Good morning, everybody. Welcome to session two of our summer DDSG virtual meeting
I'm Matt Corrigan, I'm one of the co-chairs for the DDSG
Hopefully you were able to participate in our session yesterday as well, but we've
Three smaller bite-sized meetings for this summer, 3 days, 2 hours each session, highlighting a lot of information
That either hasn't been presented previously or is a progression of efforts that were identified
In earlier meetings of being particular interest to the community.
Before we move forward quickly, we'll hit the disclaimer, just so everybody knows that
There's a lot of perspectives being provided here, and that, you know, those
Perspectives don't represent the perspectives of the Federal Highway Administration or the USDOT in general, and that any logos, trade names, company names, product names, et cetera
are done on the basis of information exchange only and should not be inferred as providing any preference, approval or endorsement of any said entities.
And just a reminder again, I'm the co-chair. I am still actively looking for a state agency co-chair. So those of you that might be interested in stepping up, please reach out to me. I'm going to
put a higher emphasis on getting a co-chair here before the next meeting, and also many thanks to the rest of the staff from the National Institute of Building Sciences under support contract
the Central Highway Administration to help with the delivery of the DDSG, including Roger Grant, Manor Rockley, and Mona. They've all worked very hard to put together the meetings and make sure that we're well prepared
The membership in general
is listed here. You can also find this at our DDSG Hub. We'll have a slide on that a little later on, but you can quickly just do a search in your browser at DDSG Hub, and you can get to the main page where
provide recordings and other information. There is a link to the charter that provides the list of the actual membership, as well as our goals and the reason for putting the group together, primarily directed at information exchange
Next slide. Thanks, Roger.
And as I mentioned, this collaborative effort, although being led by Federal Highway, is really to coordinate and integrate a lot of the various activities around the country in the digital delivery space
As we all know, digital delivery encompasses a wide array of topics, initiatives, and different levels of maturity as we look at not only the membership within the DDSG itself, but the broader
stakeholders and you as participants, where you're at in your digital delivery, which broads a very broad range as well, from just getting started and introduced
To the basic concepts, to those that have been actively pursuing digital delivery and things like model as the legal deliverable, and getting models out from design to construction, et cetera.
This is really encouraged, is to have that broad perspective, and these meetings are always open to everybody, public. We make all the meetings public, even the ones that we have physical meetings
Where the members are on site in one location. We do have a virtual component. We encourage your participation and your feedback and dialogue. Please provide any and all questions or comments or perspectives
Within either the chat to this meeting or some of our collaborative tools, which we'll cover in just a moment
As I mentioned, we do have the DDSG Hub. Again, a quick browser search should bring this website and the link to the top of
That search you'll have not only any registration information for meetings, for example, this is the second meeting
Again, glad you joined us and you were able to navigate the registration, three separate registrations. If for some reason you have not registered for the third meeting tomorrow, that link is still there. You can go in and register. You do have to register for each one individually
But we'll continue to use this as our public
facing entity with regards to posting the recordings and other items that we develop throughout the course of the DDSG's momentum moving forward.
And a quick reminder of today's session agenda. We're going to do some updates on some current Federal Highway sponsored research
That we are doing with EZDataMD and 2M Transformation Services, very much related to digital project delivery efforts, but also have specific aspects that I think you will find interesting, because I know my involvement has
found these topics and the work efforts to be very interesting as well. We'll have some member engagement, as well as participant engagement, and do some polling, and talk about some of the things that we've done over the last couple meetings
To evaluate, kind of, the direction we're going and planning for the upcoming year and our continued efforts within the DDSG
Okay, Matt, I'll give a little few words on these two items here on this slide. Hello, everyone. We, as Matt said, you know, we encourage participation, engagement, involvement from everyone
It's a little challenging in the online meetings to do that, but we've got a couple of tools to help with that. We use Mural, which is a whiteboarding application, and you can get to that through this link or through this QR code
I think Bradley put… we put the link in the chat already, so if you go there, what you will see is this board and can see a lot of people are logged in already
On the left here is the slides and our notes from yesterday's meeting and comments that people dropped in. Today's meeting is in the middle here, and there's a number of places where you can engage with us through the board
You can drop general notes in up here about things that you liked or things you think could be improved, ideas, unanswered questions, and the way this works in Mural, just for a quick tutorial, is if you just double-click
It will open up a sticky note, and you just start typing in that note, and it will
record that typing, and you can leave it at that. You can add your name to it if you want to. There's a little toolbar that has some things you can change the color, you can change things in the text and do other things to it. So that's the idea with Mural
We have all the day's sessions here, so if something occurs to you at some point during any of the presentations today, please jot us a note, and a few times we'll be asking for interaction that you'll be able to make on the board. So this is our mural board. We use it. This will be available
To people after the meeting, you can go in here anytime, really, and look at things. You can't break anything, so feel free to use this any way you want to in helping us to collect input. The other tool that we're using today
Is the Mentimeter, and in the presentation, there's also a QR code for Mentimeter and a URL, which I think we put into the chat so that you can get access to the Mentimeter
It has a couple of questions that are up right now, which are kind of some demographic questions that we're looking for responses to. The first one is just looks like this, and you just answer it and go on, and you'll be recording your responses for us
Later, we're going to use this in a live polling format and we'll come back and give you a new link then, and then we'll be asking some questions and showing the results in the second part of the meeting. So
Every day is every day
Those are our tools for interaction. There's also the chat. Feel free to put anything into the chat to ask questions. Well, as Matt said, we've got a few spots for open discussion, but those chat we can always
collect and respond to when we have time. So, those are our
tools for collecting input and engagement during today's session, and now I think we're ready to move on to our presentations on the research projects, Matt
Great, thanks Roger. One of the projects, this is actually a second phase of a project that we initiated a little while back, but it was primarily targeted at
When the consumer grade electronics provided by Apple started incorporating a LIDAR sensor. And so we've, I guess affectionately called that pocket lid
Since you can carry, an iPhone in your pocket, but it's also in multiple number of their products, including iPads. Usually it's at the pro grade. But we've initiated a second phase of that because the
technology itself has continued to evolve and progress, not only with the hardware, but also the applications and software, that have entered the market in order to use those tools specific to
construction type of activities, inspection, etc. And under contract, is easy data MD, both Michael Olson and Ezra Che. Mike's going to be presenting, today and giving us an update
And I know
We're a few minutes behind our regular schedule, but we also have a little bit of float in the schedule today, as opposed to yesterday with regards to how packed it was. So Mike, I think you'll be fine for your 30 minutes, but
Let you go ahead and share your screen, Mike, and take it away. Thanks
Sounds good, thank you, Matthew, and thank you everyone for the chance to come here and talk today about this exciting research project that we're working on. As you can see, there are several folks helping us out with this project and as well, we're very grateful for Matthew as his guidance is the FA
FHWA project coordinator, and providing some of the guidance for the project.
We saw this disclaimer earlier in the session, so same disclaimer applies to this, that this is not the views of FHWA. This is our views as a contractor, and so there's no endorsement by the US government or no mandate associated with this work.
So Matthew gave a great introduction to what pocket lidar is and talk a little bit about some of the maybe technical specs of what it is and what it's capable of. So starting with the iPhone 12 and evolving into the iPad and other iPad, or sorry, iPhone
Systems after that, the pro versions. Apple's incorporated a LIDAR sensor onto it. What you actually get out of the data set is a blend of photogrammetry as well as LiDAR and some AI processing associated with it, so you get this composite depth map
Compared to, say, you know, our traditional laser scanning sensor, where you're actually getting the laser scan data. It's kind of trying to use all these systems to the best of their capabilities. It's using what we would call flash LIDAR, so you can think of it as basically taking a picture with a camera, but instead of shooting out, or instead of receiving light, it's shooting out laser pulses in a grid array
So basically it's sending out 576 laser pulses in this array at a time, covering a field of view of about 60 degrees by 48 degrees. So really the key of how you get the area is you move around the scene. So as you can see in the left image, somebody's there using the pocket LIDAR, scanning a curb ramp
And then what you see in the middle is kind of an example from one of the apps of the data that's being collected in real time. And then you get kind of the video feed behind seeing the areas you haven't captured yet. And then on the right is kind of the 3D model that you can view on the system itself
One of the key things to consider with this device is it has a maximum range of 5 meters, and so it's very useful in a lot of really close range applications, but isn't something you're going to be using to cover very large areas across time.
There are a lot of different toolkits and things that Apple's developed, a lot of APIs to kind of support it and support some of the data associated with it. So there's a lot of capabilities, as we'll talk about.
So real quick on the first phase research, which the report's forthcoming, but there have been several technical notes and tech briefs that have been published as associated with this, as well as a very detailed webinar that you can access the recording of. The objective was to kind of explore this new technology and say, hey, is this
commercial grade equipment useful for inspectors going to be used in as-built applications. And so key things we focused in on this research is looked at different apps that were available to kind of identify which ones were more suitable for construction applications
You know, a lot of the apps are geared towards more indoor-type applications of, you know, real estate planning and furniture planning and all those kind of things, and so they weren't necessarily feasible for outdoor applications.
We ran some tests in the lab to kind of just get a feel for the sensor itself and how well it performed, and where some of the limitations are, and then took it out to the field, scanning things like maintenance holes and stockpiles to see how well those data sets came in
We did 4 case studies. I'll talk about briefly. Just some high level overview of what we did with those as well as then try to do some presentations and webinars to get word out about this so that there's awareness of the technology.
Some of the key takeaways from the first phase of the project was, you know, hey, this device is portable, it's easy to use in the field, it's easy to operate, somebody can pick it up and learn it pretty quickly. It's very effective for small spaces, especially ones that are difficult to measure other ways, like maintenance holes
Or getting small stockpiles in areas. You pretty much don't want to be too far away, even though the nominal operating range is about 5 meters. We found that it was most effective from about 30 centimeters to about 4 meters.
