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

Lead Research Organisation: Nottingham Trent University
Department Name: School of Science & Technology


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.

Planned Impact

Cultural institutes, both private and public, specifically curators, conservators and scientists, including consultant/freelance/SME conservators, archaeologists and conservation scientists, and indirectly the general public that GLAM engages with. Cultural organisations of various sizes, resource and knowhow availability, location irrespective of whether they are in the global developed or developing regions.
All sectors private or public that use modern analytical imaging technology to characterise/monitor materials. These could include remote sensing, biomedical diagnostics, industrial asset monitoring.

Impact goals
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


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Description We have improved the algorithm for automated processing of large numbers of reflectance spectral imaging dataset and extended its application to other types of spectral imaging datasets such as XRF scanning datasets. Between the partners, we have designed the user interface for the AI for DIGILAB platform to be rolled out to general users. The algorithms will cluster areas with similar spectra together. In addition a data visualisation app has been developed to allow users to examine the processed data and any other derived images and spectra for interpretation.
Exploitation Route Researchers from cultural institutions or other organisations engaged in cultural heritage related areas will benefit from this new online platform for data processing.
Other disciplines may find it useful as well, however, it requires a different way of engagement.
Sectors Agriculture, Food and Drink,Construction,Digital/Communication/Information Technologies (including Software),Environment,Manufacturing, including Industrial Biotechology,Culture, Heritage, Museums and Collections,Pharmaceuticals and Medical Biotechnology

Description From Lima to Canton and Beyond: An AI-aided heritage materials research platform for studying globalisation through art
Amount £203,201 (GBP)
Organisation Arts & Humanities Research Council (AHRC) 
Sector Public
Country United Kingdom
Start 02/2021 
End 01/2024
Description Integrating Platforms for the European Research Infrastructure ON Heritage Science (IPERION HS)
Amount € 6,000,000 (EUR)
Funding ID H2020-INFRAIA-2019-1 (GA no. 871034) 
Organisation European Commission H2020 
Sector Public
Country Belgium
Start 03/2020 
End 09/2023
Description Maintaining the cutting-edge research capability of ISAAC Lab
Amount £898,135 (GBP)
Funding ID AH/V012460/1 
Organisation Arts & Humanities Research Council (AHRC) 
Sector Public
Country United Kingdom
Start 12/2020 
End 03/2021
Title AI-based automatic analysis tool of spectral imaging data 
Description One of the aims of the project is to produce a AI based data analysis platform for the future DIGILAB data processing 
Type Of Material Improvements to research infrastructure 
Year Produced 2020 
Provided To Others? No  
Impact the tool has been tested on data provided by partners and will soon be made available to others 
Title Data visualisation tool for spectral imaging and derivative images and spectra 
Description A data visualisation tool has been developed for interrogating spectral imaging data cubes, the cluster maps after processing the data using the machine learning methods and the spectra associated with the clusters or any regions. This allows the user to better visualise the data for interpretation. 
Type Of Material Improvements to research infrastructure 
Year Produced 2022 
Provided To Others? Yes  
Impact It has just been made available