Month: October 2020

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Thoughts

The iPhone 12 Pro LiDAR Scanner is the Gateway to AR, But Not in the Way You Think

I’m sure everyone knows about it by now, the iPhone 12 Pro has a LiDAR scanner. Apple touts it to help you take better pictures in low light and do some rudimentary AR on the iPhone. But, what this scanner does today isn’t where the power will be tomorrow.

Apple cares a ton about photo quality, so a LiDAR scanner helps immensely with taking these pictures. If there is one reason today to have that scanner, it is for pictures. But the real power of the scanner is for AR. And AR isn’t ready today, no matter how many demos you see in Apple’s event. Holding up an iPhone and seeing how big a couch in your room is interesting, just as interesting as using your phone to find the nearest Starbucks.

Apple has spent a lot of time working on interior spaces in Apple Maps. They’ve also spent a ton of time working on sensors in the phone for positioning inside buildings. This is all building to an AR navigation space inside public buildings and private buildings in which owners share their 3D plans. But what if hundreds of millions of mobile devices could create these 3D worlds automatically as they go about their business helping users find that Starbucks?

The future is so bright though with this scanner. It helps Apple and developers get familiar with what LiDAR can do for AR applications. This is critically important on the hardware side because Apple Glass, no matter how little is known about it, is the future for AR. Same with Google Glass too, the eventual consumer product (ignoring the junk that the first Google Glass was) of these wearable AR devices will change the world, not so much in that you’ll see an arrow as you navigate to the Starbucks, but give you the insight into smart buildings and all the IoT devices that are around.

The inevitable outcome is in the maintenance of smart buildings

Digital Twins are valuable when they link data feeds to a 3D world that can be interrogated. But the real value comes when those 3D worlds can be leveraged using Augmented Reality to give owners, maintenance workers, planners, engineers, and tenants the information they need to service their buildings and improve the quality of building maintenance. The best built LEED building is only as good as the ongoing maintenance put on it.

The iPhone 12 Pro and the iPad Pro that Apple has released this year both have LiDAR to improve their use with photo taking and rudimentary AR, but the experience gained seeing the real-world use of consumer LiDAR in millions of devices will bring great strides to making these Apple/Google Glass devices truly usable in real-world use. I’m still waiting to get my iPhone 12, but my wife’s arrived today. I’m looking forward to seeing what the LiDAR can do.

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Thoughts

Google AI Project Recreating Historical Streetscapes in 3D

When this caught my eye I got really interested. Google AI is launching a website titled rǝ which reconstructs cities from historical maps and photos. You might have seen the underlying tool last month but this productizes it a bit. What I find compelling about this effort is the output is a 3D city that you can navigate and review by going in back in time to see what a particular area looked like in the past.

Of course, Scottsdale, my town, is not worth attempting this on, but older cities that have seen a ton of change will give some great inside into how neighborhoods have changed over the past century.

Street level view of 3D-reconstructed Chelsea, Manhattan

Just take a look at the image above, it really does give the feel of New York back in the ’40s and earlier. People remember how a neighborhood looked, but recreating it in this method gives others key insights into how development has changed how certain areas of cities look and act.

This tool is probably more aimed at history professors and community activists, but as we grow cities into smarter, cleaner places to live, understanding the past is how we can hope to create a better future. I’d love to see these tools be incorporated into smart city planning efforts. The great part of all this is it is crowdsourced, open-sourced, and worth doing. I’m starting to take a deeper dive into the GitHub repository and look how the output of this project can help plan better cities.

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Thoughts

Developing a Method to Discover Assets Inside Digital Twins

On Monday I had a bit of a tweetstorm to get some thoughts on paper.

In there I laid out what I thought addressing inside a building should look like. A couple of responses came to the “why” or “this isn’t an issue” but the important thing here is with smart buildings, they need to be able to route people not only to offices for “business” but workers to IoT devices to act upon issues that might occur (like a water valve leaking in a utility closet). Sure one, could just pull out an as-built drawing and navigate, or in the case of visiting a company, the guard at the front door, but if things such as Apple Glass and Google Glass start becoming a real thing, we’ll need a true addressing system to get people where they need to be.

Apple and Google are working this out themselves inside their ecosystems but there needs to be an open standard that people can use inside their applications to share data. I mentioned Placekey as a good starting point with their what@where.

The what is an address – poi encoding and the where is based on Uber’s H3 system. As great as all this is, it doesn’t help us figure out where the leaky valve is in the utility closet. This all is much better than other systems and is a great way to get close. I’ve not seen any way to create extensions to Placekey to do this but we’ll punt the linking problem for now.

The other problem with addressing inside a building is the digital twin might not be in any projection that our maps understand. So we’ll need to create a custom grid to figure out where the IoT and other interesting features are located. But there seems to be a standard being created that solves just this problem, UBID.

UBID builds on the open-source grid reference system and is essentially the north axis-aligned “bounding box” of the building’s footprint represented as a centroid along with four cardinal extents.

I really like this, it might even compete with Placekey, but that’s not my battle, I’m more concerned with buildings in this use case. There is so much to UBID to digest and I encourage you to read the Github to learn more.

But if we can link these grids of buildings, with a Placekey, we have a superb method of navigating to a building POI and then drilling down into navigating to that location using all the great work that companies like Pixel8 are doing. But all that navigation stuff is not my battle, just a location of an IoT sensor in a digital twin that may or may not be in a project we can use.

Working toward that link, a unique grid of a digital twin to a Placekey would solve all problems with figuring out where an asset inside a building is and what is going on at that location. The ontologies to link this could open up whole new methods of interrogation of IoT devices and so much more. e911 and similar systems could greatly benefit from this as well.

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Thoughts

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.

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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.

Credits:

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

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Thoughts

Scaling Digital Twins

View of New York

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.