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Airborne laser scanner as a data source for building selected elements of an intelligent database for transportation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, the main objective was to detect the road network and key road infrastructure elements based on airborne laser scanning data. The study included identification of the road network and determination of its axes using three independent methods, as well as detection of horizontal signs such as pedestrian crossings. The analysis process was based mainly on digital image processing methods, based solely on lidar data, without using information from other sources. The results of the analysis showed that the use of lidar data provides a fast and effective method for continuously updating information on road infrastructure and expanding the transportation database. This potentially opens the door to effectively updating relevant data in the area of transportation infrastructure.
Rocznik
Tom
Strony
197--216
Opis fizyczny
Bibliogr. 28 poz.
Twórcy
  • SOFTELNET S.A., ul. Juliusza Lea 114, 30-133 Kraków, Poland
  • AGH University of Krakow, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, Poland
Bibliografia
  • 1. Chojnacki B., M. Kowalewski, A. Pękalski. 2013. „Importance of national ITS architecture”. Prace Naukowe Politechniki Warszawskiej. Transport 95.
  • 2. Ibáñez J., S. Zeadally, J. Contreras-Castillo. 2015. “Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and Internet of Things technologies”. IEEE Wirel Commun 22: 122-128. DOI: https://doi.org/10.1109/MWC.2015.7368833.
  • 3. Zhang J., F.Y. Wang, K. Wang, W.H. Lin, X. Xu, C. Chen. 2011. “Data-driven intelligent transportation systems: A survey”. IEEE Transactions on Intelligent Transportation Systems 12: 1624-1639. DOI: https://doi.org/10.1109/TITS.2011.2158001.
  • 4. Oladimeji D, K. Gupta, N.A. Kose, K. Gundogan, L. Ge, F. Liang. 2023. “Smart Transportation: An overview of technologies and applications”. Sensors 23(8): 3880. DOI: https://doi.org/10.3390/S23083880.
  • 5. Generalna Dyrekcja Dróg Krajowych i Autostrad - Portal Gov.pl. [In Polish: General Directorate for National Roads and Motorways]. Available at: https://www.gov.pl/web/gddkia.
  • 6. Fendi K.G, S.M. Adam, N. Kokkas, M. Smith. 2014. “An Approach to Produce a GIS Database for Road Surface Monitoring”. APCBEE Procedia 9: 235-240. DOI: https://doi.org/10.1016/J.APCBEE.2014.01.042.
  • 7. Balado J., E. González, P. Arias, D. Castro. 2020. “Novel approach to automatic traffic sign inventory based on mobile mapping system data and deep learning”. Remote Sensing 12(3): 442. DOI: https://doi.org/10.3390/RS12030442.
  • 8. Elhashash M., H. Albanwan, R. Qin. 2022. “A Review of Mobile Mapping Systems: From Sensors to Applications”. Sensors 22(11): 4262. DOI: https://doi.org/10.3390/S22114262.
  • 9. Wang Y., Q. Chen, Q. Zhu, L. Liu, C. Li, D. Zheng. 2019. “A survey of mobile laser scanning applications and key techniques over urban areas”. Remote Sensing 11(13): 1540. DOI: https://doi.org/10.3390/RS11131540.
  • 10. Liang P, W. Shi, Y. Ding, Z. Liu, H. Shang. 2012. “Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning”. Sensors 21(9): 3152. DOI: https://doi.org/10.3390/S21093152.
  • 11. Wang W., N. Yang, Y. Zhang, F. Wang, T. Cao, P. Eklund. 2016. “A review of road extraction from remote sensing images”. Journal of Traffic and Transportation Engineering 3(3): 271-82. DOI: https://doi.org/10.1016/J.JTTE.2016.05.005.
  • 12. Gaetano R., J. Zerubia, G. Scarpa, G. Poggi. 2011. „Morphological road segmentation in urban areas from high resolution satellite images”. 17th DSP 2011 International Conference on Digital Signal Processing, Proceedings. DOI: https://doi.org/10.1109/ICDSP.2011.6005015.
  • 13. Raziq A., A. Xu, Y. Li, A. Raziq, A. Xu, Y. Li. 2016. “Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method”. Journal of Geographic Information System 8(4): 517-525. DOI: https://doi.org/10.4236/JGIS.2016.84043.
  • 14. Sui H., N. Zhou, M. Zhou, L. Ge. 2023. “Vector Road Map Updating from High-Resolution Remote-Sensing Images with the Guidance of Road Intersection Change Detection and Directed Road Tracing”. Remote Sensing 15(7): 1840. DOI: https://doi.org/10.3390/RS15071840.
