Spatially Adjusted

Tag: sidewalk labs

  • Machine Learning Smart City Development from Sidewalk Labs

    The last time most people heard from Sidewalk Labs was when Toronto didn’t go forward with their Smart City project. There are a ton of reasons why that didn’t happy, but moonshots are what they are and even if you don’t reach the moon, outcomes can be really good for society. Of course, I know not what Sidewalk Labs has been working on but I have to assume Delve exists because of the work they are doing to build smart cities.

    Delve at its most simple description is where computers figure out the best design options for commercial or residential project development. there is much more going on here and that’s where the Machine Learning (ML) part comes in and what really catches my eye. I’ve done a tone of work with planning in my years of working with AECs and coming up with multiple design options is time-consuming and very difficult. But with Delve, this can happen quickly and repeatable in minutes.

    A quick look at Delve

    You get optimal design options based on ranked priority outcomes such as cost or view. Delve takes inputs such as zoning constraints (how high a building can be or what the setbacks are), gross floor area (commercial or residential), and then combines these with the priority outcomes. Then you get scored options that you can look further into and continue to make changes to the inputs.

    The immediacy of this is what really sets this apart. When I was at Cityzenith years ago, we attempted to try and get this worked out but the ML tools were not developed enough yet. Clearly though, with Alphabet backing, Sidewalk Labs has created an amazing tool that will probably change how cities are being developed.

    I am really excited to see how this works out. I don’t see an API yet so integration outside of Sidewalk labs does not seem to be a priority at this point but the outcome for scaleable planning like this needs to have an API. I’ll be paying attention but seeing ML being used for this type of development is logical, understandable, and workable. We should see great success. You can read more at the Sidewalk Labs blog.

  • Sidewalk Labs’ Replica Has Spun Out

    Some really interesting news in the digital twin planning space from last week:

    The newly formed company, which is headed by Nick Bowden, also announced Thursday it has raised $11 million in a Series A funding round from investors Innovation Endeavors,  Firebrand Ventures and Revolution’s Rise of the Rest Seed Fund. The capital will be used to accelerate Replica’s growth through new hires beyond its existing 13-person staff, expansion to new cities and investment in its technology.

    What makes this interesting is what Replica is:

    The Replica modeling tool uses de-identified mobile location data to give public agencies a comprehensive portrait of how, when and why people travel. Movement models are matched to a synthetic population, which has been created using samples of census demographic data to create a broad new data set that is statistically representative of the actual population.

    How, when and why people move around a city.

    As a planner, investor or developer; you can imagine how this is really interesting. As the TechCrunch article points out, there are privacy implications to this but if this model works and can help plan cities better, we’ll all be better off. Cities are growing at exponential rates and new ones are being built every day. Helping planners make better initial decisions about where and how things should go OR help them make changes as the city develops will only improve life for all.