  | Nurunnabi, A. A. M. (2021). An efficient deep learning approach for ground point filtering in aerial laser scanning point clouds. In A. A. M. Nurunnabi, F. N. Teferle, J. Li, R. Lindenbergh, ... A. Hunegnaw, An efficient deep learning approach for ground point filtering in aerial laser scanning point clouds (pp. 31-38).  Peer reviewed |
  | González-Collazo, S. M., Balado, J., González, E., & NURUNNABI, A. A. M. (15 November 2023). A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation. Expert Systems with Applications, 230, 120672. doi:10.1016/j.eswa.2023.120672 Peer Reviewed verified by ORBi |
 | Nurunnabi, A. A. M., & Teferle, F. N. (07 October 2023). Maschinelles Lernen in der automatischen Auswertung von Punktwolken: Beispiele der Erfahrungen an der Universität Luxemburg [Paper presentation]. Kleiner Geodätentag Rheinland-Pfalz, Saarland, Luemburg, Kaiserslautern, Germany. |
 | Xie, T., Kong, R., NURUNNABI, A. A. M., Bai, S., & Zhang, X. (2023). Machine-Learning-Method-Based Inversion of Shallow Bathymetric Maps Using ICESat-2 ATL03 Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 3697 - 3714. doi:10.1109/JSTARS.2023.3260831 Peer Reviewed verified by ORBi |
 | Nurunnabi, A. A. M., Teferle, F. N., Balado, J., Chen, M., Poux, F., & Sun, C. (2022). Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds. In Robust Techniques for Building Footprint Extraction in Aerial Laser Scanning 3D Point Clouds. Peer reviewed |
 | Nurunnabi, A. A. M., Teferle, F. N., Laefer, D., Remondino, F., Karas, I. R., & Li, J. (2022). kCV-B: Bootstrap with Cross-Validation for Deep Learning Model Development, Assessment and Selection. In kCV-B: Bootstrap with Cross-Validation for Deep Learning Model Development, Assessment and Selection. Peer reviewed |
 | Nurunnabi, A. A. M., & Teferle, F. N. (2022). Resampling methods for a reliable validation set in deep learning based point cloud classification. In Resampling methods for a reliable validation set in deep learning based point cloud classification. Peer reviewed |
 | Nurunnabi, A. A. M., Lindenbergh, R., & Teferle, F. N. (2022). Deep Learning for Ground and Non-ground Surface Separation: A Feature-based Semantic Segmentation Algorithm for Point Cloud Classification. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/52284. |
 | Nurunnabi, A. A. M., Teferle, F. N., Lindenbergh, R., Li, J., & Zlatanova, S. (2022). Robust Approach for Urban Road Surface Extraction Using Mobile Laser Scanning Data. In Robust Approach for Urban Road Surface Extraction Using Mobile Laser Scanning Data. Peer reviewed |
 | Nurunnabi, A. A. M., Teferle, F. N., Laefer, D., Lindenbergh, R., & Hunegnaw, A. (2022). A TWO-STEP FEATURE EXTRACTION ALGORITHM: APPLICATION TO DEEP LEARNING FOR POINT CLOUD CLASSIFICATION. In A TWO-STEP FEATURE EXTRACTION ALGORITHM: APPLICATION TO DEEP LEARNING FOR POINT CLOUD CLASSIFICATION (pp. 401-408). Peer reviewed |
 | Nurunnabi, A. A. M., Teferle, F. N., Li, J., Lindenbergh, R. C., & Parvaz, S. (2021). INVESTIGATION OF POINTNET FOR SEMANTIC SEGMENTATION OF LARGE-SCALE OUTDOOR POINT CLOUDS. doi:10.5194/isprs-archives-XLVI-4-W5-2021-397-2021 Peer reviewed |
| Nurunnabi, A. A. M. (2021). An efficient deep learning approach for ground point filtering in aerial laser scanning point clouds. In A. A. M. Nurunnabi, F. N. Teferle, J. Li, R. Lindenbergh, ... A. Hunegnaw, An efficient deep learning approach for ground point filtering in aerial laser scanning point clouds (pp. 31-38). Peer reviewed |
 | Sha, Z., Chen, Y., Li, W., Wang, C., Nurunnabi, A. A. M., & Li, J. (2020). A boundary-enhanced supervoxel method for extraction of road edges in MLS point clouds. In A boundary-enhanced supervoxel method for extraction of road edges in MLS point clouds (pp. 65-71). ISPRS. doi:10.1109/IGARSS39084.2020.9323330 Peer reviewed |