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Research Article

Machine learning-based segmentation of aerial LiDAR point cloud data on building roof

ORCID Icon, , &
Article: 2210745 | Received 28 Nov 2021, Accepted 01 May 2023, Published online: 11 May 2023

References

  • Acar, H., Karsli, F., Ozturk, M., & Dihkan, M. (2019). Automatic detection of building roofs from point clouds produced by the dense image matching technique. International Journal of Remote Sensing, 40(1), 138–18. https://doi.org/10.1080/01431161.2018.1508915
  • Awrangjeb, M. (2016). Using point cloud data to identify, trace, and regularize the outlines of buildings. International Journal of Remote Sensing, 37(3), 551–579. https://doi.org/10.1080/01431161.2015.1131868
  • Awrangjeb, M., & Fraser, C. S. (2014a). An automatic and threshold-free performance evaluation system for building extraction techniques from airborne LIDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4184–4198. https://doi.org/10.1109/JSTARS.2014.2318694
  • Awrangjeb, M., & Fraser, C. S. (2014b). Automatic segmentation of raw LiDAR data for extraction of building roofs. Remote Sensing, 6(5), 3716–3751. https://doi.org/10.3390/rs6053716
  • Awrangjeb, M., Ravanbakhsh, M., & Fraser, C. S. (2010). Automatic detection of residential buildings using LIDAR data and multispectral imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 65(5), 457–467. https://doi.org/10.1016/j.isprsjprs.2010.06.001
  • Bassier, M., Van Genechten, B., & Vergauwen, M. (2019). Classification of sensor independent point cloud data of building objects using random forests. Journal of Building Engineering, 21, 468–477. https://doi.org/10.1016/j.jobe.2018.04.027
  • Bazazian, D., Casas, J. R., & Ruiz-Hidalgo, J., (2015), November. Fast and robust edge extraction in unorganized point clouds. In 2015 int. confe. on digital image computing: techniques and applications (DICTA), Adelaide, SA, Australia, (pp. 1–8). IEEE.
  • Becker, C., Rosinskaya, E., Häni, N., d’Angelo, E., & Strecha, C. (2018). Classification of aerial photogrammetric 3D point clouds. Photogrammetric Engineering & Remote Sensing, 84(5), 287–295. https://doi.org/10.14358/PERS.84.5.287
  • Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
  • Belton, D., & Lichti, D. D. (2006). Classification and segmentation of terrestrial laser scanner point clouds using local variance information. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(5), 44–49.
  • Ben-Shabat, Y., Lindenbaum, M., & Fischer, A., (2019). Nesti-net: Normal estimation for unstructured 3d point clouds using convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, (pp. 10112–10120).
  • Boulaassal, H., Landes, T., & Grussenmeyer, P. (2009). Automatic extraction of planar clusters and their contours on building façades recorded by terrestrial laser scanner. International Journal of Architectural Computing, 7(1), 1–20. https://doi.org/10.1260/147807709788549411
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 1–27. https://doi.org/10.1145/1961189.1961199
  • Chehata, N., Guo, L., & Mallet, C. (2009). Airborne lidar feature selection for urban classification using random forests. Laserscanning, XXXVIII(Commission III–WG III/2), 207–212 .
  • Chen, Y., Liu, G., Xu, Y., Pan, P., & Xing, Y. (2021). PointNet++ Network Architecture with Individual Point Level and Global Features on Centroid for ALS Point Cloud Classification. Remote Sensing, 13(3), 472. https://doi.org/10.3390/rs13030472
  • Chen, X., & Yu, K. (2019). Feature line generation and regularization from point clouds. IEEE Transactions on Geoscience and Remote Sensing, 57(12), 9779–9790. https://doi.org/10.1109/TGRS.2019.2929138
  • Cochran, R. N., & Horne, F. H. (1977). Statistically weighted principal component analysis of rapid scanning wavelength kinetics experiments. Analytical Chemistry, 49(6), 846–853. https://doi.org/10.1021/ac50014a045
  • Cramer, M. (2010). The DGPF test on digital aerial camera evaluation – overview and test design. Photogrammetrie – Fernerkundung – Geoinformation, 2(2010), 73–82. https://doi.org/10.1127/1432-8364/2010/0041
  • Dai, Y., Gong, J., Li, Y., & Feng, Q. (2017). Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm. International Journal of Digital Earth, 10(11), 1077–1097. https://doi.org/10.1080/17538947.2016.1269841
