Sharing Structured Archaeological 3D Data: Open-Source Tools for Artificial Intelligence Applications and Collaborative Frameworks

This paper focuses on collaborative methods and open source tools aimed to analyze and query 3D photogrammetric models of ancient architectures. The processing of virtual models led to the constitution of a training dataset of around 1300 wall facing stones from four archaeological sites in Crete. Through a purposely-conceived add-on of the open source software Blender, some algorithms expressed in Python are able to extract archaeologically significant features and to perform processes of Machine Learning and data mining. The resulting data are imported into a dedicated DB managed through a web application based on the open source framework Django. This workflow addresses some peculiar challenges of the application of Artificial Intelligence to archaeological heritage: the lack of training dataset, particularly related to architecture; the lack of best practices for geometry processing and analysis of 3D data; the use of poorly predictive data in semi-automatic processes; the sharing of data into the scientific community; the importance of the open source technology and open data.