Sidewalk Talk
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Sidewalk Talk

Announcing Delve: Discovering radically better urban designs

The traditional urban development process struggles to design for complex and competing priorities. Delve uses machine learning to reveal optimal design options — and deliver better neighborhoods.

A screenshot of the Delve product showing neighborhood design options and outcome scores.
By leveraging machine learning, Delve explores millions of design possibilities for a given project, measuring the impact of these designs to help development teams arrive at the one that’s right for them. (Sidewalk Labs)
A screenshot of the Delve product showing neighborhood designs with sunlight hours and daylight access measures.
Delve generates high-fidelity designs across a range of areas including daylight access and sunlight hours (shown above), ranking them according to how well they score on the priority metrics defined by the development team. (Sidewalk Labs)
A comparison of Quintain’s benchmark design and Delve’s high-performing
During one recent engagement, Delve identified 24 high-performing design options — including the one shown here — that exceeded the benchmark designs on priority outcomes. (Sidewalk Labs)

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Where technologists and urbanists discuss the future of cities. The official blog of Sidewalk Labs.

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