  • 15. Chen Z., L. Deng, Y. Luo, D. Li, J.J Marcato, W. Nunes Gonçalves. 2022. “Road extraction in remote sensing data: A survey”. International Journal of Applied Earth Observation and Geoinformation 112: 102833. DOI: https://doi.org/10.1016/J.JAG.2022.102833.
  • 16. Vosselman G., H-G. Maas. 2010. Airborne and Terrestrial Laser Scanning. Whittles. ISBN: 978-1904445876.
  • 17. Narwade R., V. Musande, 2014. „Automatic Road Extraction from Airborne LiDAR : A Review”. Engineering, Environmental Science 4(12): 54-62.
  • 18. Hu X., Y. Li, J. Shan, J. Zhang, Y. Zhang. 2014. “Road centerline extraction in complex urban scenes from LiDAR data based on multiple features”. IEEE Transactions on Geoscience and Remote Sensing 52(11): 7448-7456. DOI: https://doi.org/10.1109/TGRS.2014.2312793.
  • 19. Clode S., F. Rottensteiner, P. Kootsookos. 2005. “Improving city model determination by using road detection from LIDAR data”. IAPRS. Vol. XXXVI, Part 3/W24.
  • 20. Gong L., Y. Zhang, Z. Li, Q. Bao. 2010. “Automated road extraction from LiDAR data based on intensity and aerial photo”. Proceedings - 2010 3rd International Congress on Image and Signal Processing: 2130-2133. DOI: https://doi.org/10.1109/CISP.2010.5647354.
  • 21. Upadhayay S., M. Yadav, D. Singh. 2018. “Road network mapping using airborne LiDAR data”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesXLII-5: 707-711. DOI: https://doi.org/10.5194/ISPRS-ARCHIVES-XLII-5-707-2018.
  • 22. Bhutada S., N. Yashwanth, P. Dheeraj, K. Shekar. 2022. “Opening and closing in morphological image processing”. Journal of Advanced Research and Reviews 14(03): 687-695. DOI: https://doi.org/10.30574/wjarr.2022.14.3.0576.
  • 23. Czech Piotr. 2017. „Underage pedestrian road users in terms of road accidents‎”. Advances in Intelligent Systems and Computing 505: 33-44. DOI: https://doi.org/10.1007/978-3-319-43991-4_4. Springer, Cham. ISBN: 978-3-319-43990-7; 978-3-319-43991-4. ISSN: 2194-5357. In: Sierpinski Grzegorz (eds), Intelligent transport systems and travel behaviour, 13th Scientific and Technical Conference „Transport Systems Theory and Practice”, Katowice, Poland, September 19-21, 2016.
  • 24. Czech Piotr. 2017. „Physically disabled pedestrians - road users in terms of road accidents”. Advances in Intelligent Systems and Computing 505: 33-44. DOI: https://doi.org/10.1007/978-3-319-43991-4_4. Springer, Cham. ISBN: 978-3-319-43990-7; 978-3-319-43991-4. ISSN: 2194-5357. In: Sierpinski Grzegorz (eds), Intelligent transport systems and travel behaviour, 13th Scientific and Technical Conference „Transport Systems Theory and Practice”, Katowice, Poland, September 19-21, 2016.
  • 25. Simegnew Y., J. Alaba, E. Ball. 2022. “A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving”. Sensors 22(24): 9577. DOI: https://doi.org/10.3390/s22249577.
  • 26. Soilán M., A. Sánchez-Rodríguez, P. del Río-Barral, C. Perez-Collazo, P. Arias, B. Riveiro. 2019. “Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring”. Infrastructures 4(4): 58. DOI: https://doi.org/10.3390/infrastructures4040058.
  • 27. Shaoyi M., S. Yufeng Shi, Y. Qi, L. Mingyue. 2024. “A Survey of Deep Learning Road Extraction Algorithms Using High-Resolution Remote Sensing Images”. Sensors 24(5): 1708. DOI: https://doi.org/10.3390/s24051708.
  • 28. Zhang Y., Z. Zuo, X. Xiaobin, W. Jianqing, Z. Jianguo, H. Zhang, W. Jiewen, Y. Tian. 2022. “Road damage detection using UAV images based on multi-level attention mechanism”. Automation in Construction 144: 104613. DOI: https://doi.org/10.1016/j.autcon.2022.104613.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a1dde5de-19bd-4b1e-ac9f-392da5cba8a2
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