The initial phase of the project, there wasn't really good loop closure on the devices, so that means if I went and started it at point A and walked to point B and then came back to point A, I would expect that those would end up at the same spot, but there's drift in the sensor, and so we'd see that happen in quite a lot of the applications. And so
Really to keep avoiding those issues, it was limited to an area of about 10 meters by 10 square meters was kind of where it's optimal shine was without having that issue.
We are seeing accuracies in the measurements in terms of the relative distances and so on around the order of a few centimeters picking up. So again, pretty good for getting measurements that are suitable for many applications. Obviously, certain things like payment grade and other things that require higher
accuracy and precision, it's not really the tool for that, but for getting, say, a diameter of a culvert, it was very effective for getting that. The results did vary a lot by the apps and the settings, and so that's one of the things we provided a lot of guidance on is what were some of the settings that tended to perform the best
And then we, as I mentioned, went through more detailed assessments through the case studies.
So here's one example of a case study. This is in conjunction with Utah DOT and LTAP, the local technology assistance program. And the idea of this project was to go through and look at the feasibility of the technology to scan in culverts
To capture damages and debris and just basic characteristics of the culverts such as the length of the culvert or the diameter. So here you can see on the right an image of collecting the data in a culvert. This is right after some really heavy spring runoff and a lot of debris has kind of accumulated in these culverts, and there's quite a lot of damage in this particular one
We scanned a range of culverts ranging from new to ones that had been in service for a while.
This culvert you see on the right is about a 36 inch culvert. So it's a bit crammed in there, and so we kind of found just operational wise for culverts, 48 inches and larger in diameter, the technology is feasible for the smaller culverts, it gets to be pretty hard to kind of move around in there
And you also need to be doing this at low flow rates. If the flow rates are getting too high, it's pretty hard for the operator to go up and safely capture data.
We also looked at lighting sources and how that impacted it, and so depending on whether you're using a headlamp or a separate portable light made a big difference, because again, remember, this isn't just lidar technology, it's the LIDAR photogrammetry blended solution, so that makes a big difference.
Typically, we were seeing the length, getting the length of the culvert within the few centimeters, and as far as the root mean square measurement there. And then the radius was basically within a centimeter is typically what we were seeing
Slopes we would see within plus or minus 0.5%, and areas, computing kind of the surface area of the culvert, we would see that within about 8%, just to give you an idea of the metric accuracy associated with the system
As we mentioned, there's a lot of drift that you see in the system as you're measuring the culvert. So the good news is the length measurement is pretty reliable. The internal diameter is reliable. It just means it's going to shift to, say, if you're walking down a culvert, it's going to shift to the left or shift to the right
So that's kind of what you see in the image in the bottom is as we're starting at the start point, you have the point cloud that's in kind of that yellow-orange color, and you see it start to drift and get further and further as you go from one end of the culvert to the other. Now, you may notice that the reference that was scanned with the terrestrial laser scanner is blue and appears to be a little bit larger
And the main reason for that is there was a lot of damage to the culvert pipe. There was a portion of the culvert pipe that overhanged at the end, and so we weren't safely able to access that part with the
With the pocket LIDAR. But looking at the other culverts that we had in the project, the length measurements were coming out, you know, really close to what we would see.
The next case study went down to Florida DOT and looked at mechanically stabilized earth walls and used the pocket lidar, as you can see here, mounted with an extension rod or
lovingly known as a selfie stick. So basically, we could use that to access areas and collect data higher up, and made it a lot easier for the operator. So a lot of applications, that was very useful. We could look at this, in this case study, we looked at global characteristics, such as vertical and horizontal alignment. So was there tilting of the wall? Was there tilting of individual panels
We would be… we detected some effects where there's some bulging of the panels, cracks are some of the things that people are always interested in, is how well can it detect that? The answer is there's a lot of smoothing that kind of goes on in the data, so cracks can be a bit hard to pick up, especially if they're
They're less than 5 centimeters. But the you can at least map the location of those cracks. So you may not be able to get that width of the crack very reliably, but you can get that position of where that crack is occurring very well with the texture map and the photographs that get mapped to the data
And so that gives you kind of an idea of the overall severity of the cracking. As you're building MSC walls.
Next case study went to Pennsylvania dot and looked at trenches. You know, we have classic problem. We put in a bunch of utilities, dig a hole, install the utilities, backfill them, and we forget what was there. And so pocket lidar is a really great way you
go around, capture the detail of what the trench looked like while it's open, where the pipes are crossing, what size pipes those are, and everything else. And so, kind of what we found through this case study is, as long as the pipe was about 10 centimeters or so larger
We're able to reliably capture that pipe with it. I think there's also a lot of really good opportunities with augmented reality and mixed reality to have kind of visualizations, so after the fact, you know, after the trench is filled, you could bring in that point cloud, load it into a heads-up display.
and see kind of where the pipes are crossing underneath the ground and start making decisions about where maybe a borehole needs to be drilled, or where you need to avoid so that you don't run right through the pipes or other utilities that are associated with it.
So
That will reduce that kind of guesswork that sometimes has to happen in the maintenance processes.
One of the key things that that was very helpful on this again was having that extension poll made it a lot easier for the operator to get down in the trench underneath the pipes to collect data and still work from a safe location outside of the trench and not have to be directly in it
And basically, it also helped. The other key thing was the safety aspects of it. You know, if you're holding an iPhone in front of your face as you're trying to look and cover the screen, it's blocking a very large of your field of view, especially where your eyes and attention is focused
And so having that extension rod helps just kind of keep it out of your field of view and keeps you more aware of your surroundings, which is important from safety reasons on a construction site.
Jumping up to Minnesota DOT, we looked at curb ramps and we found that it was able to find pretty good reasonable data quality slopes within about 0.7%. To put it in perspective, the traditional technology, the smart levels, usually that's around 0.5% from some previous testing we've done with Oregon DOT looking at that
And that's if there aren't blunders or mix-ups or other things that happen. If you include those in it, it's typically around 1%. So, quite a lot of range in kind of the current technology we're using to deem whether a constructed curb ramp is in compliance or not. And so
It's, I think, a really feasible technology for the, kind of, at least that initial screening. Are we clearly within compliance or clearly outside of compliance, and can make a lot of those decisions a lot easier
You know, one other additional factor is we're not just getting kind of those slope measurements or the tape measurements, we're getting it all in one. So now we have a 3D model that provides that context, so we can look at debris, we can look at condition of the curb ramp
And we're also collecting GNSS or GPS data associated with this, and so now we know where that curb ramp is located and which one it was specifically taken from. That's one big issue, I think, in curb ramps. A lot of times at an intersection, there can be up to, you know, 8, 12 different curb ramps, depending on the complexity of the intersection
And a lot of times, inspectors get those mixed up, and we're not necessarily comparing the right culvert, or sorry, right curb ramps one to another. And so this really helps, kind of, that flow into an asset management framework.
I'd also argue that it's a more systematic process and repeatable to use this compared to Inspector going out there with the smart level. You never know if they're going to exactly hit the same location on it, and so it starts to become a little bit of an issue in terms of repeatability and people capturing the same measurements.
With our traditional techniques. Whereas with the LIDAR sensor, it's a much more repeatable device.
Another advantage we found through this is it's very strenuous for an operator to be down on their hands and knees. Of course, they got knee pads and other safety devices, but as you're down there trying to get those slope readings across there, it's very strenuous. You know, you're having to get down, bed down, and so on, and do that kind of repetitively throughout the day
Whereas with the pocket lidar, you're in a much more natural position walking around the area. And so there's another key advantage with it.
So, just kind of the overall takeaways of this is in that first kind of phase of the project was there were a lot of eGraph developers out there. The capabilities were changing rapidly, depending on which app you use, you could see some different differences in the results
One of the key benefits of the system is that you're getting that real-time report of what was covered, so that you have a good feel for whether you've got everything that you needed. Of course, there's still the possibility that you're going to miss some objects in the scene, but it's a lot lower compared to other technologies where you don't get that
Real-time preview. We did see some differences in the implementation between the iPhone and the iPad. The iPhone had a much better point density because it had a higher quality camera and a better IMU and GPS, as well as we speculate. But the iPad had more processing powers, and ultimately we saw
That it was actually producing models that were slightly more accurate with it, which is kind of interesting. One gave you better resolution, but the other overall, had better geometric accuracy.
Those we talked about, we needed to limit it to 10 by 10 meter areas, and we are seeing that on rougher surfaces, it was doing kind of a fair amount of smoothing. We also identified a lot of safety considerations in kind of that first phase, looking at, you know, avoiding looking at the screen
working in tight spaces and capturing data in those areas. One key thing that was also identified in this is, in a lot of cases on projects, we may be running a terrestrial scanner, there's always going to be gaps in a terrestrial scanner, and so
The iPad and the iPhone LiDAR systems are a great way to come in and capture data in localized areas and kind of fill in those gaps.
So now moving on from that first phase of the project of what was completed into kind of where we're at in terms of phase two, phase two, there have been a lot of enhancements. Phase one was done with the iPhone 13. We're now iPhone 17 and so on and 18 coming out
And so there's a lot of different
capabilities that have been improved on the system as well, in terms of hardware. There's more photogrammetry-based apps, so some apps that use the LIDAR system, some that just use the cameras, there's better SLAM-based, which is simultaneous localization and mapping
There are a lot of recent advances in that technology to basically close the loop as you repeat capturing information at different locations. And it also provides an opportunity for additional case studies and engagement with different DOTs
So
kind of the first part of phase two, we wanted to do a desktop scan to see what new apps are out there, do a detailed characterization of the new sensor, and see kind of what differences it had as they've upgraded the technology, and then do some app testing to see, kind of, where the apps have improved
We also wanted to get deeper into the API. Most of the apps out there are being developed, like I said, are for kind of commercial, maybe real estate or other types of applications, not necessarily transportation construction. And so we wanted to see in more depth what you can actually do with the API and dig deeper into it
As well as provide a field development protocol.
