biostudies-literatureUnknown42Pepe MUnmanned Aerial Vehicle (UAV) photogrammetry, thanks to the development of Structure from Motion (SfM) and Multi-View 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 photogrammeData in brief108250https://www.ebi.ac.uk/biostudies/studies/S-EPMC9117533biostudies-literatureData for 3D reconstruction and point cloud classification using machine learning in cultural heritage environment.PMC9117533Pepe MCostantino DScaringi DAlfio VSfalseData 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 Multi-View 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 photogramme2022-01-01T00:00:00Z2022 Jun2025-05-18T12:04:16.141Z2025-04-05T20:40:00.294ZS-EPMC91175333559983210.1016/j.dib.2022.108250