping using multi-beam LiDAR. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 417–422. https://doi.org/10.1109/ITSC.2018.8569894
   Google Scholar
• Home | Teledyne Geospatial. (n.d.). Retrieved December 26, 2022, from https://www.teledyneoptech.com/en/home/
   Google Scholar
• Hunčaga, M., Chudá, J., Tomaštík, J., Slámová, M., Koreň, M., & Chudý, F. (2020). The comparison of stem curve accuracy determined from point clouds acquired by different terrestrial remote sensing methods. Remote Sensing, 12(17), 2739. https://doi.org/10.3390/RS12172739
   Web of Science ®Google Scholar
• Hyyppä, E., Kukko, A., Kaijaluoto, R., White, J. C., Wulder, M. A., Pyörälä, J., Liang, X., Yu, X., Wang, Y., Kaartinen, H., Virtanen, J.-P., & Hyyppä, J. (2020). Accurate derivation of stem curve and volume using backpack mobile laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, 161, 246–262. https://doi.org/10.1016/j.isprsjprs.2020.01.018
   Web of Science ®Google Scholar
• Kaartinen, H., Hyyppä, J., Kukko, A., Jaakkola, A., & Hyyppä, H. (2012). Benchmarking the performance of mobile laser scanning systems using a permanent test field. Sensors, 12(9), 12814–12835. https://doi.org/10.3390/s120912814
   Web of Science ®Google Scholar
• Kalenjuk, S., & Lienhart, W. (2022). A method for efficient quality control and enhancement of mobile laser scanning data. Remote Sensing, 14(4), 857. https://doi.org/10.3390/rs14040857
   Web of Science ®Google Scholar
• Kalvoda, P., Nosek, J., Kuruc, M., & Volarik, T. (2020). Accuracy evaluation of RIEGL VMX-450 mobile mapping system. International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 2(2.2), 165–174. https://doi.org/10.5593/sgem2020/2.2/s10.020. 020-Augus.
   Google Scholar
• Lin, Y. C., Cheng, Y.-T., Lin, Y.-J., Flatt, J. E., Habib, A., & Bullock, D. (2019). Evaluating the accuracy of mobile LiDAR for mapping airfield infrastructure. Transportation Research Record, 2673(4), 117–124. https://doi.org/10.1177/0361198119835802
   Web of Science ®Google Scholar
• Niina, Y., Ryohei, H., Honma, Y., Kondo, K., Tsuji, K., Hiramatsu, T., & Oketani, E. (2018). Automatic rail extraction and celarance check with a point cloud captured by MLS in a railway. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2, 767–771. https://doi.org/10.5194/isprs-archives-XLII-2-767-2018
   Google Scholar
• Niță, M. D. (2021). Testing forestry digital twinning workflow based on mobile LiDAR scanner and AI platform. Forests, 12(11), 1576. https://doi.org/10.3390/f12111576
   Web of Science ®Google Scholar
• Qiu, Z., Martínez-Sánchez, J., Arias-Sánchez, P., & Rashdi, R. (2023). External multi-modal imaging sensor calibration for sensor fusion: A review. Information Fusion, 97, 101806. https://doi.org/10.1016/j.inffus.2023.101806
   Web of Science ®Google Scholar
• Rashdi, R., Balado, J., Sánchez, J. M., & Arias, P. (2023). comparative study of road and urban object classification based on mobile laser scanners. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 423–429. https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-423-2023
   Google Scholar
• Rashdi, R., Martínez-Sánchez, J., Arias, P., & Qiu, Z. (2022). Scanning technologies to building information modelling: A review. Infrastructures, 7(4), 49. https://doi.org/10.3390/infrastructures7040049
   Google Scholar
• RIEGL - RIEGL Laser Measurement Systems. (n.d.). Retrieved January 17, 2022, from http://www.riegl.com/
   Google Scholar
• Sánchez-Rodríguez, A., Riveiro, B., Soilán, M., & González-DeSantos, L. M. (2018). Automated detection and decomposition of railway tunnels from mobile laser scanning datasets. Automation in Construction, 96, 171–179. https://doi.org/10.1016/j.autcon.2018.09.014
   Web of Science ®Google Scholar
• Schneider, D., & Blaskow, R. (2021). Boat-based mobile laser scanning for shoreline monitoring of large lakes. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 43(B2–2021), 759–762. https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-759-2021
   Google Scholar