So, the desktop scan, you know, some of the key things that we're seeing people use this technology for is capturing buildings and architectural heritage. It's really good for getting floor plans and capturing, you know, rooms as far as some of the architecture that's going on in those smaller rooms
We're seeing applications of people using it for pavement and road surface damage characterization characterization, pothole scanning, for example. Some examples in transportation infrastructure, mostly kind of through the phase one project, but some different applications
And then others in underground construction and mining. We're seeing some applications there in capturing kind of those harder to reach areas.
Limitations that the studies identified as the limitation of the range of having only 5 meters to work with can be a big challenge. The drift that we were seeing that we've talked about. Some applications, they were saying that it's insufficient resolution or accuracy for really fine-scale features
You know, some of the detail on rock surfaces and other things was lacking from what they needed for those particular applications. And so there's a lot of factors that can affect the data quality, and so that's where we kind of look at some of those best practices.
So looking at kind of the evolution of the Lidar technology from 2022 to 2025, when we started the first phase of the project, going from the Apple I-17 Pro Max in 2025, looking at the system, you'll notice that the cameras have improved
As far as the quality, there are two different, or sorry, three different cameras associated with it, and that was one of the key upgrades on the system, is that it improved the telephoto and the ultra-wide camera quality up from 12 megapixels to 48 megapixel
associated with it. There was a slight upgrade to the, to the LiDAR sensor itself, just going basically one model up of the Sony LiDAR chip that was installed in the system as well. And so
Unfortunately, Apple's not the most forthcoming about some of the specifications with it. There are some some sites where people have kind of dug into it and dug out some of that information, but nothing officially released from Apple as far as what those specifications mean.
So kind of repeated some of the tests we did in phase one to see the capabilities of the new sensors. So we looked at scanning geometry. And so if we scan a checkerboard from different angles and different distances, how does the data quality vary
We don't have a lot of time today, so I'm going to kind of go through some of these examples pretty quick. But one of the things that we kind of found in this case study was looking that there were times that the iPhone 17 Pro performed better than the iPhone 13
There are other times where the iPhone 13 still performed better. So even though there were some improvements to the sensor, there's a lot of things going on in the app and other things that sometimes that case it worked better. But overall, we were seeing generally that the iPhone 17 was working better for capturing cylinders and capturing those kind of shapes associated with it
Devin, I see you have your hand up. Did you have a question?
I do, but I can wait till you're done
Okay, perfect.
All right, so just some quick capabilities we saw with this is you move beyond two meters or you get at a high incidence angle of about 60 degrees. We started to see some data degradation. The smaller objects, anything less than about 10 centimeters was really hard to capture reliably with the system
And that was still the case in iPhone 17, but we were seeing improvements in capturing some of those cylinder shapes. On the right, you see some examples of boxes and some of the shapes that those come in for 5 centimeters and 10 centimeters, which are kind of at the range where
It has a little bit more difficulty.
So ultimately, we didn't really see much of a performance difference between those in most of the other cases associated with it.
One thing, though, that was a big difference is when we did some of the field deployments, as I mentioned in the first project, when you start at a point and go to another point and come back to your first point, you'd see a lot of missed closure or offset between objects. And we still saw that in some of the apps that we tested. About half the apps still suffered from this problem.
But the other half of the apps, we could walk for about 180 seconds, so walk 190 seconds in one direction back in 90 seconds the other direction. And things were closing, which gives you a lot better range capabilities as far as where you can scan and still collect reliable information.
Apps, apps, apps, we could talk all day long about all the apps and things coming out, but we looked at a lot of different apps. You know, some newer ones that had come out just kind of towards the end of the last project, as well as some ones that we had tested before
We did notice, for example, one app, Sitescape, showed drastic improvement in the data quality from where it was when we first tested it a couple years ago. And so we kind of looked at these different apps to kind of get a feel for which ones they could capture
Here's just an example of scanning the most important construction object, a safety cone. And you can see that there's a lot of differences in how the data comes out in terms of the density of what you can see with the system, the shape. You can see some like Polycam and Abound start to distort the shape a little bit
You can see PIX4D is showing some noise on the surface. Sitescape showing really, really dense detail in the point cloud and a little bit of noise. And that you kind of see that difference. But one of the other things to also notice is this is kind of where some of that AI interpolation and smoothing's happening on the data
is this cone had a flat top, but you notice that a lot of the software wanted to make it so that it was kind of more a pointy top cone, and so you'll see in a lot of cases where it's trying to guess what the rest of it should look like to kind of make up with the sensor limitations
and made, you know, make some modifications to the surface.
This slide right here, just looking at all the different apps, the main takeaway from this is you can operate the systems and some of the apps using the LIDAR sensor with the photogrammetric or the photogrammetric only. And so that's what the left-hand side versus the right-hand's showing
We can also export the point cloud data directly, or a mesh, and the key takeaway from this is looking at the different apps here, you see a huge variance in terms of the quality and reconstructing the object, as well as in terms of the number of points. And so, for example, Sitescape
was giving about 500,000 points on the surface in the point cloud data compared to some other apps like RTAB was only about 1,000 points on the surface, right? So there's a huge variance in how they're doing the simplification of the data through the different apps
Available
With the API testing, we've been kind of doing a lot of exploring in the AR kit reality kit and room plan. And one of the things we're trying to experiment with is one, improving that loop closure and two, seeing what sort of infield computations and analysis can we do on site
One of the things that's great about the system is you have access to the point cloud directly there, or the mesh on-site. You can make measurements there directly, as far as distance measurements and even area measurements in some cases
We want to kind of try to see if we can take that a step further to see, can we calculate a slope or other information directly in the field? And so we're developing kind of a prototype app that can be then shared with the community. But a big chunk of this is to kind of identify where can we take this, and what are the benefits, and what are the limitations in the current API
That developers are constrained with.
Another product coming out of this research is looking at developing a field protocol app, or sorry, filled protocol document. And so the idea is to provide general guidelines of operations, so covering things of the scope and intended use of where
Where you can actually use the pocket lidar water applications are reasonable, how to determine your app settings, what other equipment and accessories you need, how to do pilot testing, which is always something important if you're wanting to use this on an application, run some pilot tests first to make sure that the configuration makes sense.
As well as to how to verify that once the app settings go up to date, and changes happen over time. And then some strategies for collecting some of the best data with the system and how to access the data across the site
Also, as well as some of the quality control checks that you can do, and of course, important safety considerations for the operators. This will be one of the products coming out of the research. This is just kind of an example of one of the flowcharts in it to kind of walk you through the steps of some of the different considerations
Again, we can spend all day on it, but just to give you an idea of some of the things that are going to be covered in this document.
The next part of this, the second phase and kind of a key focus is expanded case studies. So we've got six case studies using the newest models as well as is looking at some different applications. We're also looking at some other hardware enhancements. And so there are different
Real-time GPS adapters that you can install in conjunction with the iPhone lidar to get better positioning. So some of our case studies, we're going to be evaluating the capabilities of that. And also just kind of seeing what interest there is in different federal and state transportation agencies as far as the use of the technology
So, couple examples of some of the case studies that we're doing. We were just out last week in the Columbia River Gorge scanning some different tunnels that Oregon DOT recently reconstructed to get some kind of monitoring information as the construction was pretty recent
And so, one of the tunnels is the Moisier Twin Tunnels that you see at the top. This is an image that you see is the scan from scanning an entire section of the tunnel, and then the image on the right kind of shows you as if you're in the tunnel to kind of show some of the details and information that's being picked up with it.
One challenge with the system is some of the tunnel lengths are pretty long and difficult to scan with the pocket LIDAR, but it's a really great tool for kind of focused scans at particular sections, as well as giving information on the texture. You can see kind of which are the areas where, in this case, they're doing
Basically, a wood framing type construction versus the open rock versus concrete sections, and you can kind of see those clearly. Another tunnel that we scanned as well is the Mitchell Point Tunnel that's about 655 feet long, capturing data with that that has these openings, these windows, throughout the area
This one has a smoother kind of shot Creek type surface. And then in the fall we'll be going out with Odot inspectors on Vista Ridge tunnel. And so these 2 are pedestrian bike tunnels. The Vista Ridge is a tunnel open to cars, and they'll be doing their inspection process. And so we'll be on site with the pocket lidar to kind of see
What information is useful to the inspectors. One of the key things ODOT's interested in in this project is mapping where there are deformation and damage is. Right now, they're kind of estimating on their drawings as inspectors are in there, they can kind of get within about 4 feet
And they're hoping to get information on where cracks and other things are located more precisely than that. So that's one of the things we'll be looking at.
Another case study we're working with Connecticut DOT. And so they're looking, they're very interested in as-built documentation. I think like many entities out there, the as-built simply consists of a stamp saying was built as intended
Or maybe a couple red lines on a plan set, and that's about the level of documentation. And so, again, they want to get documentation of trenches while they're open, as well as the facility at different stages in the construction process. And so, they've identified
Several working with Jesse and others at the DOT have been very helpful in identifying different projects. So electric bus facilities, material testing labs, and other facilities that the DOT operates. And we're going to be going out there next week to capture some of this information
Compare it with some terrestrial scans in terms of the geometric quality, as well as test out how well the GPS sensor works on some of these sites to capture data.
Key thing that they're interested in is how to get the data into reporting workflows, and how to streamline that process. And so that's one of the things we'll be looking at, especially very relevant to the digital delivery group.
Next case study is with Caltrans, and so in this case, they're following Caltrans specifications, we're teaming up with a company called Conco, to capture data. And so, in this case, some of these construction sites, you can see that there's a lot of rebar
In place, and so they want to see what's the feasibility of LIDAR to kind of detect those rebar cages reliably, and get a feel for the spacing of the rebar, how much backfill volume was necessary, as well as documenting where pipes and other things are as, before they… and conduits before they fill in
With concrete in these particular areas.
