While heritage science in museums usually focuses on the analysis of a single or a few iconic objects, material analysis of archival collections often deals with documents at mass-scale. In this setting, automation of data acquisition and data processing becomes particularly relevant. The analysis of a collection of Tudor maps of Ireland from the National Archives, London, serves as a case study to both test the applicability, and to optimise the methodology, of AI-based spectral clustering on data extracted from a commercial multiband imaging system, used typically for single-band and colour RGB imaging of archival documents, manuscripts and artworks on paper. The clusters revealed important information about the maps’ materiality, such as highlighting the different composition of painted areas within and across maps that appeared very similar in colour with the naked eye or differentiating original ink from later annotations, for instance. The clusters guided the selection of areas for further spectroscopic point analysis, both to check the performance of the algorithm and to interpret the material composition of the different clusters, with the ultimate goal of shading light on Tudor mapmaking practices. Strengths and aspects to be optimised in the methodology are finally discussed.
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