DeepLandscape: Integrating Artificial Intelligence into practices of archaeological landscape interpretation
Project Summary
This PhD project aims to develop a method for integrating AI-led survey with contemporary topographic interpretation practices, and to reflect on the impact of the introduction of this new approach on professional practice. In the context of landscape archaeology and topographic interpretation, in order to take advantage of AI-led approaches, e.g. the automatic identification of features and changes, we must develop a framework for integrating AI into practices of archaeological landscape interpretation, a practice that is currently entirely based on individual visual observation. This requires a critical examination of our interpretive practices, interrogating the influence of our individual experiences, and asking how new interpretive practices might be developed.
The PhD project will take advantage of a pilot project on Arran, where AI-based automated identification of several classes of the islands archaeological features has been conducted within the framework of an archaeological survey of the island by Historic Environment Scotland (HES). The pilot study suggests that creating sound training sets for the identification of archaeological features in lidar topographic models is critical to the process of integrating AI-led approaches and contemporary survey practices. This task is particularly challenging because the identifications of training features are inherently highly interpretive and prejudiced by (expert) observers’ prior knowledge. To mitigate this, we must ask: How can we understand the relationship between what observers identify and what the AI identifies?
The project will have five main stages to study the relationship between human and AI led visual interpretations of topography: application of next generation DCNN methods: an exploration of deep net architectures and combinations of learned and defined features, and integration of multi-modal data-sets; method development for archaeologists’ fieldwork using AI-identifications; integration of archaeologists’ feedback; method development for archaeologists’ creating training data, and reflection on the impacts of AI methods on practice. Issues considered will include agreement between identifications by the deep net and archaeological professionals pre- and post- the addition of new training data, and responses to pre- and post- workshop surveys of archaeological professionals.
This project is embedded at HES and consequently will have direct impact on practice in Scotland. It uses lidar as its test data type, but the principles of the approach are readily expanded to HES’ archives of historic aerial photographic and more recent multi-temporal spectral dataset acquisitions and a variety of datasets increasingly produced through precision agriculture. The use of all these resources is limited by the shortage of expert human interpreters. This problem is critical, as national and international agencies responsible for the management of heritage and individual archaeological researchers working on a variety of questions from how ceramics represent trade to the role of charcoal production in the rural economy are looking to adopt AI led approaches to scale up their studies and address landscapes and assemblages as a whole, rather than through limited case studies.
The project will be formally co-supervised by Dr Rachel Opitz and Dr Jan Paul Siebert (University of Glasgow) and will include regular meetings and further supervision by Mr Dave Cowley (HES). The student will therefore be a member of both the Computer Science and Archaeology postgraduate communities at University of Glasgow and engage directly with the archaeological community at HES.
Eligibility
We encourage applications from students with the following qualifications:
- 1st class or equivalent Honours Undergraduate Degree
- 1st class / Distinction / Merit expected or earned in Master’s Degree
For non-native English speakers, applicants are required to meet the english language requirements.
A degree in Computer Science, Archaeology or allied disciplines are preferred.
Funding eligibility
To be eligible for a full award a student must have a relevant connection with the United Kingdom. A relevant connection may be established if the following criteria is met:
- The candidate has been ordinarily resident in the UK, meaning they have no restrictions on how long they can stay
- Been ‘ordinarily resident’ in the UK for 3 years prior to the start of the studentship. This means they must have been normally residing in the UK (apart from temporary or occasional absences)
- Not been residing in the UK wholly or mainly for the purpose of full-time education. (This does not apply to UK or EU nationals).
To be eligible for a fees only award:
- Students from EU countries other than the UK are generally eligible for a fees-only award. To be eligible for a fees-only award, a student must be ordinarily resident in a member state of the EU; in the same way as UK students must be ordinarily resident in the UK.
To be eligible you will also need to be accepted onto the relevant PhD programme via University of Glasgow Admissions.
Further details of funding eligibility criteria are available in the guidance notes on the SGSAH website.
How to apply
Applicants should submit a Curriculum Vitae, including contact details of one academic referee, and a 2-page covering letter outlining why they are interested in this collaborative doctoral award and what they would bring to this project.
This should be sent in an email to Rachel.Opitz@glasgow.ac.uk and Paul.Siebert@glasgow.ac.uk by 14 December 2018.
An interview will be required - date to be advised. Interviewing will enable the identification of a candidate who will liaise with the supervisory team and complete a full CDA PhD studentship application form by 13th February 2019, for consideration and final evaluation by SGSAH. Those successfully nominated will not be automatically funded.
For more information click "LINK TO ORIGINAL" below.
This opportunity has expired. It was originally published here:
https://www.gla.ac.uk/colleges/arts/graduateschool/fundingopportunities/ahrcdocawards/#/deeplandscape:integratingartificialintelligenceintopracticesofarchaeologicallandscapeinterpretation