Cities across America will soon be able to easily map their pedestrian infrastructure using the power of artificial intelligence — and challenge them to have a long overdue conversation about why those networks have such an acceptable number of gaps.

This spring, an international coalition of urban studies and computer planning researchers has announced that they will debut Tile2Net, which they’re calling “the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles” — or, in layman’s terms, the first free tool that uses machine learning technology to scan satellite imagery for the presence of sidewalks and crosswalks.