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Ballouch Zouhair

Département de géographie > Geospatial Data Science and City Infor. Modelling (GeoScITY)

SPHERES

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Main Referenced Co-authors
Billen, Roland  (5)
Hajji, Rafika  (4)
Kharroubi, Abderrazzaq  (4)
Poux, Florent  (3)
Hajji, Rafika (2)
Main Referenced Keywords
deep learning (3); Lidar (3); 3D point cloud (2); data fusion (2); Deep learning (2);
Main Referenced Unit & Research Centers
SPHERES - ULiège [BE] (1)
Main Referenced Disciplines
Earth sciences & physical geography (8)
Computer science (2)
Engineering, computing & technology: Multidisciplinary, general & others (1)

Publications (total 8)

The most downloaded
240 downloads
Ballouch, Z., Hajji, R., & Ettarid, M. (2022). Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urban Areas. In Geospatial Intelligence. Springer International Publishing. doi:10.1007/978-3-030-80458-9_6 https://hdl.handle.net/2268/290748

The most cited

9 citations (Scopus®)

Ballouch, Z., Hajji, R., Poux, F., Kharroubi, A., & Billen, R. (16 July 2022). A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning. Remote Sensing, 14 (14), 3415. doi:10.3390/rs14143415 https://hdl.handle.net/2268/294734

Kharroubi, A., Ballouch, Z., Hajji, R., Yarroudh, A., & Billen, R. (09 April 2024). Multi-Context Point Cloud Dataset and Machine Learning for Railway Semantic Segmentation. Infrastructures, 9 (4), 71. doi:10.3390/infrastructures9040071
Peer Reviewed verified by ORBi

Ballouch, Z., Hajji, R., Kharroubi, A., Poux, F., & Billen, R. (2024). Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds. Remote Sensing, 16 (2), 329. doi:10.3390/rs16020329
Peer Reviewed verified by ORBi

Ballouch, Z., Billen, R., & Kasprzyk, J.-P. (2024). OBTENTION D’OBJETS SÉMANTIQUES 3D POUR LES APPLICATIONS URBAINES - SEM3D. https://orbi.uliege.be/handle/2268/314705

Kharroubi, A., Poux, F., Ballouch, Z., Hajji, R., & Billen, R. (17 October 2022). Three Dimensional Change Detection Using Point Clouds: A Review. Geomatics, 2 (4), 457-486. doi:10.3390/geomatics2040025
Peer reviewed

Ballouch, Z., Hajji, R., Poux, F., Kharroubi, A., & Billen, R. (16 July 2022). A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning. Remote Sensing, 14 (14), 3415. doi:10.3390/rs14143415
Peer Reviewed verified by ORBi

Ballouch, Z., Hajji, R., & Ettarid, M. (2022). Toward a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar Point Clouds in Urban Areas. In Geospatial Intelligence. Springer International Publishing. doi:10.1007/978-3-030-80458-9_6
Peer reviewed

Ballouch, Z., & Hajji, R. (2021). Semantic Segmentation of Airborne LiDAR Data for the Development of an Urban 3D Model. Building Information Modeling for a Smart and Sustainable Urban Space, 113-130. doi:10.1002/9781119885474.ch7
Peer reviewed

Ballouch, Z., Hajji, R., & Ettarid, M. (2020). The contribution of deep learning to the semantic segmentation of 3D point-clouds in urban areas. In Proceedings - 2020 IEEE International Conference of Moroccan Geomatics, MORGEO 2020, art. no. 9121898. Institute of Electrical and Electronics Engineers Inc. doi:10.1109/Morgeo49228.2020.9121898
Peer reviewed

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