And then another case study is working with Federal Highways Metro Federal Lands, and they're interested in looking at how can this technology be used for survey planning? How does it compare to handheld LiDAR and multi-station for capturing as-built of bridges and other things
along areas. And, another key thing we're hoping to test with this is there is a new attachment that's come along for the iPhone LiDAR that has a long-range LIDAR system, at least long-range in this context, not your 1 kilometer, 2 kilometer ones, but a 30-meter LiDAR attachment that you can
attached to the, to the pocket LIDAR, and seeing kind of what new applications and capabilities that uploads.
So I know I kind of threw a lot at you. There's a lot of exciting things going on with the project, but we are we do have a couple more case studies. We're looking for some people that are interested. So if you're interested in participating in that, let us know, but just want to say thank you to Federal Highways for the opportunity and Matthew and his guidance in leading us in this project
As well as to all the different DOT partners and others we've been working with, it's been a great opportunity to see this technology take off in the new directions.
Thanks, Mike. Go ahead, Devin.
Mike, this is great information and it's really exciting to see where the tools and technology
have the capability to go. My question is technology and tools aside, is the research also identifying
how these tools and technology can be applied when we look at protected practices in the future, or if not, is that something that we can request that maybe the research looks at a little bit? And I'll give you a little background. I don't know
This is
specific to California. But there are questions that are coming up that
Use of this type of technology, is that bleeding across a blurred line of engineering versus survey functionality. And
Does it, you know, highlight that laws need to be updated to focus more on specifically the practice and not how technology impacts or influences a practice
Because the way we've heard it is simply picking up
Some interpretations at this point, picking up a phone and using something like that is infringing upon protected practice, and even the layperson who uses a phone with GPS
You know, is that breaking the law? So, I don't think it is, but the questions are happening, and I'm curious if you found anything in the research
Yeah, yeah, I mean, to be honest, we haven't dug into that. You're starting me off with the easiest question of all here to start the day off. We haven't dug a ton into that because, but I mean, I think that question is broader because you look at drones, for example, that's a really classic one of I'm snapping a photo
Well, that photo's tagged with GPS. Are you doing photogrammetry, you know, and as you're creating some of these things. And I… I think that's something that's a big struggle. I mean, I definitely
Believe there needs to be
need some clear delineations here. You know, definitely there are things that are in the purview of surveying and a licensed engineer that can do surveying in some states as well, where, you know, those measurements, you know, what that end application of those measurements are, right? And so.
And how those get used, and I think that's something that definitely needs a lot of thought and discussions amongst the different communities as far as where those boundary lines should be drawn. I will say, just in my opinion, again, don't call this as FHWA
I think our surveying laws and licensure need to adapt and modify, because right now, the professional land surveying license, for example, is very boundary-centric. There was
efforts within NCES, the board that does the examinations, to have different licensor exams, for example, for mapping sciences, which I think is more relevant to this type of work, right? So, there are things happening that I think are stepping in the right direction to start recognizing that there's multiple ways to survey and do
But there's also
Factors of an inspection, right? You're in construction, you need to draw tape, or you need a laser level. Is that measuring? Well, yeah. And so it's, you know, what gets… what's get documented, what gets stored, and what are those kind of end purposes? And I think we need to have some clear guidance on those things.
So, definitely something I'll kind of keep my eye out for some of those discussions and things, but…
So, Mike
Unfortunately, those are slower than the technology evolution.
Yeah, and Mike, you know, maybe something since really what the research is looking at is the technology itself, something that might help when we have to have those conversations is maybe as part of just the effectiveness of these tools
Helping to identify, like, in what coordinate system is it being captured? Is the company already taking ownership of
Providing some kind of coordinate system. Did it require surveys to go out and provide coordinate information to make it truly accurate? So like in some of those aspects, because it might, again, it might not be the technology, but it's the practice in which
How does a surveyor enable an engineer? So if there's some as you're going through that, maybe if there's some information of on the back end of these apps
how they're kind of capturing that that could be good to know to help us, you know, with decision making and implementation, so to speak.
Yeah, yeah, we have been documenting which apps use GPS and most of the apps, if they can provide information and geo-reference coordinate is UTM is basically your only option that is exporting. There are some like Pix4D that you can actually bring in survey control
Right.
You know, you kind of get in their whole ecosystem to do it, and then at that point, you can bring in your survey control and get into the coordinate system that is for your project or or whatnot.
Well, and I guess maybe a different way of stating it, if I'm not
Yeah.
being accurate enough is, like, is it fully geodetic or has it been localized? Because that's really what we're kind of seeing as maybe the drawing line in some respects. And again, I don't know if this is just a I've heard from some other dots that they might have similar kind of challenges, but I know in California there's
The protected practices laws are very
there's definitely some clear boundary lines, but with new technology, it's getting blurred.
Yeah, definitely. Definitely. I think that's a great suggestion for us to kind of dig into that and see where some of those discussions are headed.
I,
For the most part, the GPS is not that accurate on the systems, right? So unless you're applying survey control, it's not going to be geodetic grade by any means.
I love how Devon throws the easy questions at us early
Matt, you know that I do not shy from being the vocal one.
No, and we very much appreciate that, Devin.
Yeah.
Micah, we're gonna move on for the sake of time. We may have some time on the back end to circle back
Yeah.
If you're available, but, I would encourage you, there's a few questions, some quick hits in the chat that perhaps you could respond to and there's some more that are being posted in the Mural board
And if you've got the time to go in and maybe post some answers in the mural board
Raklee can get you that link again if you need it
Appreciate it. Thanks, Mike.
Yep, thank you.
Yeah
And next up
We've got our next project on digital infrastructure enablers, data transformation and governance options that Michelle is going to be presenting. Again, this is an ongoing FHW-sponsored research
that we have. It was really trying to look at two things, essentially one was
The applicability of edge computing and also tying that into BIM data governance and digital infrastructure
Recognizing that BIM for infrastructure, BIM Better Information Management, Digital As-Builds, and onward towards a fully bi-directional and predictive digital
is going to mean that we need to connect and transmit a lot of different disparate data and data sources
And to frame this from not only edge compute framework, but also within the context of interoperability and I know I've learned a lot during this, Michelle and her staff have been amazing at working through these details
One of the things that we ran into early, and it wasn't necessarily a challenge, just an observation that pivoted some of the work that we targeted, is that it's not necessarily edge is the challenge with the hardware and the sensors and the technology itself
It's, again, liberating the data from those sensors and making sure that it's interoperable and useful throughout the asset's life cycle. But I will let Michelle go ahead and share her screen and dive right in.
Okay
Well, I told you that there could be some coaching and intervention needed. So Matthew, in your inbox right now should be the presentation. Hopefully.
And just for the folks, I can kind of tap dance around here, so hopefully you… something came through, Matthew
And so Zoom workplace is not something I'm super familiar with, but
I will say that just building on Mike's presentation, technology, you know, we had this theory when we went in on the digital enablers research that Matthew was describing around edge compute, digital transformation, and
essentially how technologies at the edge of the network could transform our transportation system. Matthew, you've got it. You see it?
No
No, it hasn't come through, so either it's making its way through or
Or it's too big.
They've been too large, and it bounced it
Yeah, the issue that I'm having is that because I've never used Zoom for workplace
It is not allowing me to share my screen unless I quit the application and come back.
Yes
Michelle, would you want to try and forward it to me? Maybe it'll come through our system faster than maps with highways.
Do you want to just go ahead and verbally give me your email?
Our grant at nibs nibbs.org.
Okay.
I'm also going to copy myself so that I can see if it actually goes
It's only 6 megs, so it should come.
Oh, it just popped into my inbox
Okay, perfect. That's great news. So I'll just keep
That's all it took.
hear a little bit more. And so, you know, we kind of jumped into this project on edge computing. We were sort of talking about initially the technologies, but in the back of our mind, we were thinking technology can't really be the problem anymore, right? Can it? I mean, when I see the last presentation where we can collect
Very detailed asset and infrastructure data from an iPhone. We should be able to do those types of things for Departments of transportation at scale. So we went also on a journey with FHWA to really understand sort of why it's so difficult to get
technology deployed at the edge to be able to collect and disseminate data on our nation's transportation infrastructure. Anne
There we go. And
What we sort of learned, and as we went through about a year and a half of work is that it wasn't ever the technology that was the problem. It is data interoperability that's the problem, but the data interoperability is also not a technology problem
A lot of it is a business model problem. And so
This is sort of a hybrid presentation. It really includes the work that we've done for FHWA and how we've taken sort of this idea of interoperable data and exposing it to the next level, frankly, so that we can start to look at things like Lidar
collected from an iPhone at scale. Because if you've got Lidar collected from an iPhone and you've got legacy sensors out in the field, and you've got legacy databases in the DOT, you have lots of investments that agencies have already made around things like asset and infrastructure performance, but also around systems operation and performance
How do you start to bring those things together? Because data, whether it's for digital deliver digital delivery, whether it's for planning and programming, whether it's for asset management, whether it's
was commonly called intelligent transportation. Those are enterprise assets and essentially data is the asset data is really the star of the show. So
Unfortunately, I'm gonna have to make Matthew click, but this is about the project, How Edge Compute Enables Real Time. But I'm going to sort of take you on a journey that started this weekend
I don't know how many of you follow the news regarding AI, but this is the slide I wanted to start with, and we don't have to, you know. I'm not asking everybody to get on an AI policy bandwagon or anything like that. But I wanted to just alert everybody that what we're talking about is happening literally right now
And the presentation I'm going to give today is very, very, very connected to what is happening with openness, not only of transportation data, but with global AI models.
