Data Science Research Programme
The Faculty of Archaeology
The Faculty of Archaeology ranks as the best on continental Europe and is in the top ten of the world. Our main topics are human origins, the archaeology and deep history of migration, colonisation, colonial encounters, globalisation, and cultural identity, as well as cultural heritage management.
Digital Archaeology is concerned with digital data for for archaeological research, and the computational methods and tools required to collect, analyse and manage it. The use of computers in archaeology goes back to the 1960s, and today archaeology is one of the most digitised disciplines among the historical and social sciences. Computer-based tools such as spatial analysis, 3D modelling, simulation, image analysis and others have opened up new avenues for archaeological enquiry, significantly broadening our understanding of the human past. Our expertise in survey, remote sensing, spatial analysis and data management covers the whole workflow of archaeological research.
Data Science Research Projects
Big data in archaeology: harnessing the hidden knowledge in the “graveyard” of Malta reports
This project investigates the analysis and indexing of the full corpus of archaeological reports produced over the last 20 years of archaeological research, which is more than 60,000 in number and quickly growing. The goal is to establish an intuitive search and querying service that allows researchers to quickly retrieve the most valuable digital resources, in order to allow them to integrate and synthesise the results into a coherent narrative of the past.
The current focus of the project is to implement Named Entity Recognition to automatically detect archaeological entities (such as artefact, time period, and so on), and integrating these into a search engine. A proof of concept has been built and is currently being used as a starting point for discussion and user requirement solicitation with a representative group of end users.
Automating archaeological object detection in remotely sensed data
Environmental data feature a variety of spatial, temporal, and spectral dimensions that potentially carry relevant information. To analyse such complex data, new tools that are tailored to archaeological requirements are required, e.g., for site detection. To this end, this project aims at a generic approach to (semi-)automated archaeological prospection that allows a wide variety of archaeological traces to be detected across different data sources.