Detecting temporal evolutions in 3D lidar point clouds: an operational approach

Airborne lidar point clouds allow a dense, accurate 3D acquisition of landscapes at large scales. With the increasing number of sensors and applications, multiple surveys can now cover the same area, opening the field for effective and realistic 3D change detection. We propose here an operational approach to capture and quantify various types of changes under several acquisition configurations. A fuzzy logic approach leverages the geometric and semantic change information that was preliminarily captured, subsequently allowing to infer the types of change at a 2 m voxel level. Experiments show the validity of our solution, without supervision, and heuristic parameter tuning.