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Stanford and Google DeepMind researchers built an AI system that can analyze millions of street photos to track how cities change over time. The system found striking patterns.
The AI found striking patterns: 1,500 new solar panels across San Francisco, outdoor dining spots that multiplied after 2020, and even caught the city painting miles of freeway supports bright blue.
Unlike previous tools that could only spot specific changes they were trained to find, this system can discover unexpected shifts on its own. The researchers fed it 20 million Google Street View photos from each city, taken between 2011 and 2023.
The system, called Visual Chronicles, works in two steps. First, it looks at sequences of photos taken at the same spot over time to spot local changes. Then it finds similar changes happening across different locations to identify broader trends.
The results reveal fascinating patterns of urban evolution. In New York City, the system tracked the spread of 745 new security cameras and spotted 519 new red warning pads added to sidewalks for accessibility. In San Francisco, beyond solar panels and the blue freeway project, it found 751 new bus lanes and nearly 1,800 bike racks installed downtown.
The system proved particularly good at tracking retail changes. In New York, it documented the rise of bakeries and juice shops while noting the decline of grocery stores and bank branches. It can even spot unusual but recurring features, finding over 200 large abstract sculptures across Manhattan.
The researchers built this system to handle the massive scale of citywide photo collections. Previous AI systems could only analyze a few thousand images at once. Visual Chronicles can process tens of millions by breaking the task into smaller pieces that AI can handle efficiently.
The work opens new possibilities for understanding how cities evolve. While the current system can generate insights backed by visual evidence, the researchers note it's just a first step. Future versions could analyze different types of image collections, like historical footage, while accounting for sampling biases in the data.
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