An artificial neural network framework for classifying the style of cypriot hybrid examples of built heritage in 3D.

• Use of CNN to segment 3D reality capture 3D point clouds of built heritage. • Use ML for identification of the period of monuments' building parts. • Trained CNN on Cypriot built heritage. • Dataset of segmented and annotated built heritage examples. • Open, online accessible tool for education. The article presents a workflow based on Deep Neural Networks (DNNs) and Support Vector Machine (SVM) for identifying architectural stylistic influences of segmented building parts of Cypriot historical architecture in 3D. The research contributes in the field of Digital Cultural Heritage (DCH) by applying Machine Learning (ML) and Deep Learning (DL) on recently published DCH data [1] , with the aim to accelerate the segmentation and annotation process of Historic Building Information modelling (HBIM) that is currently based on time-consuming manual processes. The method presented works on reality captured data by 3D documentation techniques, precisely, Terrestrial Laser Scanning (TLS) or Photogrammetry. This workflow was developed to enable the operation of an online platform, 1 1 https://annfass-srv.cs.ucy.ac.cy. which also provides access to the building data presented here. Ultimately, the results of the presented method are accessible to scholars and students via this platform which provides multiple functionalities for researchers in the field to use. [ABSTRACT FROM AUTHOR]