Universiteit Leiden

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Data Science Research Programme

Archaeology

The Faculty of Archaeology

The Faculty of Archaeology ranks as the best on continental Europe and is in the top ten of the world. The Faculty hosts a successful, multi-disciplinary team of researchers active over the entire globe. Increasingly, international students find their way to Archaeology in Leiden, up to 50% of all students in the master's phase.

Our main topics are human origins, the archaeology and deep history of migration, colonisation, colonial encounters, globalisation, and cultural identity.

Data Science Research Projects

Big data in archaeology: harnessing the hidden knowledge in the “graveyard” of Malta reports

Alex Brandsen

This project will investigate the analysis and indexing of the full corpus of archaeological reports produced over the last 20 years of Malta research, which is more than 60,000 in number and quickly growing. The goal is to establish a visual 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

Wouter Verschoof

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.

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