A Computer Vision and ML approach to understand urban changes

By comparing 1.6 million pairs of photos taken seven years apart, researchers from MIT’s Collective Learning Group now used a new computer vision system to quantify the physical improvement or deterioration of neighborhoods in five American cities, in an attempt to identify factors that predict urban change.

A large positive Streetchange value is typically indicative of major new construction (top row). A large negative Streetchange value is typically indicative of abandoned or demolished housing (bottom row).

The project is called Streetchange. An article introducing the article can be found here.

Reference:Naik, Nikhil, Scott Duke Kominers, Ramesh Raskar, Edward L. Glaeser, and César A. Hidalgo. “Computer Vision Uncovers Predictors of Physical Urban Change.” Proceedings of the National Academy of Sciences 114, no. 29 (July 18, 2017): 7571–76. doi:10.1073/pnas.1619003114.