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NURUNNABI Abdul Awal Md

University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

Main Referenced Co-authors
TEFERLE, Felix Norman  (10)
Li, Jonathan (4)
Lindenbergh, Roderik (4)
Laefer, Debra (3)
Bai, Shuying (1)
Main Referenced Keywords
LiDAR (4); Machine Learning (4); Semantic Segmentation (4); Deep Learning (3); Neural Network (3);
Main Referenced Unit & Research Centers
Department of Geodesy and Geospatial Engineering (1)
Department of Geodesy and Geospatial Engineering, FSTM, UL (1)
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Engineering, computing & technology: Multidisciplinary, general & others (8)
Civil engineering (4)
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others (1)

Publications (total 13)

The most downloaded
117 downloads
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. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 397-404. doi:10.5194/isprs-archives-XLVI-4-W5-2021-397-2021 https://hdl.handle.net/10993/49710

The most cited

21 citations (Scopus®)

Nurunnabi, A. A. M., TEFERLE, F. N., Li, J., Lindenbergh, R., & HUNEGNAW, A. (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). ISPRS. doi:10.5194/isprs-archives-XLIII-B1-2021-31-2021 https://hdl.handle.net/10993/47602

The most significant

Nurunnabi, A. A. M., TEFERLE, F. N., Li, J., Lindenbergh, R., & HUNEGNAW, A. (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). ISPRS. doi:10.5194/isprs-archives-XLIII-B1-2021-31-2021
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.

NURUNNABI, A. A. M., Sadahiro, Y., TEFERLE, F. N., Laefer, D., & Li, J. (2023). DETECTION AND SEGMENTATION OF POLE-LIKE OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS. In DETECTION AND SEGMENTATION OF POLE-LIKE OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS. Cairo, Egypt: ISPRS. doi:10.5194/isprs-archives-XLVIII-1-W2-2023-27-2023
Peer reviewed

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. ISPRS. doi:10.5194/isprs-archives-XLVIII-4-W3-2022-111-2022
Peer reviewed

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. (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., 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. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 397-404. doi:10.5194/isprs-archives-XLVI-4-W5-2021-397-2021
Peer Reviewed verified by ORBi

Nurunnabi, A. A. M., TEFERLE, F. N., Li, J., Lindenbergh, R., & HUNEGNAW, A. (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). ISPRS. doi:10.5194/isprs-archives-XLIII-B1-2021-31-2021
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

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