Ohio State University researchers have created 3D digital models of historic neighbourhoods using machine learning and historic fire insurance maps.
By turning old maps into 3D digital models of lost neighbourhoods, the researchers aim to drive forward urban research and provide users with a view of the cities of the past.
The programme would allow people to use a virtual reality headset to “walk” through long-gone neighbourhoods – seeing the streets and buildings as they appeared decades ago, before they were lost to urban development projects or natural disasters.
But 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 neighbourhoods.
“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.
“It enables a whole new approach to urban historical research that we could never have imagined before machine learning. It is a game changer.”
The researchers began by using the Sanborn maps, created in the 19th and 20th centuries to allow fire insurance companies to assess their liability in about 12,000 cities and towns in the United States.
However, the team soon realised that trying to manually collect usable data from these maps was tedious and time-consuming. In order to make the process more efficient, study co-author Yue Lin, 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.
“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.
The researchers tested their machine learning technique on two adjacent neighbourhoods located on the near east side of Columbus, Ohio, that were largely destroyed in the 1960s to make way for the construction of I-70.
One of these neighbourhoods, Hanford Village, was developed in 1946 to house returning black veterans of World War II. The other neighbourhood in the study was Driving Park, which also housed a thriving black community until I-70 split it in two.
“The GI bill gave returning veterans funds to purchase homes, but they could only be used on new builds,” said study co-author Gerika Logan. “So most of the homes were lost to the highway not long after they were built.”
Comparing data from the Sanford maps to today showed that a total of 380 buildings were demolished in the two neighbourhoods for the highway, including 286 houses, 86 garages, five apartments and three stores.
Moreover, analysis of the results showed that the machine learning model was very accurate in recreating the information contained in the maps – about 90 per cent accurate for building footprints and construction materials.
“The accuracy was impressive. We can actually get a visual sense of what these neighbourhoods looked like that wouldn’t be possible in any other way,” Miller said. “We want to get to the point in this project where we can give people virtual reality headsets and let them walk down the street as it was in 1960 or 1940 or perhaps even 1881.”
Using the machine learning techniques developed for this study, researchers could develop similar 3D models for nearly any of the 12,000 cities and towns that have Sanborn maps, Miller said.
This would allow research teams to re-create digital neighbourhoods to determine the economic impact of losing them to urban renewal or other factors. Another possibility would be to study how replacing homes with highways that absorbed the sun’s heat affected the urban heat island effect.
“This will be a tremendous resource for urban historians and a variety of other researchers,” Miller said.“Making these 3D digital models and being able to reconstruct buildings adds so much more than what you could show in a chart, graph, table or traditional map. There’s just incredible potential here.”
The study was published in the journal PLOS ONE.
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