  • Dey, E. K., & Awrangjeb, M. (2020). A robust performance evaluation metric for extracted building boundaries from remote sensing data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4030–4043. https://doi.org/10.1109/JSTARS.2020.3006258
  • Dey, E. K., Awrangjeb, M., & Stantic, B. (2020). Outlier detection and robust plane fitting for building roof extraction from LiDAR data. International Journal of Remote Sensing, 41(16), 6325–6354. https://doi.org/10.1080/01431161.2020.1737339
  • Dey, E. K., Tarsha Kurdi, F., Awrangjeb, M., & Stantic, B. (2021). Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data. Remote Sensing, 13(8), 1520. https://doi.org/10.3390/rs13081520
  • Dos Santos, R. C., Galo, M., & Carrilho, A. C. (2018). Building Boundary Extraction from LiDAR Data Using a Local Estimated Parameter for Alpha Shape Algorithm. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42(1), 127–132. https://doi.org/10.5194/isprs-archives-XLII-1-127-2018
  • Gharineiat, Z., Tarsha Kurdi, F., & Campbell, G. (2022). Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques. Remote Sensing, 14(19), 4685. https://doi.org/10.3390/rs14194685
  • Gilani, S. A. N., Awrangjeb, M., & Lu, G. (2016). An automatic building extraction and regularisation technique using lidar point cloud data and orthoimage. Remote Sensing, 8(3), 258. https://doi.org/10.3390/rs8030258
  • Gilani, S. A. N., Awrangjeb, M., & Lu, G. (2018). Segmentation of airborne point cloud data for automatic building roof extraction. GIScience & Remote Sensing, 55(1), 63–89. https://doi.org/10.1080/15481603.2017.1361509
  • Gumhold, S., Wang, X., & MacLeod, R. S. (2001). Feature Extraction from Point Clouds. IMR, 293–305.
  • Hackel, T., Wegner, J. D., & Schindler, K. (2016). Fast semantic segmentation of 3D point clouds with strongly varying density. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 177–184. https://doi.org/10.5194/isprs-annals-III-3-177-2016
  • He, E., Chen, Q., Wang, H., & Liu, X. (2017). A curvature based adaptive neighbourhood for individual point cloud. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42, 219–225. https://doi.org/10.5194/isprs-archives-XLII-2-W7-219-2017
  • He, Y., Zhang, C., Awrangjeb, M., & Fraser, C. S. (2012). Automated reconstruction of walls from airborne lidar data for complete 3D building modelling. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, B3. https://doi.org/10.5194/isprsarchives-XXXIX-B3-115-2012
  • Karsli, F., Dihkan, M., Acar, H., & Ozturk, A. (2016). Automatic building extraction from very high-resolution image and LiDAR data with SVM algorithm. Arabian Journal of Geosciences, 9(14), 1–12. https://doi.org/10.1007/s12517-016-2664-7
  • Leichter, A., Werner, M., & Sester, M. (2020). Feature-extraction from all-scale neighborhoods with applications to semantic segmentation of point clouds. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 263–270. https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-263-2020
  • Lin, C. H., Chen, J. Y., Su, P. L., & Chen, C. H. (2014). Eigen-feature analysis of weighted covariance matrices for LiDAR point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing, 94, 70–79. https://doi.org/10.1016/j.isprsjprs.2014.04.016
  • Lin, Y., Vosselman, G., & Yang, M. Y. (2022). Weakly supervised semantic segmentation of airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 187, 79–100. https://doi.org/10.1016/j.isprsjprs.2022.03.001
  • Liu, T., Abd Elrahman, A., Morton, J., & Wilhelm, V. L. (2018). Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system. GIScience & Remote Sensing, 55(2), 243–264. https://doi.org/10.1080/15481603.2018.1426091
  • Li, X., Yao, X., & Fang, Y. (2018). Building-a-nets: Robust building extraction from high-resolution remote sensing images with adversarial networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10), 3680–3687. https://doi.org/10.1109/JSTARS.2018.2865187
  • Li, Z., Zhang, L., Zhong, R., Fang, T., Zhang, L., & Zhang, Z. (2016). Classification of urban point clouds: A robust supervised approach with automatically generating training data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(3), 1207–1220. https://doi.org/10.1109/JSTARS.2016.2628399
  • Lodha, S. K., Kreps, E. J., Helmbold, D. P., & Fitzpatrick, D., 2006, June. Aerial LiDAR data classification using support vector machines (SVM). In Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT’06), Chapel Hill, NC, USA, (pp. 567–574). IEEE.