And some of you may be tracking or following if you read on LinkedIn. I've certainly been posting about it like crazy. If you listen to podcasts, but
AI policy right now is a big deal. And over the weekend, NVIDIA and a coalition of dozens of companies, including Microsoft, Meta, IBM, GitHub, the Linux Foundation, Cisco, among others, published an open weights in American AI leadership
paper saying that we got to keep this stuff open. We have to have inspectable models. We have to enable competition, because that's the only way that we ensure our safety. So the point is, when you open things up, you know a lot of people think, well, if we open up data sets, or we open up AI that provides exposure right? That's scary
It's sort of when you sort of dig in and understand sort of the policy behind what happens when things are closed, right? Anything that's closed kind of generates a power system and eventual lock-in. And frankly, at a micro level
That's what we're seeing with our roadways. And so what we are worked on through the project with Matthew was sort of not only the technologies that can enable digital transformation at scale, starting at the edge, and by the edge, someone once said to me a long time ago, by the edge, do you mean edge of pavement
Kind of the edge of the network that connects devices and sensors really starts at the edge of pavement. So how do we really digitize our physical world right for everything from digital twins and asset management and digital delivery and better traveler information? How do we do it
at a with a dot-centric approach, keeping things open and honest, because the major challenge that we're going to share to you today about interoperability and data sharing isn't really about technology or tools. It's about something called vendor lock-in, which is an unattended consequence
of keeping systems closed. So we're right in the middle of it. You know, we face this identical choice, right? And what we're going to share today is our thoughts on how to translate the FHWA work into an open industry governed schema
For all kinds of transportation data. And, you know, it's a, you can go ahead and flip to the next slide. It's a big
It's a big problem to solve, and we're not going to potentially get everybody understanding or on board today, but just know I post about this stuff constantly on LinkedIn. You can find me if you don't know me, and please, you can reach out. I'm happy to ask questions or continue conversations after the meeting
particularly folks I love the ones, the comments that I get, like, you're wrong, we don't understand this. This is too big of a problem to solve DOTs can't do this. When I was prepping for this meeting, I posted something today where I really think that there's a major shift in upscaling the Department of Transportation workforce
To be able to undertake these types of initiatives. And we're, you know, with the help of FHWA and Matthew's team
We're really beginning to, you know, I think enable the digital deliveries
charter in the data collection that really can drive not only digital delivery, but the semantically rich digital twins that you all talk about in your charter
But how do we get data into that layer? And so that's really what we're… we've developed for the FHWA research. It's a canonical open schema for real-time and lifecycle data that becomes a foundation of, you know, not only digital twins and traffic signals
Any sensors that you have out on the roadside, but we're also push moving forward an open standard, which we're calling open ITS and a bigger infrastructure play called Vicasa, which is an enabling sort of asset database and GitHub so that we can actually start to do this. So
This is where we're headed. We're headed for you're extending your enterprise, so everything that you do inside your building to manage your assets and manage your data and your deterioration curves and your digital delivery and all the data you collect out in the field, we're saying that should be all one thing
And dot should be able to access that not only for transportation, transportation system management and operations, but for planning, for programming, for digital delivery, digital twins, and anything new that dots want to embark upon
Over time. So go ahead and flip to the next slide. And I actually don't know what's coming on these slides. I threw them together this morning, but
Because there's so many stakeholders in this group, and I thought, how am I going to sort of connect with each and every one of you, because that's something that we also want to do through this work, right? We want to folks that work for departments of transportation, cities, counties
Any industry owner operator to understand that, you know, we really looked at the problems behind why data aren't interoperable. And, you know, FHWA obviously
ran the research, but you know what we kind of pointed to with all the work that we did vendor interviews, looking at edge compute use cases, defining an architecture. All the things that you would do if you're like developing a product in the transportation management space, or you work for a
you know, product company. What we found through the work was that the kind of convergence was on the reason we can't put this stuff together, the reason we can't develop a national digital twin at scale is not really because we don't have the technology, it's because we don't have the data going into the thing
to make it a living, breathing organism. This idea of data interoperability, but also sort of
understanding and coalescing around the idea that transportation data isn't static, particularly the new IoT type sensors and data sets like we don't collect it at a point in time
and go back and look at it five years later, right? The asset's actually a living, breathing organism, and anything like a deteriorating paint stripe could affect an autonomous vehicle. So, when I see things like the LIDAR, the low-cost LiDAR
The pocket lidar, you know, it really leads me to believe with an interoperable data foundation, we really can start to collect data at scale. And sort of to tie everything back together, one of the things we've been thinking about as part of this project is
You know, how could we collect massive amounts of LIDAR data on somebody's pocket LIDAR and sort of gamify it, right? How do we get people out collecting so that we can create, you know, a lot… a map of our whole system? So
We've got sort of these grand ideas, right? And I've had so many people look at me over the course of this research, and frankly, the course of my career, saying.
Yeah, but we can't do that. We can't do that. Or, my favorite one is, that's already been done. Somebody else has already done it. And I think through this project and with Matthew's support, we've kind of challenged the status quo. This hasn't already been done and there's a reason it can't be
And it has a lot to do with vendors locking us into things that really should be open and exposed for departments of transportation to build on. And it's really about interoperability. So go ahead and flip to the next slide.
This really is the uncomfortable truth. And again, I love kind of pushback on these, but the State Departments of Transportation own the roads and many of you acknowledge like data I've heard people say data is the new oil, data is the new goal, data is
You know the asset that should be managed and maintained. But the way agencies contract for technology solutions, the databases behind them, and frankly, the buying of the outcomes, like saying, oh, we want to buy an AI model for congestion management
You end up locking yourself into this closed, repetitive loop of a vendor saying, yeah, we'll just give you that. We're just going to give you the insights. We'll just give you better work zone information. We'll just give you a better dashboard. We'll just give you a better map, or just deploy our sensors, deploy our sensors, we'll give you a northbound application. You're going to have all the congestion management you
information you need. I happen to be down here in Texas, working with TxDOT a lot on this particular project right now.
You know, a vendor sort of came in and said, oh, all your districts can access this, but you have to do it our way. It's within our system. It's within our northbound application. Everything's proprietary. And so when it comes down to it, and then, you know, I think that, you know, the agencies say to these vendors, well, like, is the data are? Sure, the data is ours. We'll give you an API
You can do anything you want with the data. Well, you know, what folks are starting to find out is, because we're engineers, we like to get our hands in stuff, that API gives you post-process data. You go in and you say, well, I actually want sort of the macro data of what's happening at this segment on my roadway network. And the vendor says back to you, well, you can't really have that
That's proprietary. And that's exactly what we're trying to really overcome. Because in that proprietary model and back to my first slide about sort of open AI technologies
One could argue that even if AI models were open, if the data schemas and the data themselves come from proprietary vendors, right, how can you be assured that, you know, AI is providing a model that's open and honest? I mean, it's kind of like garbage
you know, garbage out, right? So… so the uncomfortable truth is, right now, states that even think they own their own data absolutely do not. You're locked in. Neighboring agencies are locked out. Good luck sharing data, even post-process data, and we'll say, oh, we'll share data with an API that's post-process data does not allow you to do integration
In the work for federal highways, one of the things that we really doubled down on is this idea of folks telling us, oh, well, we just move everything to the cloud, and the cloud will take care of it
you know, just kind of like, you know, nobody ever thought that, like, moving your problem somewhere else made them better. I don't know if you've ever heard there's a saying called, wherever you go, there you are. But the… the, you know, it's intentional in saying that, like, just because you move all this data to a data lake
That's cloud hosted in someone else's cloud. You haven't really changed the interoperability problem. So that's where we went with this project. And so on the next slide.
Hopefully something's there that talks about what
Okay, you already know this, and I'm not going to get into, I don't want to get into details because everybody says, oh, you know, look what we need, we need to fix safety, we need to fix pedestrian, we need to, like, lower our motor, you know, annual economic cost of motor vehicle crashes and all this kind of stuff. We're not actually doing anything about data interoperability. We actually can't fix these problems right because the technologies exist to make it better
And so this is kind of the piece where, you know, again, what we found with the research is that, yeah, we've got real-time processing at the edge, of course, right? I mean
We don't need to belabor that. Privacy and security can absolutely and is absolutely being addressed. Scalability is there. Look at the things that we can do in other industries. Financial services is one that we really leaned on, but interoperability is the gap that we wanted to fill
So essentially, what we learned from the research and what we have done is we have proposed and recommended to not only USDOT and to FHWA, but to departments of transportation and cities and counties and our friends
everybody in our family and our cats and our dogs that we need to start to decouple, regardless of what
Where you sit in the agency, the producer of the data from the consumer of the data. So if you are buying sensors that are already locked into a northbound application or a cloud, and it's a whole package, prepackaged solution, that is not decoupling the producer of the data from the consumer of the data
So what we have done is we have essentially re-architected
a solution that does all the things kind of flips the script on everything that I've been talking about, gives agencies the power, gives agencies the control, allows data to be produced from all the from all the things, including those that you just saw
various Lidar, maybe low cost, low grade to very high cost, you know, high resolution lidar radars, Rsu, V to X environmental sense environment sensors, anything you would need to build, you know, kind of that, I guess
vision of a digital twin at scale
organize that data, and then push that data to the consumer. So that day, the consumer can be your ATMS, it can be your digital twin, it can be your Snowflake repository at your DOT, it can be an AI model, another agency, another researcher
But the trick is in the middle, we've got one canonical schema, one vendor neutral event stream, one that we're going to amalgamate and organize, and we're going to point and shoot that anywhere, and it's going to be governed in the open. I liken it to, and Matthew, you can go ahead and click to the next slide
Who's ever clicking. I liken it to folks have seen the movie, if you haven't seen it, you should see it now. It's on
Prime, it's called
Hail Mary
And, they're out in space, and he meets a robot and all this kind of stuff. You can say, oh, this doesn't make any sense, but trust me, it's a very good movie. And when he meets this, yeah, I'm glad Sam Welsh says everyone, great movie.
