Information extraction system for urban planning and governance using LiDAR based 3D repository

References

• Abdul-Rahman, A., Zlatanova, S., and Coors, V., 2006. Innovations in 3D geo information systems. Berlin Heidelberg: Springer.
   Google Scholar
• Asaeedi, S., Didehvar, F., and Mohades, A., 2017. α-Concave hull, a generalization of convex hull. Theoretical Computer Science, 702, 48–59. doi:10.1016/j.tcs.2017.08.014.
   Web of Science ®Google Scholar
• Ben, H., et al., 2012. From unstructured 3D point clouds to structured knowledge - a semantics approach. In: Semantics - advances in theories and mathematical models. InTech.
   Google Scholar
• Cura, R., Perret, J., and Paparoditis, N., 2015. Point cloud server (pcs): point clouds in-base management and processing. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3 (W5), 531–539. doi:10.5194/isprsannals-II-3-W5-531-2015.
   Google Scholar
• Dayal, S., et al., 2021. Comprehensive airborne laser scanning (als) simulation. Journal of the Indian Society of Remote Sensing, 49 (7), 1603–1622. doi:10.1007/s12524-021-01334-5.
   Web of Science ®Google Scholar
• Deng, M. 2020. Using foreign data wrapper in PostgreSQL to expose point clouds on file system (MSc thesis in Geomatics). Delft University of Technology.
   Google Scholar
• Deng, T., Zhang, K., and Shen, Z.J.M., 2021. A systematic review of a digital twin city: a new pattern of urban governance toward smart cities. Journal of Management Science and Engineering, 6 (2), 125–134. doi:10.1016/j.jmse.2021.03.003.
   Google Scholar
• El-Mahgary, S., Virtanen, J.P., and Hyyppä, H., 2020. A simple semantic-based data storage layout for querying point clouds. ISPRS International Journal of Geo-Information, 9 (2), 72. doi:10.3390/ijgi9020072.
   Web of Science ®Google Scholar
• Fuller, A., et al., 2020. Digital twin: enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. doi:10.1109/ACCESS.2020.2998358.
   Web of Science ®Google Scholar
• Huang, J., et al., 2022. City3D: large-scale urban reconstruction from airborne point clouds. Remote Sensing, 14 (9) .
   Web of Science ®Google Scholar
• Iman Zolanvari, S.M., et al., 2019. DublinCity: annotated LiDAR point cloud and its applications. arXiv, 1–13.
   Google Scholar
• Ismagilova, E., et al., 2019. Smart cities: advances in research—an information systems perspective. International Journal of Information Management, 47, 88–100. doi:10.1016/j.ijinfomgt.2019.01.004.
   Web of Science ®Google Scholar
• Kiemle, C., et al., 2013. Detection and analysis of water vapor transport by airborne lidars. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6 (3), 1189–1193. doi:10.1109/JSTARS.2013.2239609.
   Web of Science ®Google Scholar
• Knublauch, H., et al., 2004. The protégé OWL plugin: an open development environment for semantic web applications. In: S.A. McIlraith, D. Plexousakis and F. Harmelen, eds. The semantic web – ISWC 2004. Berlin Heidelberg: Springer, 229–243.
   Google Scholar
• Lewis, P., Mc Elhinney, C.P., and McCarthy, T. 2012. LiDAR data management pipeline; from spatial database population to web-application visualization. ACM International Conference Proceeding Series, Reston, VA, USA, May 2014.
   Google Scholar
• Ma, H., et al., 2022. Extracting urban road footprints from airborne LiDAR point clouds with PointNet and two-step post-processing. Remote Sensing, 14 (3), 789. doi:10.3390/rs14030789
   Web of Science ®Google Scholar
• Meyer, T. and Brunn, A. 2019. 3D point clouds in postgreSQL/POstGIS for applications in GIS and geodesy. GISTAM 2019 - Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management. Heraklion, Crete, Greece: Gistam, 154–163.
   Google Scholar
• Namouchi, S. and Farah, I., 2021. Graph-based classification and urban modeling of laser scanning and imagery: toward 3D smart web services. Remote Sensing, 14 (1), 114. doi:10.3390/rs14010114
   Web of Science ®Google Scholar
• Pajić, V., Govedarica, M., and Amović, M., 2018. Model of point cloud data management system in big data paradigm. ISPRS International Journal of Geo-Information, 7 (7), 265. doi:10.3390/ijgi7070265.
   Web of Science ®Google Scholar
• Pfeifer, N., et al., 2014. Opals – a framework for airborne laser scanning data analysis. Computers, Environment and Urban Systems, 45, 125–136. doi:10.1016/j.compenvurbsys.2013.11.002.
