1. [1] C. Gaydon, M. Daab, and F. Roche, "FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes," arXiv preprint arXiv:2405.04634, 2024.

2. [2] S. Yang, M. Hou, and S. Li, "Three-Dimensional Point Cloud Semantic Segmentation for Cultural Heritage: A Comprehensive Review," Remote Sensing, vol. 15, no. 3, p. 548, 2023. [Online]. Available: https://www.mdpi.com/2072-4292/15/3/548. [DOI:10.3390/rs15030548]

3. [3] L. Landrieu and M. Simonovsky, "Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18-23 June 2018 2018, pp. 4558-4567, doi: 10.1109/CVPR.2018.00479. [DOI:10.1109/CVPR.2018.00479]

4. [4] R. Khan, M. Abbas, R. Anjum, F. Waheed, S. Ahmed, and F. K. Khan, Evaluating Machine Learning Techniques on Human Activity Recognition Using Accelerometer Data. 2020. [DOI:10.1109/UCET51115.2020.9205376]

5. [5] L. Tchapmi, C. Choy, I. Armeni, J. Gwak, and S. Savarese, "Segcloud: Semantic segmentation of 3d point clouds," in 2017 international conference on 3D vision (3DV), 2017: IEEE, pp. 537-547, doi: [DOI:10.48550/arXiv.1710.07563. https://doi.org/10.1109/3DV.2017.00067]

6. [6] Y. Wu, Q. Yao, X. Fan, M. Gong, W. Ma, and Q. Miao, "PANet: A Point-Attention Based Multi-Scale Feature Fusion Network for Point Cloud Registration," IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13, 2023, doi: 10.1109/TIM.2023.3271757. [DOI:10.1109/TIM.2023.3271757]

7. [7] Y. Li, R. Bu, M. Sun, W. Wu, X. Di, and B. Chen, "Pointcnn: Convolution on x-transformed points," Advances in neural information processing systems, vol. 31, 2018.

8. [8] B. Wu, A. Wan, X. Yue, and K. Keutzer, "Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud," in 2018 IEEE international conference on robotics and automation (ICRA), 2018: IEEE, pp. 1887-1893. [DOI:10.1109/ICRA.2018.8462926]

9. [9] Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, "Dynamic graph cnn for learning on point clouds," ACM Transactions on Graphics (tog), vol. 38, no. 5, pp. 1-12, 2019, doi: [DOI:10.48550/arXiv.1801.07829. https://doi.org/10.1145/3326362]

10. [10] X. Lei et al., "WSPointNet: A multi-branch weakly supervised learning network for semantic segmentation of large-scale mobile laser scanning point clouds," International journal of applied earth observation and geoinformation, vol. 115, p. 103129, 2022. [DOI:10.1016/j.jag.2022.103129]

11. [11] C. Gaydon and F. Roche, "PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests," arXiv preprint arXiv:2404.12064, 2024, doi: [DOI:10.48550/arXiv.2404.12064. https://doi.org/10.1109/WACV61041.2025.00575]

12. [12] F. Matrone, M. Paolanti, E. Frontoni, and R. Pierdicca, "Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation," International Journal of Digital Earth, vol. 17, no. 1, p. 2390457, 2024/12/31 2024, doi: 10.1080/175389.47.2024.2390457. [DOI:10.1080/17538947.2024.2390457]

13. [13] H. Yan, A. Lau, and H. Fan, "Evaluating Deep Learning Advances for Point Cloud Semantic Segmentation in Urban Environments," KN - Journal of Cartography and Geographic Information, 2025/01/23 2025, doi: 10.1007/s42489-025-00185-1. [DOI:10.1007/s42489-025-00185-1]

14. [14] Q. Hu et al., "Randla-net: Efficient semantic segmentation of large-scale point clouds," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 11108-11117. [DOI:10.1109/CVPR42600.2020.01112]

15. [15] Z. Xie, H. Liu, Y. He, Y. Shi, P. Yu, J. Ai, and L. Zhong, "Cross modal networks for point cloud semantic segmentation of Chinese ancient buildings," npj Heritage Science, vol. 13, no. 1, p. 131, 2025. [DOI:10.1038/s40494-025-01701-2]

16. [16] Delft University of Technology. "GeoTiles - Geospatial data services." Delft University of Technology. https://geotiles.citg.tudelft.nl (accessed.

17. [17] H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, "Kpconv: Flexible and deformable convolution for point clouds," in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6411-6420. [DOI:10.1109/ICCV.2019.00651]

18. [18] C. R. Qi, L. Yi, H. Su, and L. J. Guibas, "Pointnet++: Deep hierarchical feature learning on point sets in a metric space," Advances in neural information processing systems, vol. 30, 2017.