The robot in space actually has empathy and is, like, lovely, makes you cry, does all the things, but what… what they do in the movie is that
I can't think of the gentleman's name off the top of my head who's the main star. It's not Ryan Reynolds. Ryan Gosling and the alien, instead of trying to communicate with each other, he's like, well, I don't have time to learn your language.
alien, really cute alien that looks like a rock alien doesn't have time to learn Ryan Gosling's language. What they do is they create a shared language, and that's essentially what a canonical schema is that we're suggesting that dots do. And I'm going to show you today how we started creating
That shared language. This is kind of the reason why we need that shared language, because the stuff is old. If this isn't for you and you don't understand it, you probably don't need to, but I will tell you this.
I've seen this presentation given before by Deacon and another one of my colleagues, Subcontractor 360, and he said he can assure you that before the ink dries on the standards being written to the technology has already advanced way past them. So that's the idea of writing this open shared language to connect the dots among these different standards. Next slide
I think I've kind of gone through this, enough for you all to understand this is pretty technical for the folks on the call, your IT. Folks might like this, but again, this is we're building an open data fabric. It is an open standard. These are the technologies that are on the left that provide that open standard
Those are also all open source technologies. That's the other thing we're going to show you a little today here in just a couple minutes is that if you do this our way, you can use freeware to build any AI application that you want. I haven't looked at any freeware to build a digital twin yet
I don't know that that exists, but you'd be amazed what you can do with AI just reading your data model and schemas right now, which hopefully I'll be able to show you in this deck if it will
Allow me to run the video. So that's what we built through with the FHWA research. Just as a disclaimer, everybody gave their disclaimer. We did the research for FHWA, and our contract has us writing a bunch of reports, and I love writing. It's one of my favorite things to do
But you know I'm an old school planner. I came up from planning, and everybody says a report on the shelf isn't going to get you anywhere. So that's essentially why I keep talking about Vicasa openites and this GitHub repository that you can access on Vikasa.io because
Yeah, you don't need to read my report. We actually made an investment into the industry so that we can actually operationalize what we built.
How did we get this FHWA sponsor to CodeFest for us? So we started with work from the work we were doing with Matthew. We brought together 40 plus stakeholders. We looked at our conceptual architecture for how to get data out of a device and into a shared canonical schema
And we proved the concept that it works again for those who are really interested in understanding this, how it could feed your BIM or your digital twin, how you can, you know what these schemas look like, what even a code fest would look like. Happy to talk to you offline or even potentially talk about
hosting one with Europe with your department. We have a little bit of money left and a little bit of time, but even outside of time and money, I think it's something important that my team is committed to doing for the ecosystem. So, we're not all coin operated. We know that we've got
do something here to advance the state of the practice, so please reach out to me if you're interested.
Next slide. Okay, here's what I'm going to show you as just kind of a demo.
So what we did was we just threw a couple devices from I-85, a multi-state data corridor in a lab. So this is synthetic data, but we use actual devices deployed in Georgia, South Carolina, and North Carolina
To be able to collect data across
Interstate. The cool thing about this is, and I hope we can… I can show you one, on the next slide, if we're able to run
hit play, and if not, I can share it with folks individually.
So essentially what we're doing here is we're we we've
We're letting the AI model access the data in the VICASA schema and framework. And it's just going to start pulling this data and generating
I'm only pausing because I don't know if I have all the slides or if I
Yeah, I don't think I put the whole thing in
That's okay. I can send folks this after the fact, because I don't think I have the whole demo in play. This is hard to show on video. But the idea is we we gave it sort of the foundation. We gave it the open its schema and data layer with a synthetic synthetic data, and we just expose this canonical schema to
Grafana and
other open source freeware tools, and we were able to using free AI tools with our open source data model
provide things like information for GDOT's managed lanes when they should be open, when they should be closed. Data sharing across state lines, even incident detection from a camera. And I don't know if you noticed on the previous slide
We did it without any database. So I think that's really interesting. So 0 databases were used to share data across on this corridor, because essentially what we're doing is we're taking the data, we're organizing it right at the device, right there at the edge, and we're just able to analyze it on the fly. Yeah, and I'm sorry about the demo. I don't know why
Oh, there we go. Okay, it's running. Thanks. So what you're seeing here is again, the devil is truly in the details, but you can see the map. There's I-85, there's three locations. Those happen to be camera data. We just simply took our Vacasa architecture that
It is published, you know, will be published by the Fhwa in an open source model. We coded it into GitHub. We actually gave you the repo. It's on vacasa.io. And the first one was just aggregating data on the I-85 corridor
The second one that we're showing here is I-75. We just grabbed some sensors that were out in Georgia Dot, the exact radar and devices we have show how we can show when managed lanes should be opened or closed based on existing data. So that's pretty cool, right? No database
No application was bought and no vendor interfered. This was all done as freeware. These are ATSPMs if you're interested, it's more of a traffic operations thing. But again, the point here is that based on this live open source data project that FHWA got us started, that we're building out with states like yours
We're doing all this stuff for free. And so we've really taken, I mean, this shows you the power of simply taking back the night for our transportation agencies. This is your data. These are free applications you should be able to do
Anything you want, and this certainly has big implications for all the everyday count stuff coming out of USDOT. And again, if you take away anything
there were no databases used to change
No database is used to share any of this data. So this idea that you have to collect it somewhere and then push it out, that's an old school methodology. I know I'm getting close to time, so we'll just kind of run through these. Open does not mean exposed.
Open does not mean exposed. If you're a security person, trust that we have that covered. Some of the biggest supporters of this type of open architecture are the networking companies. In fact, Cisco is
Cisco and its security infrastructure and its AI models that look at security on the network are one of our biggest supporters and looking forward to them potentially even being an investor in the open source data fabric.
Data Fabric. I think we're close. Oh, good. This is one you might actually care about.
So
Hopefully you get it, right? If we do this stuff, data arrives resolved. And it makes digital twins possible at scale. And digital twins are only good if that data is real and it's open and
you know, able to be sort of real time because, you know, the transportation system's always changing. So
An architecture like this normalizes data, one canonical vendor neutral open ITS schema governed in the open. So really backlink to that first slide, you know, openness is really
kind of the underlying
principle of why this is so important
And, you know, making your data live and flow to any destination. So that's this is it, right? If you have this data fabric in place, look at the things you can feed.
You know, early in the project, we were speaking a lot about traffic operations use cases, and, you know, Matthew was, like, very
Supportive and saying, look, yeah, traffic ops is good, but you know there's a lot more to a DOT. And so we, you know, wanted to sort of go back and re-architect and say, you know, the live data is an agency asset. It shouldn't be kept to just ITS. Those data
should be able to be shared with any northbound application, including digital twins, and these are just… this is just exemplary, but that's why this type of data architecture is so important.
Hopefully this is self-explanatory, but the other thing that this type of architecture saves you is time. DOT, so we can't do that. It seems like too much work to re-architect our edge to edge to cloud device architecture. Well, now we've given you the architecture. We're giving you all the source code in GitHub
And we're telling you that we can save
you a ton of time and a ton of money. So, in, with this project, we interviewed numerous vendors, 30 plus, who, you know, some got really granular and say, look, today, if you have a million dollars for a project to implement a technology, and it's a smart grant from USDOT, and you're super excited
You spend 90% of your time trying to connect up everything, and 10% of your time trying to innovate. With this model, we're going to flip the script on that because, you know, it's going to be really fast to kind of integrate everything. It's all it's almost all going to be basically be done. You might have to build, you know, something a little bit new
And you know this is where we're kind of headed, right? This type of shared data is the fuel every agency ambition runs on or should run on.
And hopefully you can tell we're super excited to be able to share. This is pretty much all I had, Matthew. You know, there are some other slides in here of why open source, why now if folks are interested in really further exploring, if they say, why hasn't anybody done this before
Why would we want to participate in, you know, an open source community? Love to talk more about that. I will say the open source community that we founded on top of the research is governed by Apache 2.0. So folks say, how do we know that's not going to be closed
You can look at Apache, which is really sort of the legal framework for how data gets put in and our open source developers and architects take that extremely seriously. And the time is now
Let me know what, if you want to get together and help, I would love to, you know, talk more about it, help you learn, bring in your, you know, bring in your data.
Bring in your ideas.
There you go
We really believe the data beneath the road should be public. Again, I didn't put the disclaimer on my first slide, so that might be sort of just the opinion of Michelle Major, but, you know, kind of spent my life in a transportation sector, including a little bit of time in the public sector
And I think I just really believe that we're on a transformational path, and we need your support to be able to get there. And look forward to any Q&A or just picking up this conversation where it left off.
Thanks so much, Michelle
Really appreciate your efforts on this project and your insights, and always your enthusiasm
Gets me, amped up for the rest of my day
I think we're… I don't see any questions in the chat. I think there may be some on the mineral board if you got time to go in and we can get you that link. We can send that to you again if you need it.
Yeah, absolutely.
We're gonna have to move on, but there will be other opportunities, you know, in the future to update, as we work towards the conclusion here as well. Thanks, Michelle.
Matthew has my prezo. Feel free to share it with the group, Matthew. Folks are asking for a link, you can go ahead and share it. Yes, if I need to access something else to be able to answer questions, find me on LinkedIn, DM me. And folks can ask me anything they want offline. I love
Talking.
All right.
Yeah, very interesting, Michelle. It does tie in with a lot of things we're trying to work on on the
Representation of asset side and using open standards to develop models for the physical
representation of assets, you know, I think we haven't, we in this community that's working on BIM and open standards for how to model things hasn't done as much on the sensor and live data side. So it sounds like that's where a lot
Your focus is on bringing those worlds together is a great thing to do. So this could be really, this is really interesting to learn more about and hopefully we can continue to dialogue and build on
connection lots to accomplish there
Lots going on in the building world in a similar way to what's going on in the transportation sector on this. So
Thank you. So Matt, yeah, you think we're going to not discuss this further, but go to what we had planned for the rest of our session today.
So, okay
We'll go into that. So I guess our next thing on the agenda was to do some polling. Maybe we can run through this fairly quickly. I'll share my screen and put that new poll
information up there, so we can people can get to it and we can also drop it in the chat
Here we go.
