Road pavement macro-texture plays a crucial role in tire-road interaction, skid resistance, and driving safety. Historically, the empirical Sand Patch Test represented the benchmark method for determining the Mean Texture Depth (MTD). However, this procedure is inherently operator-dependent, time-consuming, non-replicable, and limited to discrete, point-wise evaluations. This thesis proposes an automated digital workflow for pavement macro-texture characterization based on 3D data acquisition and custom Python algorithms developed with the support of artificial intelligence tools. To evaluate the adequacy of the utilized equipment, 3D data were collected by testing different acquisition modalities. Specifically, the workflow integrated a professional structured-light scanner (Mantis Vision F6-SR) alongside a widely accessible consumer device (iPhone 15 Plus) used to capture photographs for close-range photogrammetry.