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Abstract This work aims to present a new methodological approximation to enhance and depict details in 3D models, named AsTrend. It is based on the extraction of las points from a tridimensional model, which are processed with the most common LiDAR visualisation techniques. It is being revealed as an accurate method to study the grooves of the carvings in inscriptions or petroglyphs, as it is proposed here.

Abstract The solid geometry of archaeological deposits is fundamental to the interpretation of their chronological sequence. However, such stratigraphic sequences are generally viewed as static two-dimensional diagrammatic representations which are difficult to manipulate or to relate to real layers. The ADS 3D Viewer is a web-based resource for the management and analysis of archaeological data. The viewer was developed to take advantage of recent developments in web technology, namely the adoption of WebGL (Web Graphics Library) by current web browsers. The ADS 3D Viewer combines the potential of the 3D Heritage Online Presenter (3DHOP), a software package for the web-based visualization of 3D geometries, with the infrastructure of the Archaeology Data Service (ADS) repository, in the attempt to create a platform for the visualization and analysis of 3D data archived by the ADS. Two versions of the viewer have been developed to answer the needs of different users. The first version, the Object Level 3D Viewer , was implemented to extend the browsing capability of ADS project archives by enabling the visualization of single 3D models. The second version, the Stratigraphy 3D Viewer , is an extension which allows the exploration of a specific kind of aggregated data: the multiple layers of an archaeological stratigraphic sequence. This allows those unable to participate directly in the fieldwork to access, analyse and re-interpret the archaeological context remotely. This has the potential to transform the discipline, allowing inter-disciplinary, cross-border and ‘at-distance’ collaborative workflows, and enabling easier access to and analysis of archaeological data.

Abstract 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 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 the digitisation, annotation and cataloguing of tangible cultural heritage in museums and collections. Specifically, for built heritage, a pressing challenge is that historical monuments such as buildings, temples, churches or city fortification infrastructures are hard to document due to their historic palimpsest; spatial transformations, actions of destruction, reuse of material, or continuous urban development that covers traces and changes the formal integrity and identity of a cultural heritage 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 presented workflow contributes a new approach to the identification of a building’s architectural components (e.g., arch, dome) and to the study of the stylistic influences (e.g., Gothic, Byzantine) of building parts. In doing so this workflow can assist in tracking a building’s 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.