In close cooperation between architectural historians and computer scientists, ArchiMediaL researches methods for the automatic recognition of architectures in representations that are available in different digital media and on the web. Recent advances in machine learning have made it possible to process large amounts of data and thus provide both new and novel information for the fields of architectural history. The aim is to facilitate the automatic development and linking of metadata and image content and to prepare these data for the comparative investigation of contemporary and historic built form.
The project aims at a better understanding of the investigated areas of architectural history. To this end, digital images must be separated from their existence as individual artefacts and integrated into a global network of visual sources. The project thus extends the scope of hermeneutic analysis by a quantitative reference system in which subject-specific canons and boundaries are questioned. For the dialogue between architectural history and urban form, this means careful consideration of qualitative and quantitative information and the negotiation of new methodological approaches for future studies.