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Automatic generation of E-LOD1 from LiDAR point cloud | ||
| Earth Observation and Geomatics Engineering | ||
| مقاله 1، دوره 1، شماره 1، شهریور 2017، صفحه 16-25 اصل مقاله (2.25 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2017.230917.1004 | ||
| نویسندگان | ||
| Maryam Sajadian؛ Hossein Arefi* | ||
| School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| LiDAR as a powerful system has been known in remote sensing techniques for 3D data acquisition and modeling of the earth’s surface. 3D reconstruction of buildings, as the most important component of 3D city models, using LiDAR point cloud has been considered in this study and a new data-driven method is proposed for 3D buildings modeling based on City GML standards. In particular, this paper focuses on the generation of an Enhanced Level of Details 1 (E-LOD1) of buildings containing multi-level flat-roof structures. An important primary step to reconstruct the buildings is to identify and separate building points from other points such as ground and vegetation points. For this, a multi-agent strategy is proposed for simultaneous extraction of buildings and segmentation of roof points from LiDAR point cloud. Next, using a new method named “Grid Erosion” the edge points of roof segments are detected. Then, a RANSAC-based technique is employed for approximation of lines. Finally, by modeling of the rooves and walls, the 3D buildings model is reconstructed. The proposed method has been applied on the LiDAR data over the Vaihingen city, Germany. The results of both visual and quantitative assessments indicate that the proposed method could successfully extract the buildings from LiDAR data and generate the building models. The main advantage of this method is the capability of segmentation and reconstruction of the flat buildings containing parallel roof structures even with very small height differences (e.g. 50 cm). In model reconstruction step, the dominant errors are close to 30 cm that are calculated in horizontal distance. | ||
| کلیدواژهها | ||
| Point cloud؛ Building extraction؛ Edge detection؛ Line approximation؛ 3D RECONSTRUCTION | ||
| مراجع | ||
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Döllner, J., Buchholz, H., Brodersen, F., Glander, T., Jütterschenke, S., & Klimetschek, A. (2005, June). Smart Buildings – A concept for ad-hoc creation and refinement of 3D building models. In Proceedings of the 1st International Workshop on Next Generation 3D City Models (Vol. 1, No. 3.3). Haala, N., & Kada, M. (2010). An update on automatic 3D building reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 570-580. Maas, H. G., & Vosselman, G. (1999). Two algorithms for extracting building models from raw laser altimetry data. ISPRS Journal of photogrammetry and remote sensing, 54(2), 153-163. Alharthy, A., & Bethel, J. (2002). Heuristic filtering and 3D feature extraction from LiDAR data. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/A), 29-34. Alharthy, A., & Bethel, J. (2004, July). Detailed building reconstruction from airborne laser data using a moving surface method. In 20th Congress of International Society for Photogrammetry and Remote Sensing (pp. 213-218). Arefi, H. (2008). Levels of detail in 3D building reconstruction from LiDAR data. Kabolizade, M., Ebadi, H., & Mohammadzadeh, A. (2012). Design and implementation of an algorithm for automatic 3D reconstruction of building models using genetic algorithm. International Journal of Applied Earth Observation and Geoinformation, 19, 104-114. Satari, M., Samadzadegan, F., Azizi, A., & Maas, H. G. (2012). A Multi‐Resolution Hybrid Approach for Building Model Reconstruction from LiDAR Data. The Photogrammetric Record, 27(139), 330-359. Mongus, D., Lukač, N., & Žalik, B. (2014). Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 145-156. Song, J., Wu, J., & Jiang, Y. (2015). Extraction and reconstruction of curved surface buildings by contour clustering using airborne LiDAR data. Optik-International Journal for Light and Electron Optics, 126(5), 513-521. Yu, Y., Liu, X., & Buckles, B. P. (2010, July). A cue line based method for building modeling from LiDAR and satellite imagery. In Computing Communication and Networking Technologies (ICCCNT), 2010 International Conference on (pp. 1-8). IEEE. Awrangjeb, M., Fraser, C. S., & Lua, G. (2013, July). Integration of LiDAR data and orthoimage for automatic 3D building roof plane extraction. In Multimedia and Expo (ICME), 2013 IEEE International Conference on (pp. 1-6). IEEE. Li, H., Zhong, C., Hu, X., Xiao, L., & Huang, X. (2013). New methodologies for precise building boundary extraction from LiDAR data and high resolution image. Sensor Review, 33(2), 157-165. Arefi, H., & Reinartz, P. (2013). Building reconstruction using DSM and orthorectified images. Remote Sensing, 5(4), 1681-1703. Haala, N., & Brenner, C. (1999). Extraction of buildings and trees in urban environments. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2), 130-137. Alexander, C., Smith-Voysey, S., Jarvis, C., & Tansey, K. (2009). Integrating building footprints and LiDAR elevation data to classify roof structures and visualise buildings. Computers, Environment and Urban Systems, 33(4), 285-292. Kada, M., & McKinley, L. (2009). 3D building reconstruction from LiDAR based on a cell decomposition approach. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(Part 3), W4. Mian, A. S., Bennamoun, M., & Owens, R. A. (2004, December). Automatic multiview coarse registration of range images for 3D modeling. In Cybernetics and Intelligent Systems, 2004 IEEE Conference on (Vol. 1, pp. 158-163). IEEE. Sajadian, M., & Arefi, H. (2014). A Data Driven Method for Building Reconstruction from LiDAR Point Clouds. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(2), 225. Fischler, M. A., & Bolles, R. C. (1981). Random Aample Consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395. | ||
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