The Geography & Map Division recently digitized an important set of maps of Austria-Hungary. In this post, we explore these 19th- and early 20th-century maps and the layers of history and language that they contain. Every Bridge and Meadow: The Austro-Hungarian Empire in 19th Century Maps | Worlds Revealed Top of page Are you Austria- hungry for maps of Austria-Hungary? If so, you are in luck! The Geography and Map Division, in collaboration with the Collections Digitization Division, recently completed digitization of a large (and I mean large ) set of maps. The new digital collection comprises all editions of all sheets of the Spezialkarte der österreichisch-ungarischen Monarchie , a detailed topographic survey of the country at a 1:75,000 scale, held in the Geography and Map Division for a grand total of 6,346 digital images. Index map to Spezialkarte der ö̈sterreichisch-ungarischen Monarchie . The empire was multiethnic, with large Hungarian, German, Czech, Jewish, Romanian, Ukrainian, and various other populations. Dozens of languages were spoken daily; this multilingual situation is reflected in the Spezialkarte , which frequently provides translations for place names. For an example, let’s look at the hometown of 20 th -century cartographer Erwin Raisz. His obituary in Annals of the Association of American Geographers lists his birthplace as Lőcse, Hungary. Lőcse appears on the 1912 edition of sheet 4365 of the Spezialkarte . Beneath the name Lőcse, the German name for the town appears: Leutschau. Detail of sheet 4365. Spezialkarte der ö̈sterreichisch-ungarischen Monarchie . K.u.K. Militärgeographisches Institut, 1912. Geography and Map Division. In this 1894 edition, however, Leutschau is given prominence, with the Hungarian Lőcse and Slovak Levoce appearing in parentheses. Today, the town is located in Slovakia, and can be found on modern maps with the spelling Levoča. Detail of sheet 4365. Spezialkarte der ö̈sterreichisch-ungarischen Monarchie . K.u.K. Militärgeographisches Institut, 1894. Geography and Map Division. Older gazetteers may list the  Comitat and  Bezirk in which the town is located; modern gazetteers frequently include geographic coordinates. The research guide  Cartographic Resources for Genealogical Research: Eastern Europe and Russia has more information on using this map set for genealogical research. Aside from its usefulness in locating toponyms, the  Spezialkarte provides a fascinating snapshot of land use in this part of Europe during the period of coverage. A peek at the map key, found in the beginning of the set, shows the dazzling variety of features depicted on these maps: Key to symbols. Spezialkarte der ö̈sterreichisch-ungarischen Monarchie . K.u.K. Militärgeographisches Institut, [1875?-1945?]. Geography and Map Division. As an example for non-German readers, the section labeled “Culturen” shows the symbols used to distinguish between (from left to right) arable land, meadows and pastures, vineyards, hop gardens, rice fields, individual trees and groups of trees, bushes, sheds, forest with cut-throughs, fruit and vegetable gardens, and sand. Myriad symbols are used throughout the maps to indicate natural and human-made features. As the maps span several decades, interesting comparisons can be made across maps of the same area, with the caveat that use of symbols may have varied over time. Several areas of trees to the east of Moschendorf, labeled “Saroslaki erdo” (Saroslak forest) in the 1896 map, have disappeared by 1935, as has the label. And the change in the symbol for the church in Moschendorf from a circle to a triangle indicates that by 1935 it was being used as a trigonometrical survey point. With such a wealth of information contained in these maps, we’ve provided multiple ways to access them. In addition to the digitized sheets, an earlier, experimental digitized version of the set can be downloaded as a dataset from LC Labs. The Austro-Hungarian map set data package contains 4,998 georeferenced TIFF image files (as well as non-georeferenced versions). And, of course, the maps can be viewed in person in the Geography and Map Reading Room . Learn More: Find tips for using this set for genealogical research in our research guide Cartographic Resources for Genealogical Research: Eastern Europe and Russia Download the set as an experimental data package Comments (2) --> James C. Armstrong says: April 11, 2025 at 6:13 pm A splendidly informative piece. Congratulations! Jessica jolly says: May 19, 2025 at 5:49 pm I am listening to Peter Judson’s book “The Hapsburg Empire” and these maps help a lot, as well as your helpful note about changing place names. See All Comments Add a Comment Cancel reply Your email address will not be published. Required fields are marked * Name (no commercial URLs) * Email (will not be published) * Comment: Δ Opens in a new window

Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes * E-mail: miller.81@osu.edu 28 6 2023 2023 18 6 437135 e0286340 17 11 2022 13 5 2023 28 06 2023 29 06 2023 29 06 2023 © 2023 Lin et al 2023 Lin et al https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods. The author(s) received no specific funding for this work. The development of the streetcar and the personal automobile profoundly altered the millennia-old urban development patterns that were constrained by walking as the primary mode of transport. In the mid to late 20th century, construction of urban highways combined with Federal support for mortgage lending favoring new construction helped to encourage widespread suburbanization, partly through the Federal-Aid Highway Act (1956), continuing the drop in population densities in central cities [ 1 ]. Historical maps are a valuable resource for geo-humanities research because they often contain retrospective geographic information that can be difficult to find elsewhere [ 10 – 15 ]. Among the many historical maps, Sanborn Fire Insurance maps provide highly detailed historic building-level urban information in over 12,000 American cities and towns dating back to the 19th century [ 16 , 17 ] (see a more detailed discussion in the Background section). Originally created in the late 19th and early 20th centuries to evaluate fire insurance liability, Sanborn maps have been continuously produced through today and covered more than 12,000 American cities and towns. Atlas pages contain information such as street names, parcel boundaries, block numbers, building footprints, as well as the construction materials, utilization, and number of stories of each building ( Fig 1 ). Digital scans of Sanborn maps are now widely available in a variety of online archives [ 18 ], including the Library of Congress’s digital collection, which enables the analysis of Sanborn maps on a large scale. Creating immersive and interactive 3D digital models using information from Sanborn maps can be highly beneficial in the applications of these maps within the humanities and social sciences. These models offer realistic 3D representations of past urban environments that can be compared across different time periods, and even contrasted with present-day urban landscapes, which can provide scholars and planners with an intuitive and comprehensive understanding of what has been lost or gained during urban However, since the labels on Sanborn maps are in handwriting styles, existing OCR techniques, which are typically designed to detect printed text, may result in low accuracy in textual detection for Sanborn maps [ 60 ]. In addition, most CNN-based models for text detection are trained on railroad [ 61 ] or topographical maps [ 62 ] instead of labeled textual data from Sanborn maps, and thus applying existing CNN-based models to detect building properties on Sanborn maps may also be ineffective. This setting is adopted in this paper. Fig 6 illustrates the training and evaluation of two machine learning models for detecting building utilizations and numbers of stories. We use data sets U 1 and S 1 to train the two machine learning models, M 1 and M 2, respectively. We evaluate the trained models on data sets U 2 and S 2 using the AP metric. We then apply the trained models to the Sanborn maps to obtain the building utilizations and numbers of stories. 10.1371/journal.pone.0286340.g006 Fig 6 Detecting building utilizations and numbers of stories. cities from the late 19th to late 20th centuries. Our experimental results show that our workflow is effective at extracting information with high accuracy and creating realistic 3D digital models of historic urban neighborhoods. This research is an essential step toward exploring and demonstrating the potential of computational methods for urban studies within and beyond the humanities. The proposed workflow has the potential to be applied to other geographic areas and time periods, albeit depending on the availability of the Sanborn maps and software and computational resources. This would enable us to make use of a broader resource of digital Sanborn maps. Creating 3D visualizations using the proposed framework requires georeferenced Sanborn maps, which can often be obtained from established sources like ProQuest or through manual georeferencing. Recent advancements in automated georeferencing have also made it possible to derive these maps efficiently. For example, research on automated identification of landmarks [ 93 ] and road intersections [ 33 ] makes it possible for efficient control points retrieval.