AI-driven Structure Detection and Information Extraction from Historical
  Cadastral Maps (Early 19th Century Franciscean Cadastre in the Province of
  Styria) and Current High-resolution Satellite and Aerial Imagery for Remote
  Sensing
Wolfgang Göderle Christian Macher Katrin Mauthner Oliver Pimas Fabian Rampetsreiter
Cadastres from the 19th century are a complex as well as rich source for
historians and archaeologists, whose use presents them with great challenges.
For archaeological and historical remote sensing, we have trained several Deep
Learning models, CNNs as well as Vision Transformers, to extract large-scale
data from this knowledge representation. We present the principle results of
our work here and we present a the demonstrator of our browser-based tool that
allows researchers and public stakeholders to quickly identify spots that
featured buildings in the 19th century Franciscean Cadastre. The tool not only
supports scholars and fellow researchers in building a better understanding of
the settlement history of the region of Styria, it also helps public
administration and fellow citizens to swiftly identify areas of heightened
sensibility with regard to the cultural heritage of the region.