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.

19 世纪的地籍是历史学家和考古学家的一个复杂且丰富的来源，使用时面临巨大挑战。我们使用卷积神经网络和视觉变换器训练了几个深度学习模型，从这种知识表示中提取大规模数据。我们在这里展示了我们工作的主要结果，并展示了我们基于浏览器的工具的演示，该工具使研究人员和公众利益相关者能够快速识别出 19 世纪弗朗西斯喜安地籍中具有建筑物的地点。这个工具不仅支持学者和研究人员更好地理解斯泰里亚地区的定居历史，还帮助公共行政机构和公民迅速确定文化遗产敏感区域。

基于人工智能的历史地籍图结构检测与信息提取（19 世纪初斯蒂里亚省方济各会地籍）与当前高分辨率卫星航拍图像用于遥感

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