In Architectural Heritage, the masonry’s interpretation is an instrument for analyzing the construction phases, the assessment of structural properties, and the monitoring of its state of conservation.
This work is generally carried out by specialists that, based on visual observation and their knowledge, manually annotate ortho-images of the masonry generated by photogrammetric surveys.
This time-consuming manual work, often done with tools that have not been designed for this purpose, represents a bottleneck in the documentation and management workflow and is
a severely limiting factor in monitoring large-scale monuments.
The work proposed in this paper explores the potential of AI-based solutions to improve the efficiency of masonry annotation in Architectural Heritage, experimenting the use of the TagLab open source tool (developed by the Visual Computing Lab), defining a workflow that support and empower the specialists’ expertise.