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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.

A lidar machine creating a digital twin.
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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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.

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IoT is not About Hardware

When you think about IoT you think about little devices everywhere doing their own thing. From Nest thermostats and Ring doorbells to Honeywell environmental controls and Thales biometrics; you imagine hardware. Sure, there is the “I” part of IoT that conveys some sort of digital aspect, but we imagine the “things” part. But the simple truth of IoT is the hardware is a commodity and the true power in IoT is in the “I” part or the messaging.

IoT messages can inundate us but they are the true power of these sensors

IoT messages are usually HTTP, WebSockets, and MQTT or some derivative of them. MQTT is the one that I’m always most interested, but anything works which is what is so great about IoT as a service. MQTT is leveraged greatly by AWS IoT and Azure IoT and both services work so well at messaging that you can use either in replacement of something like RabbitMQ, which my daughter loves because of the rabbit icon. I could write a whole post on MQTT but we’ll leave that for another day.

IoT itself is built upon this messaging. That individual hardware devices have UIDs (unique identifiers) that by their very nature allow them to be unique. The packets of information that are sent back and forth between the device and the host are short messages that require no human interaction.

The best part of this is that you don’t need to hardware for IoT. Everything that you want to interact with should be an IoT message, no matter if it is an email, data query or text message. Looking at IoT as more than just hardware opens connectivity opportunities that had been much harder in the past.

Digital twins require connectivity to work. A digital twin without messaging is just a hollow shell, it might as well be a PDF or a JPG. But connecting all the infrastructure of the real world up to a digital twin replicates the real world in a virtual environment. Networks collect data and store it in databases all over the place, sometimes these are SQL-based such as Postgres or Oracle and other times they are simple as SQLite or flat file text files. But data should be treated as messages back and forth between clients.

All I see is IoT messages

When I look at the world, I see messaging opportunities, how we connect devices between each other. Seeing the world this way allows new ways to bring in data to Digital Twins, think of GIS services being IoT devices, and much easier ways to get more out of your digital investments.

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Open Environments and Digital Twins

The GIS world has no idea how hard it is to work with data in the digital twin/BIM world. Most GIS formats are open, or at works readable to import into a closed system. But in the digital twin/BIM space, there is too many close data sets that makes it so hard to work with the data. The loops one must go through to import a Revit model are legendary and mostly are how you get your data into IFC without giving up all the intelligence. At Cityzenith, we were able to work with tons of open formats, but dealing with Revit and other closed formats was very difficult to the point it required a team in India to handle the conversions.

All the above is maddening because if there is one thing a digital twin should do, is be able to talk with as many other systems as possible. IoT messages, GIS datasets, APIs galore and good old fashioned CAD systems. That’s why open source data formats are best, those that are understood and can be extended in any way someone needs. One of the biggest formats that we worked with was glTF. It is widely supported these days but it really isn’t a great format for BIM models or other digital twin layers because it is more of a visual format than a data storage model. Think of it similar to a JPEG, great for final products, but you don’t want to work with it for your production data.

IFC, which I mentioned before, is basically a open BIM standard. IFC is actually a great format for BIM, but companies such as Autodesk don’t do a great job supporting it, it becomes more of interchange file, except where governments require it’s use. I also dislike the format because it is unwieldy, but it does a great job of interoperability and is well supported by many platforms.

IFC and GLTF are great, but they harken back to older format structures. They don’t take advantage of modern cloud based systems. I’ve been looking at DTDL (Digital Twins Definition Language) from Microsoft. What I do like about DLDT is that it is based on JSON-LD so many of those IoT services you are already working with take advantage of it. Microsoft’s Digital Twin platform was slow to take off but many companies, including Bentley Systems, are leveraging it to help their customers get a cloud based open platform which is what they all want. Plus you can use services such as Azure Functions (very underrated service IMO) to work with your data once it is in there.

Azure Digital Twins
Azure Digital Twins

The magic of digital twins is when you can connect messaging (IoT) services to your digital models. That’s the holy grail, have the real world connected to the digital world. Sadly, most BIM and digital twin systems aren’t open enough and require custom conversion work or custom coding to enable even simple integration with SAP, Salesforce or MAXIMO. That’s why these newer formats, based mostly on JSON, seem to fit the bill and we will see exponential growth in their use.

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Studio is the New Pro

For quite some time, appending “Pro” after a product was a great way to highlight the new hotness of a product. Believe me, I’m guilty as charged! But the new product name is “Studio”.

I mean I could go on, but Studio seems to be the new method of naming authoring tools. I’m not here to make fun of this, just state that I love the name studio used in this sense. Having worked with Architects most of my life, Studio has a great connotation for workspace. All those apps above are used as a workspace to create something else. The concept of a studio, used this way, really helps define what a product is used for. I think the term, hackspace, has taken on a modern connotation for studio but the core concept is just so sound on this part.

A classic studio

Pro or Professional probably harkens back to the early days of software and hardware, where you’d create consumer and professional products. These days, professional is usually used to show a higher end product, vs a professional product (or at least used inconsistently). But appending “pro” to an end of a product name doesn’t signify the same purpose as studio does.

One could almost call ArcMap or QGIS a studio since that is what people are doing with it. My personal studio is my office with this computer I’m writing this blog post on. That thought makes me smile.

