AI for DIGILAB: A New Concept in Digital Infrastructure for Heritage Materials Research
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 focused 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 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.
The ISAAC Research Centre 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.
MAKING A DIFFERENCE
The aims of this project are to:
1. increase the incorporation of heritage materials information in the interpretation, presentation and care of collections and ultimately into large scale digitization campaigns
2. change the digitisation practice of cultural institutes by incorporating the recording of materials information using modern analytical imaging technology
3. narrow the gap in access to modern imaging technology and data science between institutes of different sizes (e.g. national versus regional) and locations (e.g. developed versus developing regions) through the use of the new concept of DIGILAB with the support of MOLAB for data collection
4. increase the collaboration in studies of colonial collections with those in the former colonies
5. demonstrate the impact arts and humanities research can have in other disciplines (e.g. remote sensing and biomedical imaging) and in industry (e.g. quality control and asset management), based on the challenging demands posed by materials research in cultural heritage.
6. in the longer term the adoption of the new concept of DIGILAB and the combined MOLAB + DIGILAB offering by E-RIHS and other research infrastructures.
Kogou, S., Shahtahmassebi, G., Lucian, A., Liang, H., Shui, B., Zhang, W., Su, B., and Van Schaik, S., 2020. From Remote Sensing and Machine Learning to the History of the Silk Road: Large Scale Material Identification on Wall Paintings. Scientific Report 10, 19312, Https://Doi.Org/10.1038/S41598-020-76457-9
Kogou, S., Lee, L., Shahtahmassebi, G. and Liang, H., 2020. A New Approach to the Interpretation of XRF Spectral Imaging Data Using Neural Networks. X-Ray Spectrometry Https://Doi.Org/10.1002/Xrs.3188
Nottingham Trent University, United Kingdom (Lead Research Organisation)
Getty Conservation Institute, United States (Project Partner)
University of Southern Maine (Project Partner)
Professor Haida Liang (Nottingham Trent University)
Dr Golnaz Shahtahmassebi (Nottingham Trent University)
Dr Marta Melchiorre Di Crescenzo (National Gallery London)
Dr Lucia Pereira Pardo (The National Archives)
Dr Aniko Bezur (Institute for the Preservation of Cultural Heritage, Yale University)
Dr Lynn Lee (Getty Conservation Institute)
Professor Matthew Edney (Osher Map Library, University of Southern Maine)
Dr Sotiria Kogou (Nottingham Trent University)
Dr Florence Liggins (Nottingham Trent University)
Dr Marcie Wiggins (IPCH, Yale)
Luke Butler (Nottingham Trent University)