Despite the accessibility challenges mentioned above, there is substantial historical research that deals either with the construction of cadastral maps, or with the information stored in them(Dolejš and Forejt 2019). Already prior to digitization, cadastral maps have played an important role in historical and particularly archeological research on micro-level(Petek and Urbanc 2004). Cadastral maps have been a research resource with regard to genealogical research for a very long time, they were used as sources in agricultural and environmental history and further environmental studies, in archeological remote sensing, in social and economic history, and with regard to the development of a broader understanding of the evolution of landscapes (Drobesch 2013; Rumpler 2013; Scharr 2024; Rumpler, Scharr, and Ungureanu 2015; Hohensinner et al. 2021). Their value, however, has frequently been delimitated by the difficult accessibility of the data and the difficulties regarding the extension of analysis beyond relatively limited borders.
-Access to historical cadastral maps in Central Europe became publicly available relatively late, with data now accessible through projects like www.mapire.eu. In most cases, the digitization of cadastral maps is accompanied by georeferencing. However, these repositories often lack comprehensive metadata and may require payment for research use. Public authorities have increasingly made parts of the Franziszeische Kataster available online for research purposes, offering data at various quality levels for free (Pivac et al. 2021).Exploration of historical map data, including cadastral maps, has accelerated due to recent advancements in machine learning(Chiang et al. 2020; Budig 2017). Deep learning technologies, in particular (L.-C. Chen et al. 2016), have revolutionized feature extraction, focusing on streets (Ekim, Sertel, and Kabadayı 2021; Can, Gerrits, and Kabadayi 2021; Jiao, Heitzler, and Hurni 2021; Uhl et al. 2022; Jiao, Heitzler, and Hurni 2022) and buildings (Heitzler and Hurni 2020; Uhl et al. 2020) among other applications (Wu et al. 2023; Rémi Petitpierre, Kaplan, and Di Lenardo 2021a; Garcia-Molsosa et al. 2021). These advancements offer new possibilities for researchers in history, archaeology, and historical geography. Recent research has focused on feature extraction from cadastral maps, particularly in the context of the Venetian cadastre and the 1900 Atlas of Paris(Oliveira et al. 2019; Remi Petitpierre and Guhennec 2023). As Petitpierre and Guhennec point out, consistent annotation is the main challenge with regard to automatized vectorization. +
Access to historical cadastral maps in Central Europe became publicly available relatively late, with data now accessible through projects like www.mapire.eu. In most cases, the digitization of cadastral maps is accompanied by georeferencing. However, these repositories often lack comprehensive metadata and may require payment for research use. Public authorities have increasingly made parts of the Franziszeische Kataster available online for research purposes, offering data at various quality levels for free (Pivac et al. 2021).Exploration of historical map data, including cadastral maps, has accelerated due to recent advancements in machine learning(Chiang et al. 2020; Budig 2017). Deep learning technologies, in particular (L.-C. Chen et al. 2016), have revolutionized feature extraction, focusing on streets (Ekim, Sertel, and Kabadayı 2021; Can, Gerrits, and Kabadayı 2021; Jiao, Heitzler, and Hurni 2021; Uhl et al. 2022; Jiao, Heitzler, and Hurni 2022) and buildings (Heitzler and Hurni 2020; Uhl et al. 2020) among other applications (Wu et al. 2023; Rémi Petitpierre, Kaplan, and Di Lenardo 2021a; Garcia-Molsosa et al. 2021). These advancements offer new possibilities for researchers in history, archaeology, and historical geography. Recent research has focused on feature extraction from cadastral maps, particularly in the context of the Venetian cadastre and the 1900 Atlas of Paris(Oliveira et al. 2019; Remi Petitpierre and Guhennec 2023). As Petitpierre and Guhennec point out, consistent annotation is the main challenge with regard to automatized vectorization.
However, the bulk of current research is directed towards the analysis of aerial and satellite imagery (Jiao, Heitzler, and Hurni 2022). This research spans a wide range of objectives and interests, with stakeholders including municipal and urban administrations, as well as archaeological remote sensing missions. Additionally, significant attention has been given to LIDAR data and research on urban landscape transformation, as well as real-time surveillance tasks involving UAVs (J. Li et al. 2022; Ji, Wei, and Lu 2019; M. Li et al. 2023; Fiorucci et al. 2020; 2022; Bickler 2021; Ren et al. 2015; Ding and Zhang 2021; Luo, Wu, and Wang 2022; Lee, Wang, and Lee 2023; K. Chen et al. 2021; Uhl et al. 2021).
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