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Spatial Tau Newsletter

Happy Friday everyone. These weeks just fly by when you are locked in your house looking out your front window for the Instacart delivery from the grocery store. I just wanted to remind everyone that I’ve got a weekly newsletter were I do some deep dives into things that are on my mind related to GIS, BIM, Smart Cities and technology.

Just sign up below and you’ll get a newsletter in your inbox each Wednesday (or maybe Thursday LOL).

SpatialTau Newsletter

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Smart Cities and Digital Twins Will Be Built Using Smart Phone Cameras

I’ve spent years trying to build worldwide building datasets for Smart City and Digital Twin applications. I’ve tried building them using off-the-shelf data providers that give you COLLADA files, I’ve tried using APIs such as the Mapbox Unity SDK and buying buildings one by one to fill in gaps. None of these solutions have the resolution needed to perform the types of analysis needed to make better choices for cities and development potential. How to create real 3D cities with enough resolution has been out of our grasp until now.

I’ve been following Pixel8 for a while now and it is clear that crowdsourcing these models is going to be the only way forward. Over 10 years ago, Microsoft actually had this figured out with their Photosyth tool but they never were able to figure out what to do with it. Only today are we seeing startups attack this problem with a solution that has enough resolution and speed that we can start seeing cities build highly detailed 3D models that have actual value.

Example stolen from Pixel8

It is still early days with these point cloud tools, but at the speed they’ve improved over the last year, we should be seeing their use more and more. Mixing the data from smartphones, lidar and satellite imagery can make large areas of cities mapped in 3D with high accuracy. Pixel8 isn’t the only company attempting this so we should see real innovation over the next year. Stay tuned!

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And Then What?

Technology is verbose. There are no shortages of superlatives that help define the solution. It becomes almost noise when you are looking at what truly this solution solves, or even has a problem defined. Just drop something in something and then something could happen. I’ve spent a career trying to help fight through this noise and in the end one question should always come up.

And then what?

Yes you can spend millions of dollars on what seems like the perfect application, workflow or cloud-based solution, but after you get it all done, what then? We deal with this all the time on our own, part of why I’ve left digital note taking is because the “And then what?” of putting all that effort into getting text into a smartphone is either non-existent, worthless, or unneeded.

Throwing money at the problem

So much money has been wasted on “solutions” that “revolutionize” the “process”. Being able to answer the question above is how you get the best value out of the proposal. Dead projects, software not being used, databases withering on the server all happen because the users of the tools have no idea what to do with them when they are done. Time for this madness to stop.

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Uber and Google Sign 4 Year Agreement on Google Maps

This is one of those surprised/not surprised things.

Uber Technologies Inc. announced that it has entered into a Google master agreement under which the ride-hailing company will get access to Google Maps platform rides and deliveries services.

I mean today Uber uses Google Maps with their app, even on iOS. This is basically a continuation of the previous agreement with some changes that better align with how Uber does business. Rather than number of requests that Uber makes for Google Maps services, it is based on billable trips that are booked using Uber, a much more manageable deal for Uber. Last year, it came out that Uber paid Google $58 million over the past 3 years for access to Google Maps. This quote really strikes me as bold:

“We do not believe that an alternative mapping solution exists that can provide the global functionality that we require to offer our platform in all of the markets in which we operate. We do not control all mapping functions employed by our platform or Drivers using our platform, and it is possible that such mapping functions may not be reliable.”

For as much money Uber has invested in mapping, they don’t believe their technology is reliable enough to roll out to the public. That is mapping services in a nutshell, when you business is dependent on the best routing and addressing, those businesses pick Google every time. All that time and effort to build a mapping platform and they still pay another company tens of millions of dollars.

I’ve read so much about how Uber is about ready to release their own mapping platform run on OSM. But in the end the business requires the best mapping platform and routing services and clearly nobody has come close to Google in this regard. Google Maps is not only the standard but almost a requirement anymore.

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Abandoning the Digital Notetaking

A couple months I made one last attempt to enjoy taking notes digitally. I used a combination of Github, Microsoft VS Code and VIM to make my notes shareable and archivable across multiple platforms. As I expected, it failed miserably. It isn’t to say that Github doesn’t do a good job of note taking, just the workflow is wonky because that is what technologists do, make things harder for them because they can.

The thing is though, I find myself taking less notes now than before, because of the workflow. Just because I can do something doesn’t mean I should. Moving back to analog is usually a good choice, how often do I need to search my notes? Rarely, I mostly look at the dates and then go from there.

My workflow has now standardized to using the Studio Neat Totebook which I enjoy because it is thin, has the dot grid that gives note taking flexibility and has archival stickers so I can put them on the shelf like my old Field Notes. Why did I not go back to them? I find their size for normal note taking too constrictive, but that’s just me. The size of the Totebook is just right, small enough to not be to big, but big enough to not be too small.

I still use the same pens I’ve been using for years, I feel like they don’t smear and don’t cost a bundle if you lose them and have a bit of friction on the writing that makes it much easier to control. Pens are more a personal preference, it’s hard to move between them as easily as paper. Find a pen you like and stick with it.

I’m just done with Evernote, Bear, OneNote and all the rest of platforms I’ve spent years trying to adapt to.