We probably have to
watch our time on this. Mo and I see you're there to help with it.
So we've traditionally done some polling at all of our meetings, and we wanted to keep that thread going with capturing some input from the community that we can continue to
Track and manage over time. And so the Mentimeter on the right there will take you to a new
poll which I think the first question in it is
This one
Yeah, right? Mona, are you gonna then we're gonna get the live results to this. That's the way this is set up. And so we can
Take maybe 10 minutes on this
5 to 10, yeah, 10 minutes is that work, Mona
Yes, that sounds great. Yeah, so thank you so much, Roger, and we'll go ahead and dive into the polling section. Michelle, I really enjoyed your presentation as well. Look forward to connecting with you on LinkedIn. It was really, really great.
Alright, so I believe everybody should be able to see our screen here, and if you've attended for the group of folks that have attended in the past, these questions will look quite familiar, so we've got several that we'd like to walk through about maybe, I don't know, 8 or 9 different questions. And it looks like many of you are in, so which DDGSG focus areas fill most critical to you right now
And as you're responding here, you know, I'd like to offer some commentary when we look at how everyone responded at GIST's adjacent symposium. You know, the majority of respondents said that coordinating standards, adoption and alignment would be the top priority, followed by technology development
and deployment supporting standards implementation. So seems still quite consistent, actually, with what we saw as the two key priorities last year.
And I'll give it just another
Couple seconds here.
All right
So how would you describe your organization's position in its digital delivery journey? And for anybody who hasn't been able to get into the Menti, I've dropped a QR code and a link inside of the chat so you can access it from there.
So, you know, when we were at our session in Chicago, 41 folks responded and said that they're still early in their exploration and there are some pilots that are underway. And it looks, again, consistent here is the top item
And it was followed by some are actively doing implementation across select programs. And so as we're going through this, you know, we can run through these pretty quickly, but if anybody wants to share any feedback, feel free to turn off your mic and just jump in. I can't see hands raised necessarily while we're running the survey
But we'd love to hear any, you know, quick comments that you might have based on some of the survey questions.
All right, so let's talk about the pace of digital delivery now.
Where are you in your pace of digital delivery? Is it slowing down? Is it staying the same, slightly increased? Is it increasing significantly? This is where I'd love to hear your feedback. If things have changed for you, if you've attended past events with us and you've answered this question in the past
If something has changed, we'd love to hear why you think it's changing or what's happening within your state and organization.
Any thoughts from anybody?
Hey, this is Jennifer Lloyd. I'm one of the ones that chose to increase significantly. And I feel like it's because
Everybody finally understands that it's here. You know, we've been doing the training, we've been giving presentations, and we've been talking to everybody since 2018, but we're now finally seeing projects that are being let
as a 3D model, and we've done some pilot projects, so it feels like, although TDOT knew it was coming, and our contractors knew it was coming, now that there are some out there, there seems to be really heavy push, for everybody
that had training, maybe to have it again, or to talk to us and discuss any issues that they might have.
That's fantastic. That's great feedback. And keep building on the momentum. That's excellent. Thank you so much for sharing.
All right, which drivers have become more important for advancing digital delivery in your organization? So here we can select your top three. There's a lot to choose from, whether it's efficiency and cost savings, improving collaboration, enhancing quality, consistency, safety
Risk, is it better decision making? Regulatory compliance, or is there something else that we haven't thought of here?
Now, when we answered these questions at GIST, it was interesting. There were about four of the responses that had almost the same or similar weighting in terms of the key factors. So
would love to see how much that is changing. So efficiency and cost savings is number one so far. That was the number 2 factor. Number one was enhancing quality and consistency.
So it's changing slightly, but the top four were efficiency, improving collaboration, quality, consistency, and better decision making.
It seems almost exactly the same here as well.
Yep.
I think Mona, since you're inviting people to come off.
You know, here in California, something
Efficiency and cost savings has kind of been rising to the top yet again, because it's
Doing digital delivery, there's a change in what you're doing, and in some respects, in certain phases of delivery, it might cost more to get an overall benefit. And so I… I would say that if, if there's a way that some of the national efforts I've heard about of helping to explain
The efficiency and cost savings from a broad perspective are there would be beneficial.
Because we've had some cases where specific projects are getting scrutinized, because it might be a little bit more in a design and a delivery phase to get an overall benefit. So
Right.
It is true. It's interesting that you mentioned that, because we've really got to make sure when we're evaluating ROI and cost and efficiency, almost always there will be a ramp-up startup costs that may be significantly higher than, you know, what you may be doing today, but the long-term costs and the long-term ROI, I think, needs to be considered for sure.
Yeah.
Right, yeah, that investment… that investment early
And what is the lost benefit of waiting to invest
It is interesting. We're working on a project at Michigan DOT right now where we're doing a cost analysis in the spectrum of digital delivery and it really is looking at the cost of staying the same versus the cost of change and what the ROI is behind that. And there are models that a lot of states are looking at as well. And so that cost piece absolutely needs to be considered over the full life cycle of
the initiative as well.
Yeah, this is a good topic for the DDSG to keep on the list, I would say, for sure.
I agree, yep.
All right, so let's look at which barriers are getting easier to manage, and it is open mic, so anybody wants to weigh in, please, you know, just jump in and, you know, share as people are taking the survey results. We've got 55 people taking the survey results. I see 105 of us online, so if you haven't been able to get online, let us know in the chat and
We'll resend the links in the QR codes as well, but the barriers that are getting easier to manage, again, this is selecting up to three, anywhere from limited executive leadership support. I've heard that quite a bit here recently. Is it resistance to change from your staff? You know, so many of us are talking about change management and organizational development and
Sometimes it isn't the willingness to change. People see the possibility and the potential with digital delivery, but it could be time. I've heard that from folks that say in the field, we love it, but we don't have the time to change, and so how do we create the space so that our people can actually invest the time to learn and implement the new technology?
There's a lot that we can look at here that could be really interesting as well.
And when we compare it to what we talked about at our last meeting, I'm seeing somewhat
different results. The overwhelming winner last time was increased awareness. And so, actually, now things are leveling out. So increased awareness, limited executive leadership support, and resistance to change were the 3 most
Popular answers in our last meeting.
So that continues to be consistent
All right. Same list of question or answers. Have any become difficult? So we've talked about
Things that have become that were easier, but perhaps there are things that are becoming more difficult. Same from limited executive support, resistance to change tech investment
With the exception of a couple from last session
We had a pretty balanced representation here, actually more balanced than the last question that we just asked. And so it feels very similar. Does anybody want to lay in or share why some of these things have become more difficult for you?
I'll weigh in on
The one there in the lavender that you got your hand actually hovering over the standards and interoperability challenges. I think
You know, we've been talking about interoperability and standards for a long time, but the more we dive into the tools, the more we try to connect different tools, we're just finding more interoperability challenges
And
I think from a, at least from a Caltrans perspective, oftentimes it was a let's figure out how to fix it and fill the gap.
And that is a lot of effort and work and lost opportunity for State staff when
If we can really fix those issues of interoperability and make it across various kind of data standard types
That's gonna go a long way to getting more benefit out of this. And
I know there's some solution vendors that are here on the call, and I, you know, I'll just use this opportunity to say, you know, we really want to work with you to help figure out how we can get through those challenges and remove some of those
roadblocks that seem to be in place and
in essence should be seem like on the surface an easy fix, and it's causing a lot of delay and struggle, and people throwing their hands in the air, which becomes that resistance to change and, you know, the skills and training gaps being harder to fill
That's great. Thank you so much for weighing in.
All right, so this is an interesting question. So what do you believe is the biggest gap between our vision for digital delivery and our ability to execute, even within your organizations or across your projects?
Is it standards but limited adoption? We believe the tools exist, you know, but the workflows don't yet. Tools exist but aren't aligned with standards and workflows. The data exists, but governance does not. So there's I almost feel like we can check every one of these boxes to some degree, but if we had to pick just one
Curious to see what everybody would say here.
So the tools existing but aren't aligned with standards and workflows is pretty consistent from the things that we've heard from you in the last couple meetings as the front runner.
Very consistent.
Last time the small groups bought in, but the rest are not interested, that was much higher, actually. So it's interesting to see that kind of come down as well. So, maybe there's more interest by broader groups, and that's really moving in the right direction, I think
All right, next question.
So if DDSG could support the delivery of one new resource over the next year, which would have the greatest impact for your organization or your state, is it national implementation playbook, digital delivery maturity assessment, state DOT peer exchange networks
Continue to doing that. CBTL expansion, training and credentialing resources, model procurement, language templates.
Mona, could you help me understand what CBTL stands for? I'm blanking
Okay.
Yeah, yeah, so I'm glad you asked. That's a session that we actually are presenting on tomorrow, Erin and myself, Dr. Aaron and I, with Nibs, Central BIM Transportation Library, so it's building… yep
Thank you.
And I know that some may be unfamiliar with that effort, so, you know, please, if you're able to join tomorrow, we're going to be presenting along with WSP as well, we'll be presenting on some of their efforts as well, too.
And Caltrans, yep.
And Caltrans will be sharing their work dictionaries. Yeah
Which are a part of a CBTL.
Alright.
Answers are still coming in. I don't want to skip too quickly here.
How many more questions do we have?
I think just a couple more.
Yeah.
All right, I'll go ahead and go forward. So in one or two words, where should DDSG focus to deliver the most value in the near future?
And I believe this may be the last question
Oh, word cloud
Yes, so as we're doing the word cloud, if anybody briefly wants to share any thoughts based on what you're sharing, would love to hear that as well as we close this portion of our stakeholder engagement.
And move on to the next part of our presentation to close out today's session.
If I look at last word cloud standards was the number one. It was the largest, you know there followed by interoperability. Now we're seeing open source adoption
All the same consistent themes
But standards and interoperability continue to be the largest in all of the ones that we've done
And Roger, I believe with that, maybe give it another couple seconds. I think that'll end this portion here and I'll end the presentation here shortly.
