The emerging BIM methodology and the exchange data format IFC are changing the way of collaboration, visualisation, and documentation in 3D models for architectural planning and engineering. At the same time, the introduction of the Semantic Web, spreading the idea of structured, formalised and linked data, offers semantically enriched human- and machine-readable data. In contrast to civil engineering (BIM/IFC) and cultural heritage (CIDOC CRM), academic object-oriented disciplines, like archaeology, art and architecture history, are acting as outside spectators.

Since the 1990s, however, it has been argued that a 3D model is not likely to be considered a scientific reconstruction unless it is grounded on accurate documentation and visualisation. There have been many calls for an approved e-documentation related to 3D reconstruction projects, as witnessed by the London Charter (2006) and the Principles of Seville for archaeological 3D reconstructions (2011). Nonetheless, standards are still missing and the validation of the outcomes is not fulfilled. Meanwhile, the digital research data remain ephemeral and the 3D reconstruction projects continue to fill the growing digital cemeteries.

The dissertation by Irene Cazzaro focuses, in this context, on uncertainty classification and visualisation for source-based 3D reconstructions in the domain of archaeology, art and architecture history. The application of an uncertainty scale to these models is in fact of vital importance in order to declare to which extent the collected documents allow an accurate reconstruction. In order to set a standard or, at least, some general guidelines, the first step is reaching an agreement on the terminology used in the field of digital 3D reconstruction: this is the starting point of the dissertation (fig. 1). Subsequently, the techniques used to express the uncertainty degree (fig. 2-3) in a human- and machine-readable data mode are investigated. This finally conducts to the creation of a workflow applied to various models, with the aim of publishing them in a scientific repository (DFG Repository) and testing the preservation of uncertainty information (fig. 4).