29 citations · 64 across the 7 of their papers we have counts for
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eess.IV2024★ 29 cited
3DMASC: Accessible, explainable 3D point clouds classification. Application to Bi-spectral Topo-bathymetric lidar data
Mathilde Letard, Dimitri Lague, Arthur Le Guennec +5
Three-dimensional data have become increasingly present in earth observation over the last decades. However, many 3D surveys are still underexploited due to the lack of accessible…
eess.IV2023★ 17 cited
Deep Unsupervised Learning for 3D ALS Point Cloud Change Detection
Iris de Gélis, Sudipan Saha, Muhammad Shahzad +3
Change detection from traditional \added{2D} optical images has limited capability to model the changes in the height or shape of objects. Change detection using 3D point cloud \ad…