Machine Learning for the Built Heritage Archaeological Study

Abstract

The presence of artificial intelligence in our lives is increasing and being applied to fields such as medicine, engineering, telecommunications, remote sensing and 3D visualization. Nevertheless, it has never been used for the stratigraphic study of historical buildings. Thus far, archaeologists and architects, the experts in archaeology of architecture, have led this research. The method consisted of visually—and, consequently, subjectively—identifying certain evidence regarding the elevations of such buildings that could be a consequence of the passage of time. In this article, we would like to present the results from one of the research projects pursued by our group, in which we automated the stratigraphic study of some historic buildings using multivariate statistic techniques. To this end, we first measured the building using surveying techniques to create a 3D model, and then, we broke down every stone into qualitative and quantitative variables. To identify the stratigraphic features on the walls, we applied machine learning by conducting different predictive and descriptive analyses. The predictive analyses were used to rule out any blocks of stone with different characteristics, such as rough stones, joint ashlars, and voussoirs of arches; these are irregularities that probably show building processes and whose identification is crucial in ascertaining the structural evolution of the building. In supervised learning, we experimented with decision trees and random forest—and although the results were good in all cases, we ultimately opted to implement the predictive model obtained using the last one. While identifying the evidence on the walls, it was also very important to identify different continuity solutions or interfaces present on them, because although these are elements without materiality, they are of great value in terms of timescale, because they delimit different strata and allow us to deduce the relationship between them.