   Web of Science ®Google Scholar
• Poux, F., et al., 2017. Digital investigations of an archaeological smart point cloud: a real time web-based platform to manage the visualisation of semantical queries. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42 (5W1), 581–588.
   Google Scholar
• Ramiya, A.M., Nidamanuri, R.R., and Krishnan, R., 2017. Segmentation based building detection approach from lidar point cloud. The Egyptian Journal of Remote Sensing and Space Science, 20 (1), 71–77. doi:10.1016/j.ejrs.2016.04.001.
   Web of Science ®Google Scholar
• Ramsey, P., 2013. LIDAR in PostgreSQL with PointCloud. Nottingham, England: Foss4G.
   Google Scholar
• Richter, R. and Döllner, J., 2014. Concepts and techniques for integration, analysis and visualization of massive 3D point clouds. Computers, Environment and Urban Systems, 45, 114–124. doi:10.1016/j.compenvurbsys.2013.07.004.
   Web of Science ®Google Scholar
• Rodrguez-Puerta, F., et al., 2022. Mapping tree canopy in urban environments using point clouds from airborne laser scanning and street level imagery. Sensors, 22 (9), 3269. doi:10.3390/s22093269.
   PubMed Web of Science ®Google Scholar
• Rusu, R.B., 2010. Semantic 3D object maps for everyday manipulation in human living environments. KI-Künstliche Intelligenz, 24 (4), 345–348. doi:10.1007/s13218-010-0059-6.
   Google Scholar
• Shahat, E., Hyun, C.T., and Yeom, C., 2021. City digital twin potentials: a review and research agenda. Sustainability, 13 (6), 3386. doi:10.3390/su13063386.
   Web of Science ®Google Scholar
• Shen, W., 2008. Building boundary extraction based on lidar point clouds data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII, 157.
   Google Scholar
• Silva, B.N., Khan, M., and Han, K., 2018. Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697–713. doi:10.1016/j.scs.2018.01.053.
   Web of Science ®Google Scholar
• Tian, D., et al., 2022. Lidar super-resolution based on segmentation and geometric analysis. IEEE Transactions on Instrumentation and Measurement, 71, 1–17.
   Web of Science ®Google Scholar
• Tseng, Y.H. and Hung, H.C., 2016. Extraction of Building Boundary Lines From Airborne Lidar Point Clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B3, 957–962. July. doi:10.5194/isprs-archives-XLI-B3-957-2016
   Google Scholar
• United Nations. 2020. World cities report, the value of sustainable urbanization.
   Google Scholar
• Varney, N., Asari, V.K., and Graehling, Q. 2020. Dales: a large-scale aerial lidar data set for semantic segmentation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, Seattle, WA, USA, 186–187.
   Google Scholar
• Wehr, A. and Lohr, U., 1999. Airborne laser scanning—an introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing, 54 (2), 68–82. doi:10.1016/S0924-2716(99)00011-8.
   Web of Science ®Google Scholar
• Wijga-Hoefsloot, M., 2012. Point clouds in a database: data management within an engineering company. Delft University of Technology .
   Google Scholar
• WorldBank. 2022. Urban development.
   Google Scholar
• Xiong, H., et al., 2022. Road-model-based road boundary extraction for high definition map via lidar. IEEE Transactions on Intelligent Transportation Systems, 23 (10), 18456–18465. doi:10.1109/TITS.2022.3153689.
   Web of Science ®Google Scholar
• Yadav, M., Singh, A.K., and Lohani, B., 2017. Extraction of road surface from mobile lidar data of complex road environment. International Journal of Remote Sensing, 38 (16), 4655–4682. doi:10.1080/01431161.2017.1320451.
   Web of Science ®Google Scholar
• Zhang, M., et al., 2023. Morphological transformation and spatial-logical aggregation for tree species classification using hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–12. doi:10.1109/tgrs.2022.3233847
   Web of Science ®Google Scholar
• Zhang, P., et al., 2022. 3D urban buildings extraction based on airborne lidar and photogrammetric point cloud fusion according to u-net deep learning model segmentation. IEEE Access, 10, 20889–20897. doi:10.1109/ACCESS.2022.3152744
   Web of Science ®Google Scholar
• Zhu, Q., Gong, J., and Zhang, Y., 2007. An efficient 3D r-tree spatial index method for virtual geographic environments. ISPRS Journal of Photogrammetry and Remote Sensing, 62 (3), 217–224. doi:10.1016/j.isprsjprs.2007.05.007
   Web of Science ®Google Scholar