Thank you.
Okay, thanks Mona. I will take back the screen share
And thanks everyone for sharing your thoughts with us. Those are in the mural and will be compiling that information and sharing it with me in the meeting results
So, we've got a couple slides to take a pretty brief look at here about
The DDSG operations. We finished up our first year of the DDSG in April, and we had three full meetings last year. We had over 800 people register for those meetings, two in-person and one virtual meeting, and
We formed our member group, which is our steering committee of DOT and industry representatives, and they've been participating and giving us a lot of guidance. We got the DDSG hub set up as a place for everybody to be able
To go to that you can find information about the DDSG, and that's where we post meeting announcements and everything. And then we've been working to meet one of our goals, which has been to share updates
from industry, you know, it's important for everybody to know what's going on. We're trying to leverage efforts, not duplicate efforts. There's a lot of activity out there, and finding space for this is challenging, but it's also important that we are
taking advantage of everything that's going on between the different Federal Highway programs. Interestingly, we heard about two today that are very relevant that we hadn't really talked about before that intersect with a lot of things we're doing. So, this was a great meeting of the mission
of the DDSG. We've also transitioned to diving deeper into topics and doing workshopping on critical topics, and we're going to be doing that in our next in-person meeting. That's kind of where we're dealing with that kind of thing more
Of course, we did identify some things we can continue to do more and better with, and that's doing everything we can to get engagement and participation in meetings, get input from people and use it. We're still trying to get the co-chair position filled, as Matt identified
And of course, we're always trying to get things scheduled as quickly as we can and get our calendar out there
We're catching up with that now and getting a little better, but we still have work to do to get out ahead of as many things as possible and to get things posted and really make this transition from just doing the reporting
to addressing the kind of things that that are critical to industry. So these are some of the things that came out of
Our review of the first year
The hub site, as I mentioned, and we've mentioned that before, is where we post materials and make everything available
And that site is
Out there
Yeah, that's where I can.
Sorry
Yeah, ribbon is in the way.
Anyhow, I think it's hiding behind there. Mentimeter. Oh, well, sorry, I was trying to get to the website, but over here.
No? I don't know. I must have closed it down, so I'll keep going with the PowerPoint. But the hub site has all of our materials posted where you can get access to them
And find out what's going on and identify and join our distribution list. We've got
Over like around 1,200 people signed up on this now and we trying to keep growing that list.
And what we have coming up is our
This is our summer virtual meeting. We'll be having another virtual meeting towards the end of the year, probably the week of December 13th. We're trying to finalize that now. We have finalized our fall in-person meeting, which will be September
29th through the 30th in Vienna, Virginia, at the National Highway Institute's facility, and we're about to get that
Yeah, registration posted on the website. We've communicated with our members, and we've started working on the plans for that meeting. I'll share just a bit more about it here in a minute, and then we're looking at the next in-person meeting after that to be
In the spring of next year, we're still trying to coordinate whether we do something
That coincides with everyday counts and advanced digital construction management system meeting or have
another meeting in Washington, D.C. area, or possibly in conjunction with one of our member state departments of transportation. So that planning is in the works now
For our in-person meeting in September, the 29th and 30th
As I said, registration will be opening up soon. We're working on the agenda, and this is the announcement. We're going to have a few more updates. We missed a few things at this meeting due to the few people that
had conflicts and couldn't make it from our
Industry members they'll provide updates. The next meeting we also didn't really hear from AASHTO at this meeting because we had quite a bit from them at the last one. So we'll probably engage more with the next meeting. And then we're
Putting together or trying to put together three different workshops. One of the workshops is going to be a continuation of the workshop we had
in March at the March in-person meeting around information management processes and the open standards that support those. We heard there from work at Arizona and Oklahoma
And we're going to get some further updates from them on what they're doing, and also we're working to identify a couple of other states that
Looking at how to use open standards and standardized processes around information management. The level of activities that have to do with bringing the whole process of
Planning, design, construction, and operations together, and the… how to manage the information around that
And that was interesting, the presentation from today on
the open standard for lifecycle information. So sure we'll hear more about that
We're also putting together a workshop, kind of ties in with something that came up on the polling there, and it's around
Applications that are being used on ADCMS projects and
How those are working on those projects. We've got a few states that have pilot projects going, and they're going to share about the applications that they're using, and
We're working on putting that agenda together now, so that should be an interesting opportunity to help address that
question Devin raised about, you know, interoperability, and how do we get the standards to support, how do we get applications to support the work that we're doing in a way that makes it easier to share things
So that's the second workshop we're looking at. We're looking at then a third workshop to continue the work that we're going to talk about tomorrow
on libraries and dictionaries. We're going to hear about the pilot project that Federal Highway is working on right now, or the proof of concept for a central dim transportation library, and we want to do some more workshopping on that in the fall
Dive deeper into what are the needs and the requirements? What would it take to make this useful for states, and what kind of work is being done on sharing components like dictionaries and we want to try and maybe tie in
This work on signage that's been done that we had a presentation on at one of our… at our virtual meeting in January from Washington
So there's several things there we'd like to pull together in that workshop session. And then
We're also looking at the potential of more sharing on research projects from Federal Highway and from the National Cooperative Highway Research Program.
There's some other projects out there that are relevant to digital delivery, and we want to get the word out and get sharing about what's going on with those projects as well at the September meeting. So these are the things that we are going to
be talking about there. This announcement is posted in the mural
As well as the what worked and what didn't work, items, are in the mural as well, and if you,
want to go into the mural and leave us any thoughts or comments or questions about
Year one of the DDSG, anything about
Our meetings, or anything about the plans that we have together for the fall in-person meeting, you can drop those in here in Miro. We'd appreciate anything you want to say to us there, and the mural will be open after the meeting, too.
I know we're almost out of time here, but I wanted to point that out to you
The other thing that's here in the mural is a
Our work to plan what we want to cover at the meetings and what we hear from the community needs to be covered. And we have a list here of the things that we are working on and continuing to work
Updates and sharing. We had a lot of that. We're having a lot of that here at our virtual meeting. We heard from yesterday from ACEC and from a pilot project contractor from Building Smart
We had intentions to hear from AGC and ARCPA, but that will happen at our next meeting
We were able to hear about these Federal Highway research projects. So that's a big part of what we do, sharing coordination on activities. If looking at this list, anything occurs to you that might be missing other groups, other
things that would be helpful to get the word out about and share
Please drop us a note here in the comments
And, these workshop topics, a lot of them are in the works for our next meeting, but there are others that we haven't gotten to yet that we think are important to share on. The first one on this list, information exchange standardization
Fortunately, mostly that is being covered by the pool funds currently and the work that they're doing to develop open standards for open standard applications and processes, open standard based
processes for information exchange using IFC and ISO standards. That's a lot of what the Bridge Pool Fund has worked on and the infrastructure pool fund is picking up
So we don't have to do much with that, but we are trying to explore information management
process standardization, as I mentioned, terminology, libraries
And applications and what they're doing, those are going to be a lot of
what we talked about, and a lot of these things are intertwined. Those things support model as a legal document, so that is indirectly being addressed. We haven't gotten a lot into asset management and the handover, but that's certainly on our list of things we want to cover
And we heard yesterday about the importance of workforce development and training, and it keeps coming up. And we'd like to do more with that
So this is our working list. We'll keep this going, but any comments or thoughts, love to be able to open up dialogue on this, but we don't really have time for that today. So
Getting it out there. If you have any thoughts or comments about any of this, please feel free to drop them in the chat and or to use the mural to communicate with us about
these… about what we're planning and where we're going, and we'll talk more about this at our next meeting, and we'll keep at it
It's a journey, not a destination. So we'll keep going.
So with that, I think that covers the things we wanted to share about the Ddsg updates
Matt, we do have our final Mentimeter, which you can see here and we'll drop into the chat, which is just our
poll of the meeting, or any feedback you want to give us on the meeting today, that final Mentimeter will allow you to do that. And there's the QR code and maybe Mona or Rakut you can put the link in the chat for this final poll
So with that and seeing the time, Matt, I think
We probably have another item to address today, which is wrapping up and adjourning and also
reminding people about our meeting tomorrow. I thought we had a slide for that, but I don't see where it is, but maybe you can just mention what we're doing tomorrow
Yeah, thanks Roger.
Yeah, the last kind of closing mentee, it's the same as the opening one. So aside from the two
slides on, you know, collecting, you know, what organization you represent. There's a few closing
items that we
do value the feedback on as far as the content that you saw today. So I would encourage everybody to please take the time to either go back in or continue
If you still have that poll open from the beginning and
provide your feedback in those last couple questions. This, in addition to the polling that we are tracking
progress over time and changes in response is going to be very valuable for helping us to formulate our future plans as well.
And yes, as Roger mentioned, we have our third and final session of the summer meeting tomorrow scheduled at one o'clock in the afternoon Eastern Time
And again, if you don't have that appointment on your calendar, you can go to the DDSG Hub website and register and make sure that you get that and participate in tomorrow's session as well
A lot of great information that's coming up on some efforts that were mentioned by Mona in discussions with regards to the CBTL and some efforts that Caltrans have that are related as well. Again, I want to appreciate
Your
I know that it's often a struggle to find time to carve out to participate, but your participation and your interaction and engagement is very critical and beneficial to our efforts
Thanks for all the questions and posting on the mural board. The Mural Board will be open after the fact, so if something comes to mind later on
Or something comes up that you want to add. And as Raklee reminded, we are still canvassing events, that are occurring that help us with our planning and schedule for the future, so if you have big national events or regional events
that are significant that we need to consider as far as planning around. We appreciate if you can add those items to the mural board as well.
With that, I hope to see everybody again tomorrow at 1 o'clock, and I hope you enjoy the rest of your day. Thanks so much.
Thanks, Matt. Thanks, everybody.
Alrighty, I'm going to close out the webinar now. Thank you, everyone.