  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  • Maltezos, E., Doulamis, A., Doulamis, N., & Ioannidis, C. (2018). Building extraction from LiDAR data applying deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 16(1), 155–159. https://doi.org/10.1109/LGRS.2018.2867736
  • Mérigot, Q., Ovsjanikov, M., & Guibas, L. J. (2010). Voronoi-based curvature and feature estimation from point clouds. IEEE Transactions on Visualization and Computer Graphics, 17(6), 743–756. https://doi.org/10.1109/TVCG.2010.261
  • Niemeyer, J., Rottensteiner, F., & Soergel, U. (2014). Contextual classification of lidar data and building object detection in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 152–165. https://doi.org/10.1016/j.isprsjprs.2013.11.001
  • Ni, H., Lin, X., Ning, X., & Zhang, J. (2016). Edge detection and feature line tracing in 3D-point clouds by analyzing geometric properties of neighborhoods. Remote Sensing, 8(9), 710. https://doi.org/10.3390/rs8090710
  • Ni, H., Lin, X. G., & Zhang, J. X. (2017). APPLICATIONS of 3D-EDGE DETECTION for ALS POINT CLOUD. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII-2/W7, 42. https://doi.org/10.5194/isprs-archives-XLII-2-W7-277-2017
  • Nurunnabi, A., West, G., & Belton, D. (2015). Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data. Pattern Recognition, 48(4), 1404–1419. https://doi.org/10.1016/j.patcog.2014.10.014
  • Özdemir, E., Remondino, F., & Golkar, A. (2019). Aerial point cloud classification with deep learning and machine learning algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 843–849. https://doi.org/10.5194/isprs-archives-XLII-4-W18-843-2019
  • Pamungkas, I. R., & Suwardi, I. S. (2015, March). 3D-building reconstruction approach using semi-global matching classified. In International Conference on Soft Computing, Intelligence Systems, and Information Technology (pp. 382–391). Springer, Berlin, Heidelberg.
  • Park, Y., & Guldmann, J. M. (2019). Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach. Computers, Environment and Urban Systems, 75, 76–89. https://doi.org/10.1016/j.compenvurbsys.2019.01.004
  • Pauly, M., Gross, M., & Kobbelt, L. P., (2002). Efficient simplification of point-sampled surfaces. In IEEE Visualization, 2002. VIS 2002, Boston, MA, USA, (pp. 163–170). IEEE.
  • Pohle-Fröhlich, R., Bohm, A., Ueberholz, P., Korb, M., & Goebbels, S. (2019). Roof Segmentation based on Deep Neural Networks. VISIGRAPP (4: VISAPP), 326–333.
  • Qi, C. R., Su, H., Mo, K., & Guibas, L. J., (2017). Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, (pp. 652–660).
  • Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30.
  • Rutzinger, M., Elberink, S. O., Pu, S., & Vosselman, G. (2009). Automatic extraction of vertical walls from mobile and airborne laser scanning data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(W8), 7–11.
  • Rutzinger, M., Rottensteiner, F., & Pfeifer, N. (2009). A comparison of evaluation techniques for building extraction from airborne laser scanning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2(1), 11–20. https://doi.org/10.1109/JSTARS.2009.2012488
  • Sampath, A., & Shan, J. (2009). Segmentation and reconstruction of polyhedral building roofs from aerial lidar point clouds. IEEE Transactions on Geoscience and Remote Sensing, 48(3), 1554–1567. https://doi.org/10.1109/TGRS.2009.2030180
  • Sanchez, J., Denis, F., Dupont, F., Trassoudaine, L., & Checchin, P. (2020). Data-driven modeling of building interiors from lidar point clouds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 395–402. https://doi.org/10.5194/isprs-annals-V-2-2020-395-2020
  • Serna, A., & Marcotegui, B. (2014). Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 243–255. https://doi.org/10.1016/j.isprsjprs.2014.03.015
  • Sterri, A. J. E. (2021). Building Boundary Extracting from Pointcloud with a Generative Adversarial Network (Master’s thesis, NTNU).
  • Tarsha Kurdi, F., & Awrangjeb, M. (2020). Automatic evaluation and improvement of roof segments for modelling missing details using Lidar data. International Journal of Remote Sensing, 41(12), 4702–4725. https://doi.org/10.1080/01431161.2020.1723180
  • Tarsha Kurdi, F., Awrangjeb, M., & Munir, N. (2021). Automatic filtering and 2D modeling of airborne laser scanning building point cloud. Transactions in GIS, 25(1), 164–188. https://doi.org/10.1111/tgis.12685
  • Tarsha-Kurdi, F., Landes, T., Grussenmeyer, P., & Smigiel, E., (2006), September. New approach for automatic detection of buildings in airborne laser scanner data using first echo only. In ISPRS Comm. III Symposium, Photogrammetric Comp. Vision, Bonn, Germany, (pp. 25–30).
