Using old insurance fire maps, researchers have enabled a machine-learning system to recreate 3D models of neighborhoods that no longer exist. Not only could the technique lead to VR tours of the ‘hoods, but it could help study the economic impact of urbanization.

In 1866, a young surveyor named D. A. Sandborn was contracted by the Aetna insurance company to create insurance maps for several cities in Tennessee. That project, along with an atlas he made of the city of Boston, led Sandborn to found a company that still provides insurance companies with maps to this day. In total, the company has created maps that have helped insurance companies evaluate the fire risk to about 12,000 cities and towns in the United States, Mexico, and Canada.

Because the maps have been around so long, they have proven to be an invaluable historical archive; a record of the way urban areas have grown and, in some cases, dwindled in North America for over a century. The map collection was eventually digitized and the Library of Congress now has over 35,000 maps in its online collection.

Working with this digital archive, a doctoral student in geography at The Ohio State University named Yue Lin carried out a study in which he created machine learning tools allowing a computer to comb through the records to extract data such as their footprints, the materials they were built from, and what they were used for.

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