• Shi, B., Bai, Y., Zhang, S., Zhong, R., Yang, F., Song, S., & Li, G. (2021). Reference-plane-based approach for accuracy assessment of mobile mapping point clouds. Measurement: Journal of the International Measurement Confederation, 171, 108759. https://doi.org/10.1016/j.measurement.2020.108759
   Web of Science ®Google Scholar
• Sofia, H., Anas, E., & Faïz, O. (2020). mobileMobile mapping, machine learning and digital twin for road infrastructure monitoring and maintenance: Case study of Mohammed VI bridge in Morocco. 2020 IEEE International Conference of Moroccan Geomatics (Morgeo), 1–6. https://doi.org/10.1109/Morgeo49228.2020.9121882
   Google Scholar
• Soilán, M., Justo, A., Sánchez-Rodríguez, A., & Riveiro, B. (2020). 3D point cloud to BIM: Semi-automated framework to define IFC alignment entities from MLS-Acquired LiDAR data of highway roads. Remote Sensing, 12(14). https://doi.org/10.3390/rs12142301
   Web of Science ®Google Scholar
• Takahashi, G., & Masuda, H. (2020). Scanline normalization for mms data measured under different conditions. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 325–331. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-325-2020
   Google Scholar
• Teo, T.-A. (2018). The extraction of urban road inventory from mobile lidar system. IOP Conference Series: Earth and Environmental Science, 169.
   Google Scholar
• Toschi, I., Rodríguez-Gonzálvez, P., Remondino, F., Minto, S., Orlandini, S., & Fuller, A. (2015). Accuracy evaluation of a mobile mapping system with advanced statistical methods. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(5W4), 245–253. https://doi.org/10.5194/isprsarchives-XL-5-W4-245-2015
   Google Scholar
• Trimble Geospatial | Survey and Mapping Solutions. (n.d.). Retrieved December 27, 2022, from https://geospatial.trimble.com/
   Google Scholar
• Tucci, G., Visintini, D., Bonora, V., & Parisi, E. I. (2018). Examination of indoor mobile mapping systems in a diversified internal/external test field. Applied Sciences, 8(3), 401. https://doi.org/10.3390/app8030401
   Google Scholar
• Vaaja, M., Kukko, A., Kaartinen, H., Kurkela, M., Kasvi, E., Flener, C., Hyyppä, H., Hyyppä, J., Järvelä, J., & Alho, P. (2013). Data processing and quality evaluation of a boat-based mobile laser scanning system. Sensors, 13(9), 12497–12515. https://doi.org/10.3390/s130912497
   PubMed Web of Science ®Google Scholar
• Veneziano, D., Hallmark, S., & Souleyrette, R. (2002). Accuracy evaluation of LIDAR-derived terrain data for highway location. Computer-Aided Civil and Infrastructure Engineering.
   Google Scholar
• Weinmann, M., Jutzi, B., & Mallet, C. (2013). Feature relevance assessment for the semantic interpretation of 3D point cloud data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 313–318. https://doi.org/10.5194/isprsannals-II-5-W2-313-2013
   Google Scholar
• Weinmann, M., Jutzi, B., & Mallet, C. (2014). Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features. ISPRS Annals of the Photogrammetry, Remote Sensing & Spatial Information Sciences, II, 181–188. https://doi.org/10.5194/isprsannals-II-3-181-2014
   Google Scholar
• Wu, C., Yuan, Y., Tang, Y., & Tian, B. (2022). Application of Terrestrial Laser Scanning (TLS) in the Architecture, Engineering and Construction (AEC) industry. Sensors, 22(1), 265. https://doi.org/10.3390/s22010265
   Web of Science ®Google Scholar
• Wysocki, O., Xu, Y., & Stilla, U. (2021). Unlocking point cloud potential: fusing MLS point clouds with semantic 3D building models while considering uncertainty. ISPRS Annals of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 8, 45–52. https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-45-2021
   Google Scholar
• Xu, S., Cheng, P., Zhang, Y., & Ding, P. (2015). Error analysis and accuracy assessment of mobile laser scanning system. The Open Automation and Control Systems Journal, 7(1), 485–495. https://doi.org/10.2174/1874444301507010485
   Google Scholar
• Zhou, M., Ma, L., Li, Y., & Li, J. (2018). Extraction of building windows from mobile laser scanning point clouds. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 4304–4307. https://doi.org/10.1109/IGARSS.2018.8518899
   Google Scholar