  • Thomas, H., Goulette, F., Deschaud, J. E., Marcotegui, B., & LeGall, Y. (2018). Semantic classification of 3D point clouds with multiscale spherical neighborhoods. 2018 International Conference on 3D Vision (3DV), Verona, Italy, 390–398.
  • Wang, R., Peethambaran, J., & Chen, D. (2018). Lidar point clouds to 3-D urban models: A review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 606–627. https://doi.org/10.1109/JSTARS.2017.2781132
  • Wang, Z., & Prisacariu, V. A. (2020). Neighbourhood-insensitive point cloud normal estimation network. 31st British Machine Vision Conference, 2020, UK.
  • Wang, J., & Shan, J. (2009, March). Segmentation of LiDAR point clouds for building extraction. In American Society for Photogram. Remote Sens. Annual Conf (pp. 9–13).
  • Weinmann, M., Jutzi, B., Hinz, S., & Mallet, C. (2015). Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 286–304. https://doi.org/10.1016/j.isprsjprs.2015.01.016
  • 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, 5(W2), 1. https://doi.org/10.5194/isprsannals-II-5-W2-313-2013
  • Weinmann, M., Schmidt, A., Mallet, C., Hinz, S., Rottensteiner, F., & Jutzi, B. (2015). Contextual classification of point cloud data by exploiting individual 3D neigbourhoods. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (2015), II-3/W4, 271–278. Nr. W4, 2(W4). https://doi.org/10.5194/isprsannals-II-3-W4-271-2015
  • Wen, C., Yang, L., Li, X., Peng, L., & Chi, T. (2020). Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 50–62. https://doi.org/10.1016/j.isprsjprs.2020.02.004
  • Xia, S., Chen, D., Wang, R., Li, J., & Zhang, X. (2020). Geometric primitives in LiDAR point clouds: A review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 685–707. https://doi.org/10.1109/JSTARS.2020.2969119
  • Xia, S., & Wang, R. (2017). A fast edge extraction method for mobile LiDAR point clouds. IEEE Geoscience and Remote Sensing Letters, 14(8), 1288–1292. https://doi.org/10.1109/LGRS.2017.2707467
  • Xie, L., Zhu, Q., Hu, H., Wu, B., Li, Y., Zhang, Y., & Zhong, R. (2018). Hierarchical regularization of building boundaries in noisy aerial laser scanning and photogrammetric point clouds. Remote Sensing, 10(12), 1996. https://doi.org/10.3390/rs10121996
  • Xiong, B., Elberink, S. O., & Vosselman, G. (2014). A graph edit dictionary for correcting errors in roof topology graphs reconstructed from point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 227–242. https://doi.org/10.1016/j.isprsjprs.2014.01.007
  • Xu, Y., Tuttas, S., Hoegner, L., & Stilla, U. (2018). Reconstruction of scaffolds from a photogrammetric point cloud of construction sites using a novel 3D local feature descriptor. Automation in Construction, 85, 76–95. https://doi.org/10.1016/j.autcon.2017.09.014
  • Yang, Y., Tang, R., Wang, J., & Xia, M. (2021). A hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds. Computers & Geosciences, 157, 104932. https://doi.org/10.1016/j.cageo.2021.104932
  • Yang, Z., Tan, B., Pei, H., & Jiang, W. (2018). Segmentation and multi-scale convolutional neural network-based classification of airborne laser scanner data. Sensors, 18(10), 3347. https://doi.org/10.3390/s18103347
  • Yousefhussien, M., Kelbe, D. J., Ientilucci, E. J., & Salvaggio, C. (2018). A multi-scale fully convolutional network for semantic labeling of 3D point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 143, 191–204. https://doi.org/10.1016/j.isprsjprs.2018.03.018
  • Zhang, Y., Geng, G., Wei, X., Zhang, S., & Li, S. (2016). A statistical approach for extraction of feature lines from point clouds. Computers & Graphics, 56, 31–45. https://doi.org/10.1016/j.cag.2016.01.004
  • Zhang, J., Lin, X., & Ning, X. (2013). SVM-based classification of segmented airborne LiDAR point clouds in urban areas. Remote Sensing, 5(8), 3749–3775. https://doi.org/10.3390/rs5083749
  • Zhao, R., Pang, M., Liu, C., & Zhang, Y. (2019). Robust normal estimation for 3D LiDAR point clouds in urban environments. Sensors, 19(5), 1248. https://doi.org/10.3390/s19051248
  • Zhao, R., Pang, M., & Wang, J. (2018). Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network. International Journal of Geographical Information Science, 32(5), 960–979. https://doi.org/10.1080/13658816.2018.1431840