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.
In collaboration with the Data Science Research Programme we are currently developing tailor-made computational methods and tools that help us to extract relevant archaeological information from large bodies of digital data, such as LiDAR point clouds and excavation reports, in a largely automated way. This line of interdisciplinary research is a response to what has been called the data explosion or data deluge that archaeology is now facing due to the increasing availability of large amounts of open archaeological, environmental, textual and other digital data relevant to 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 use of Deep learning in the automated detection of archaeological objects in remotely sensed data
Wouter Verschoof-van der Vaart
This research project explores recent advancements in computer sciences, such as Deep learning and Convolutional Neural Networks (CNNs), in order to develop a generic, flexible and robust automated detection method for archaeological objects in remotely sensed data. The aim of the project is to implement these methods into a user-friendly workflow.