AI for DIGILAB: A New Concept in Digital Infrastructure for Heritage Materials Research

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Over the last 20 years, digital imaging or digitisation of collections has become the norm within museums and archives. However, so far, it has mainly been focussed on recording what humans can see with their eyes, that is, colour RGB images and sometimes laser scanning of 3D objects. Growing interest by digital humanities scholars and medievalists in advanced imaging techniques and the new layers of information they can uncover affirms that the curatorial, art historical, and historical fields are receptive to a concept that has been explored by heritage scientists for several decades. We propose that the material composition of heritage objects analysed through various modalities of imaging spectroscopy such as reflectance spectral imaging and macro X-ray fluorescence (MA-XRF) scanning may be incorporated into digitisation campaigns to deliver a disruptive transformation in arts and humanities scholarship related to heritage. Since each material combination has its unique spectrum, imaging spectroscopy, depending on the modality, records to a greater or lesser extent, the material makeup (e.g. the pigments, dyes, binders, substrates) of an object. These added layers of information about heritage objects can lead to new insights and narratives about their creation, history of trade and cultural influences, and can impact significantly on conservation and preservation decisions. In addition, reflectance spectral imaging in the visible naturally gives the most accurate colour images thus removing the need for recording colour images. Nottingham Trent University's (NTU) ISAAC research group has made the first step in automatic collection of high spatial resolution reflectance spectral images of tens of square metres of wall paintings. Automatic data collection increases significantly the rate of data generation necessitating an automatic tool to process and reduce the data. While ML/AI has been used mostly in searching and organising digital content in the sector, the ISAAC team has pioneered their use in large scale heritage materials analysis. In addition to a new bespoke digital tool, we propose a new concept in digital research infrastructure through making the tool available to users remotely. The European Research Infrastructure for Heritage Science is divided into 4 platforms of operations: archives (ARCHLAB), mobile laboratory (MOLAB), fixed laboratory (FIXLAB) and digital laboratory (DIGILAB). While the first three platforms are well established, DIGILAB is in the concept phase, but offers opportunities for transformation in terms of access to digital tools and resources. Here we propose a model where DIGILAB functions as a data analysis lab where the user is helped remotely with their data analysis. The ML code will automatically process the image cubes into materials cluster maps and the experts will examine the results before releasing it to the users. A user interface and a visualisation add-on will also be developed to allow the user to view the outputs in a user-friendly manner. The remote access to digital lab aspect of the project aims to lower the barriers collections and scholars face in unlocking potentially relevant information encoded in the identity and distribution of materials used in the creation of heritage objects. Three varied case studies, each chosen for their different data-related challenges, will also serve to demonstrate and address issues in the workflow, starting from scientific data collection, then the new concept of DIGILAB for data reduction leading to material identification using complementary spectroscopic techniques, to finally address the research questions in history and conservation.