  | Ballouch, Z., Jeddoub, I., Hajji, R., Kasprzyk, J.-P., & Billen, R. (31 May 2024). Towards a Digital Twin of Liege: The Core 3D Model based on Semantic Segmentation and Automated Modeling of LiDAR Point Clouds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-4/W4-2024, 13-20. doi:10.5194/isprs-annals-x-4-w4-2024-13-2024  Peer Reviewed verified by ORBi |
 | Billen, R., & Ballouch, Z. (14 May 2024). From semantic segmentation of LiDAR point clouds to 3D objects for digital twins [Paper presentation]. Beodays, Hasselt, Belgium. |
 | 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 |
 | Jeddoub, I.* , Ballouch, Z.* , Hajji, R., & Billen, R. (2024). Enriched Semantic 3D Point Clouds: An Alternative to 3D City Models for Digital Twin for Cities? In T. H. Kolbe, A. Donaubauer, ... C. Beil (Eds.), Recent Advances in 3D Geoinformation Science - Proceedings of the 18th 3D GeoInfo Conference. Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-3-031-43699-4_26 Peer reviewed* These authors have contributed equally to this work. |
 | 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 |