Student Topics

Feature Learning and Clustering for Archaeological Pottery Typology

Master thesis

Status: available
Supervisors: Martin Kampel, Irene Ballester

Context

This thesis tackles a real-world archaeological challenge in collaboration with the Austrian Archaeological Institute (ÖAI) and Archaeological Park Carnuntum. You’ll develop computational methods to analyze a large collection of Roman pottery drawings from Carnuntum, one of the most important Roman sites along the Danube frontier (1st-4th century CE). Archaeological pottery drawings present unique challenges: varying quality, heterogeneous styles, and diverse preservation states. You’ll work on: (1) preprocessing using domain-specific tools, (2) learning meaningful feature representations, and (3) clustering to discover coherent pottery groupings.

Tasks

  1. Literature review: computer vision for technical drawings and archaeological artifacts, feature learning, clustering methods
  2. Implement and evaluate PyPotteryLens preprocessing tool; adapt for dataset-specific challenges
  3. Investigate and compare feature extraction approaches (pre-trained models, self-supervised learning, domain-specific methods)
  4. Implement and compare clustering algorithms
  5. Evaluate clustering quality through quantitative metrics and visual analysis
  6. Optional: explore integration of archaeological metadata (chronology, context, measurements…)
  7. Document methodology, experiments, and results

Deliverables

  1.  Master thesis
  2. Documented and clean code repository (preprocessing, feature extraction, clustering)
  3. Preprocessed dataset with extracted features and cluster assignments

Are You a Good Fit?

  • Strong Python programming skills (PyTorch)
  • Background in computer vision/machine learning or willingness to learn quickly
  • item Independent and systematic working style
  • Interest in interdisciplinary research and digital humanities

What to expect

  • Duration: typically 6-8 months full-time, flexible based on availability
  • Weekly meetings to dicuss progress and next steps (online or in-person)
  •  Optional: participation in interdisciplinary meetings and site visits
  • Opportunity to contribute to research publications

Contact
Irene Ballester (irene.ballester@tuwien.ac.at)
Martin Kampel (martin.kampel@tuwien.ac.at)