SEAHA (Science and Engineering in Arts Heritage and Archaeology) is a PhD research training programme delivering the next generation of research leaders in Heritage Science. It is a collaboration between UCL, Oxford and Brighton and supported by the EPSRC. SEAHA is funding 60 four-year PhD studentships between 2014 and 2022, involving over 70 heritage and industrial partners and offering an exceptionally rich and well-supported PhD experience for our future heritage scientists.
A SEAHA PhD consists of a one-year MRes at UCL, followed by a three-year PhD programme at the host institution (in this case, University of Brighton). All SEAHA projects are supervised by a team of academic, heritage and industrial supervisors, who are involved in the project throughout the four years, and often involve substantial placements with heritage or industrial partners. In addition, SEAHA students take part in additional training and professional activities with other students across the programme, have access to heritage science facilities in all three institutions and partners, and have individual research budgets of £3,000 per year.
The pace at which content for arts, heritage and archaeology is being acquired continues to accelerate in both the raw volume acquired and the variety of datatypes being recorded. For instance, Fraunhofer-IGD has developed technology which automates the acquisition of 3D digital models of cultural artefacts, making it possible for museums and other cultural organisations to digitise their collections on a large scale. However, building large collections of 3D models, and other digital assets (such as text, images, video, manuscripts), brings with it new problems of search, access and presentation.
Such processes can be supported by organising and classifying content together and providing searchable representations of properties such as shape, material or style. This project aims to develop visual analysis approaches, based on machine learning methods, to organise and classify 3D models which can be integrated into cultural heritage practice to enhance the management, accessibility and experience of digitised cultural heritage.
Key research questions
1. What challenges does the advent of large-scale 3D digitisation bring to the organisation and management of museum collections?
2. What new opportunities for visual analysis, based on machine learning methods, arise from the availability of large-scale digitised collections?
3. Can the provision of scalable acquisition tools have a significant impact on cultural heritage practice, workflows and standards in an age of large-scale digitisation?
1. Develop research scenarios in conjunction with the cultural collaborator to hypothesise new approaches for the visual analysis of 3D models that could be empowered by the availability of large-scale digital asset collections.
2. Select an experimental set of artefacts to explore the practicalities of machine learning techniques for organising and classifying large-scale digital collections.
3. Engage with potential cultural heritage researchers to develop web-based 3D-centred tools to support the visual analysis of digital collections.
4. Evaluate the degree to which the organisation and linking of digital collections’ metadata can be effectively automated and produce results that would not have been anticipated without the use of the technologies.
The University of Brighton will provide expertise in knowledge engineering, cultural heritage ontologies and digitisation campaigns. Fraunhofer-IGD will provide expertise in, and datasets resulting from, mass digitisation and access to/involvement in a re-engineered 3D repository infrastructure. Cultural partners will provide access to original artefacts and expertise in the nature of collections and the types of knowledge that it is desirable to detect within them.