Integrating Product Coefficients for Improved 3D LiDAR Data Classification (Part II)

LiDAR point clouds, representing detailed three-dimensional descriptions of natural and built environments, are widely used in applications such as updating digital elevation models, monitoring glaciers and landslides, shoreline analysis, and urban development. A crucial step in these applications is the classification of 3D LiDAR points into semantic categories such as vegetation, man-made structures, and water. In our previous work [5], we introduced product coefficients as measure-theoretic descriptors that enrich LiDAR data with local structural information. Computed on dyadic neighborhoods around each point, these coefficients capture geometric variability beyond raw spatial coordinates.