Ricerc@Sapienza

The recent years saw an extensive use of 3D point cloud data for heritage documentation, valorisation and visualisation. Although rich in metric quality, these 3D data lack structured information such as semantics and hierarchy between parts. In this context, the introduction of point cloud classification methods can play an essential role for better data usage, model definition, analysis and conservation. The paper aims to extend a machine learning (ML) classification method with a multi-level and multi-resolution (MLMR) approach.

Preliminary analysis of an archaeological site requires the acquisition of information by several diverse diagnostic techniques. Remote sensing plays an important role especially in spatially ex-tended and not easily accessible sites for the purposes of preventive and rescue archaeology, landscape archaeology, and intervention planning. In this paper, we present a case study of a de-tailed topographic survey based on a light detection and ranging (LiDAR) sensor carried by an unmanned aerial vehicle (UAV; also known as drone).