Data for 3D reconstruction and point cloud classification using machine learning in cultural heritage environment

Unmanned Aerial Vehicle (UAV) photogrammetry, thanks to
the development of Structure from Motion (SfM) and MultiView Stereo (MVS) algorithms, allows the generation of dense
point clouds, capable of representing three-dimensional objects and structures in a detailed and accurate manner. In addition, the possibility of associating more semantic information through automatic segmentation and classification models, becomes of fundamental importance in the field of development, protection and maintenance of Cultural Heritage
(CH). With the developments in Artificial Intelligence (AI),
classification algorithms based on Machine Learning (ML)
have been developed. In particular, the Random Forest is
used in order to perform a semantic classification of the
point cloud generated by UAV photogrammetry and Global
Navigation Satellite Systems (GNSS) survey of a structure belonging to CH environment. Indeed, this paper describes the
images collected through a UAV survey, for 3D reconstruction of Temple of Hera (Italy) based on photogrammetric
approach and georeferenced by the use of 8 Ground Control Points (GCPs) acquired by GNSS survey. In addition, the
shared dataset contains the point cloud and data for classification using Random Forest algorithm.