SEMANTIC URBAN MESH SEGMENTATION BASED ON AERIAL OBLIQUE IMAGES AND POINT CLOUDS USING DEEP LEARNING

7 citations as recorded by crossref.

1. GeoSparseNet: A Multi-Source Geometry-Aware CNN for Urban Scene Analysis M. Afzal et al. 10.3390/rs16111827
2. Oblique Aerial Images: Geometric Principles, Relationships and Definitions S. Verykokou & C. Ioannidis 10.3390/encyclopedia4010019
3. MeshSegNet: a local to global feature fusion-based semantic segmentation approach for urban meshes H. Yu et al. 10.1080/10095020.2025.2541883
4. Hierarchical 2-D/3-D Object-Based Classification of Photogrammetric Textured Mesh Models Z. Hu et al. 10.1109/JSTARS.2024.3500012
5. Innovative AI strategies for enhancing smart building operations through digital twins: A survey A. Oulefki et al. 10.1016/j.enbuild.2025.115567
6. MeshNet-SP: A Semantic Urban 3D Mesh Segmentation Network with Sparse Prior G. Zhang & R. Zhang 10.3390/rs15225324
7. Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey J. Adam et al. 10.1016/j.jag.2023.103365