Exploring Naming Inventories for Architectural Elements for Use in Multi-modal Machine Learning Applications*

Research output: Contribution to journalConference articleContributedpeer-review


  • Ronja Utescher - , Friedrich Schiller University Jena, Bielefeld University (Author)
  • Aaron Pattee - , Ludwig Maximilian University of Munich (Author)
  • Ferdinand Maiwald - , Friedrich Schiller University Jena (Author)
  • Jonas Bruschke - , University of Würzburg (Author)
  • Stephan Hoppe - , Ludwig Maximilian University of Munich (Author)
  • Sander Münster - , Friedrich Schiller University Jena (Author)
  • Florian Niebling - , University of Würzburg (Author)
  • Sina Zarrieß - , Bielefeld University (Author)


Computer vision models are increasingly relevant and useful to Digital History. Next to the increasingly complex neural models, data and data selection are an integral part of this process. In this paper, we examine and extend the data collection practices from a major recent paper in the domain of architectural element classification. We collected an image-text data set for a selection of 56 Baroque landmarks to be analysed in like manner. This different architectural domain yielded insights into the transferability of the original model and data collection procedures. Notably, the architectural domain also has an impact on the availability of classes of architectural elements as well as the performance of the models classifying them.


Original languageEnglish
Pages (from-to)95-105
Number of pages11
JournalCEUR Workshop Proceedings
Publication statusPublished - 2023
Externally publishedYes


Title2nd Workshop on Computational Methods in the Humanities, COMHUM 2022
Duration9 - 10 June 2022

External IDs

ORCID /0000-0002-2456-9731/work/155292070


ASJC Scopus subject areas


  • Architecture, Art History, Computer Vision, Machine Learning