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.


Capturing As-Built Changes to Make Better Digital Twins

This post originally appeared on LinkedIn.
Augmented Reality view of Apple Park

Digital Twins are easy. All you have to do is create a 3D object. Some triangles and you’re done. A BIM model is practically a Digital Twin. The problem is usually those twins are created from data that isn’t “as-built“. What you end up with is a digital object that ISN’T a twin. How can you connect your IoT and other assets to a 3D object that isn’t representative of the real world?

I talked a little bit last time on how to programmatically create digital twins from satellite and other imagery. Of course, a good constellation can make these twins very up to date and accurate but it can miss the details needed for a good twin and it sure as heck can’t get inside a building to update any changes there. What we’re looking for here is a feedback loop, from design to construction to digital twin.

There are a lot of companies that can help with this process so I won’t go into detail there, but what is needed is the acknowledgment that investment is needed to make sure those digital twins are updated, not only is the building being delivered but an accurate BIM model that can be used as a digital twin. Construction firms usually don’t get the money to update these BIM models so they are used as a reference at the beginning, but change orders rarely get pushed back to the original BIM models provided by the architects. That said there are many methods that can be used to close this loop.

Construction methods cause changes from the architectural plans

Companies such as Pixel8 that I talked about last week can use high-resolution imagery and drones to create a point cloud that can be used to verify not only changes are being made as specifications but also can notify where deviations have been made from the BIM model. This is big because humans can only see so much on a building, and with a large model, it is virtually impossible for people to detect change. But using machine learning and point clouds, change detection is actually very simple and can highlight where accepted modifications have been made to the architectural drawings or where things have gone wrong.

Focus on getting those changes into the original BIM models helps your digital twins

The key point here is using ML to discover and update digital twins at scale is critically important, but just as important is the ability to use ML to discover and update digital twins as they are built, rather than something that came from paper space.


Photo by Patrick Schneider on Unsplash
Photo by Elmarie van Rooyen on Unsplash
Photo by Scott Blake on Unsplash


Scaling Digital Twins

This article originally appeared on LinkedIn.

Let’s face it, digital twins make sense and there is no arguing their purpose. At least with the urban landscape though, it is very difficult to scale digital twins beyond a section of a city. At Cityzenith we attempted to overcome this need to have 3D buildings all over the world and used a 3rd party service that basically extruded OSM building footprints where they existed. You see this in the Microsoft Flight Simulator worlds that people have been sharing, it looks pretty good from a distance but up close it becomes clear that building footprints are a horrible way to represent a digital twin of the built environment because they are so inaccurate. Every building is not a rectangle and it becomes impossible to perform any analysis on them because they can be off upwards of 300% on their real-world structure.

Microsoft Flights Simulator created world-wide digital twins at a very rough scale.

How do you scale this problem, creating 3D buildings for EVERYWHERE? Even Google was unable to do this, they tried to get people to create accurate 3D buildings with Sketchup but that failed, and they tossed the product over to Trimble where it has gotten back to its roots in the AEC space. If Google can’t do it who can?

Vricon, who was a JV between Maxar and Saab but recently absorbed by Maxar completely, gives a picture into how this can be done. Being able to identify buildings, extract their shape, drape imagery over them, and then continue to monitor change over the years as additions, renovations, and even rooftop changes are identified. There is no other way I can see that we can have worldwide digital twins other than using satellite imagery.

Vricon is uniquely positioned to create on demand Digital Twins world-wide.

Companies such as Pixel8 also play a part in this. I’ve already talked about how this can be accomplished on my blog; I encourage you to take a quick read on it. The combination of satellite digital twins to cover the world and then using products such as Pixel8 can create that highly detailed ground truth that is needed in urban areas. In the end, you get an up to date, highly accurate 3D model that actually allows detailed analysis of impacts from new buildings or other disruptive changes in cities.

Hyper-accurate point clouds from imagery, hand-held or via drone.

But to scale out a complete digital twin of the world at scale, the only way to accomplish this is through satellite imagery. Maxar and others are already using ML to find buildings and discover if they have changed over time. Coupled with the technology that Vricon brings inside Maxar, I can see them really jump-starting a service of worldwide digital twins. Imagine being able to bring accurate building models into your analysis or products that not only are hyper-accurate compared to extruded footprints but are updated regularly based on the satellite imagery collected.

That sounds like the perfect world, Digital Twins as a Service.