![]() ![]() ![]() One of the neighborhoods, Hanford Village, was developed in 1946 to house returning Black veterans of World War II. The researchers tested their machine learning technique on two adjacent neighborhoods on the near east side of Columbus, Ohio, that were largely destroyed in the 1960s to make way for the construction of I-70. “We are able to get a very good idea of what the buildings look like from data we get from the Sanborn maps,” Lin said. Study co-author Yue Lin, a doctoral student in geography at Ohio State, developed machine learning tools that can extract details about individual buildings from the maps, including their locations and footprints, the number of floors, their construction materials and their primary use, such as dwelling or business. Digital versions are now available from the Library of Congress. The problem for researchers was that trying to manually collect usable data from these maps was tedious and time-consuming – at least until the maps were digitized. In larger cities, they were often updated regularly, said Miller, who is director of Ohio State’s Center for Urban and Regional Analysis (CURA). This research begins with the Sanborn maps, which were created to allow fire insurance companies to assess their liability in about 12,000 cities and towns in the United States during the 19 th and 20 th centuries. The study was published today (June 28, 2023) in the journal PLOS ONE. “It enables a whole new approach to urban historical research that we could never have imagined before machine learning. “The story here is we now have the ability to unlock the wealth of data that is embedded in these Sanborn fire atlases,” said Harvey Miller, co-author of the study and professor of geography at The Ohio State University. īut the digital models will be more than just a novelty – they will give researchers a resource to conduct studies that would have been nearly impossible before, such as estimating the economic loss caused by the demolition of historic neighborhoods. That’s a very real possibility now that researchers have developed a method to create 3D digital models of historic neighborhoods using machine learning and historic Sanborn Fire Insurance maps. Imagine strapping on a virtual reality headset and “walking” through a long-gone neighborhood in your city – seeing the streets and buildings as they appeared decades ago.
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