A 3D digitisation workflow for architecture-specific annotation of built heritage

Contemporary discourse points to the central role that heritage plays in the process of enabling groups of various cultural or ethnic background to strengthen their feeling of belonging and sharing in the society. Safeguarding heritage is also valued highly in the
priorities of the European Commission. As a result, there have been several long-term initiatives involving digitising, annotating and cataloguing artefacts of tangible cultural heritage in museums and collections. In the case of built heritage, a specific challenge
is that historical monuments such as buildings, temples, churches and city fortification infrastructures are hard to document due to historic transformations, destruction, reuse of material, urban development that covers traces and changes the spatial configuration
of the site. The ability to reason about a monument’s form is crucial for efficient documentation and cataloguing. This paper presents a 3D digitisation workflow through the involvement of reality capture technologies for the annotation and structure analysis
of built heritage with the use of 3D Convolutional Neural Networks (3D CNNs) for classification purposes. The present workflow can contribute to the identification of a building’s architectural components (e.g., arch, dome) and to the understanding their
stylistic influences (e.g., Gothic, Byzantine) can assist in tracking its history, identifying its construction period and comparing it to other buildings of the same period. This process can contribute to educational and research activities, as well as facilitate the
automated classification of datasets in digital repositories for scholarly research in digital humanities.