Researchers at Ohio State University (OSU), the Mid-Ohio Regional Planning Commission, and Chicago-based marketing solutions provider Epsilon have converted old Sanborn Fire Insurance maps into three-dimensional digital models of historic neighbourhoods with a new machine learning (ML) technique. The researchers tested their machine learning technique on two adjacent neighbourhoods 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 the 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 researchers used 13 Sanborn maps for the two neighbourhoods produced in 1961, just before I-70 was built. Machine learning techniques were able to extract the data from the maps and create digital models.
Comparing data from the Sanborn 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. Analysis of the results showed that the machine learning model was very accurate in recreating the information contained in the maps – about 90% accurate for building footprints and construction materials. 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. This will allow researchers to re-create neighbourhoods lost to natural disasters like floods, as well as urban renewal, depopulation, and other types of change. Because the Sanborn maps include information on businesses that occupied specific buildings, researchers could re-create digital neighbourhoods to determine the economic impact of losing them to urban renewal or other factors.
More information:
https://news.osu.edu/turning-old-maps-into-3d-digital-models-of-lost-neighborhoods/