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Moving Towards a Digital Twin Ecosystem

Smart Cities really start to become valuable when they integrate with Digital Twins. Smart Cities do really well with transportation networks and adjusting when things happen. Take, for example, construction on an important Interstate highway that connects the city core with the suburbs causes backups and a smart city can adjust traffic lights, rail, and other modes of transportation to help adjudicate the problems. This works really well because the transportation system talk to each other and decisions can be made to refocus commutes toward other modes of transportation or other routes. But unfortunately, Digital Twins don’t do a great job talking to Smart Cities.

Photo by Victor Garcia on Unsplash

A few months ago I talked about Digital Twins and messaging. The idea that:

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.

This was in the context of a Digital Twin talking to services that might not be hardware-based, but the idea stands up for how and why a Digital Twin should be messaging the Smart City at large. Whatever benefits a Digital Twin gains from an ecosystem that collects and analyzes data for decision-making those benefits become multiplied when those systems connect to other Digital Twins. But think outside a group of Digital Twins and the benefit of the Smart City when all these buildings are talking to each other and the city to make better decisions about energy use, transportation, and other shared infrastructure across the city or even the region (where multiple Smart Cities talk to each other).

When all these buildings talk to each other, they can help a city plan, grow and evolve into a clean city.

What we don’t have is a common data environment (CDE) that cities can use. We have seen data sharing on a small scale in developments but not on a city-wide or regional scale. To do this we need to agree on model standards that allow not only Digital Twins to talk to each other (Something open like Bentley’s iTwin.js) and share ontologies. Then we need that Smart City CDE where data is shared, stored, and analyzed at a large scale.

One great outcome of this CDE is all this data can be combined with City ordinances to give tools like Delve from Sidewalk Labs even more data to create their generative design options. Buildings are not a bubble in a city and their impacts on the city extend out beyond the boundaries of the parcel they are built on. That’s what so exciting about this opportunity, manage assets in a Digital Twin on a micro-scale, but share generalized data about those decisions to the city at large which then can share them with other Digital Twins.

Graphic showing chart of change over time

And lastly, individual Smart Cities aren’t bubbles either. They have huge impacts on the region or even the country that they are in. If we can figure out how to create a national CDE, one that covers a country as diverse as the United States, we can have something that can even benefit the world at large. Clean cities are the future and thinking about them on a small scale will only result in the gentrification of affluent areas and leave less well areas behind. I don’t want my children to grow up in a world like that and we have the processes in place to ensure that they have a better place than use to grow up in.

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COVID-19 is Showing How Smart Cities Protect Citizens

I feel like there is a before COVID and an after COVID with citizens’ feelings for Smart City technology. Now there is an election tomorrow in the United States that will probably dictate how this all moves forward and after 2016, I’ve learned to not predict anything when it comes to the current president. But, outside that huge elephant in the background, Smart City concepts have been thrust into the spotlight.

Photo by Michael Walter on Unsplash

Most cities have sent their non-essential workers home, so IoT and other feeds to their work dashboards have become critical to their success. The data collection and analysis of the pulse of a city is now so important that traditional field collection tools have become outdated.

Even how cities engage with their citizens has changed. Before COVID, here in Scottsdale, you needed to head to a library to get a library card in person. But since COVID restrictions, the city has allowed library card applications in person which is a huge change. The core structure of city digital infrastructure has to change to manage this new need. Not only engaging citizens deeper with technology but need to ensure those who don’t have access to the internet or even a computer are represented. I’ve seen much better smartphone access on websites over the summer and this will continue.

Even moving from a public space to a digital space for city council meetings has implications. The physicality of citizens before their elected leaders is a check on their power, but being a small zoom box in a monitor of zoom boxes puts citizens in a corner. Much will have to be developed to have a way for those who don’t wish to be in person be represented as well as those who choose to attend meetings in person.

COVID has also broken down barriers to sharing data. The imagined dashboard where Police, Fire, Parks & Rec, City Council, and other stakeholders have come to fruition. The single pane of glass where decision-makers can get together to run the city remotely is only going to improve now that the value has been shown.

Lastly, ignoring the possible election tomorrow, contact tracing, and other methods of monitoring citizens as they go around the city has changed mostly how people feel. Before COVID, the idea that a city could track them even anonymously scared the daylights out of people. But today we are starting to see the value in anonymous tracking so that not only we see who has been in contact with each other but how they interact in a city with social distancing restrictions.

Future planning of cities is changing and accelerated because of COVID. The outcome of this pandemic will result in cities that are more resilient, better managed, planned for social distancing, and are working toward carbon neutral environments. In the despair of this unprecedented pandemic, we see humanity coming together to create a better future for our cities and our planet.

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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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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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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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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.

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Ninety Percent of the World’s Data Has Been Generated Over the Last Two Years

Tucked into a company blog post about Smart Cities was this statement that caught my eye.

Ninety percent of the world’s data has been generated over the last two years.

Unlike the “80% of Data is Spatial” I have to admit this is totally believable and I can find the source. Most of this data is pure junk but the biggest problem with it is that it is literally unsearchable. Even in the age of Google, we can’t even begin to start aggregating this data and sorting through it.

On the BLM GPM projected that I was part of at AECOM/URS, we teamed with Voyager to attempt to find all their spatial data and share it. The good news is that I hear the BLM Navigator will be rolling out soon so at least we can know that the BLM is indexing their data and attempting to share it. But that is one organization out of billions.

This unaccounted for data is unable to be leveraged by users and becomes wasted. We all know GIS is great for making informed decisions about just about anything, yet we are most likely uninformed ourselves because the data just doesn’t happen to be at our fingertips. We’re a society that loves to create data, but not one that likes to organize data. If we’re truly going to change the world with GIS, we need to make sure we have all the information available to do so. Smart Cities, GeoDesign and all the rest are big data use cases. Let’s figure out how to start pumping them full of it.