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Citywide Reality Capture for Infrastructure Design Using InfraWorks and AutoCAD Civil 3D

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설명

Ever wish you could visualize real-world data without leaving the comfort of your own office? Sound too wonderful to be true? Well, it isn't. Sending crews into the field to measure and collect information about assets is time consuming and costly. But when you have street-level spherical imagery and colorized LIDAR (light detection and ranging) data at hand, you have the freedom to quickly, accurately, and cost-effectively create photorealistic 3D models for planning, design, emergency and disaster management, regulatory compliance, and asset management. We'll explore the collection process and business implications for street-level reality capture and examine 2 alternatives, such as Google Street View and CycloMedia. We'll also discuss practical workflows for municipal governments using this data, including design and visualization, with a focus on water asset management and pavement surface markings. Finally, we'll describe how to effectively integrate these data sources with AutoCAD Civil 3D software and InfraWorks software.

주요 학습

  • Understand the business process and benefits of street-level, mobile reality capture at the scale of the municipality
  • Understand how photorealistic street-level data and colorized LIDAR can work together to provide a low-cost source of design measurement and visualization data
  • Learn the principles of working with AutoCAD Civil 3D to integrate these tools into the design process, including tips on application development
  • Learn how InfraWorks can access this data for even stronger visualization and native 3D design

발표자

  • Stephen Brockwell
    Stephen Brockwell founded Brockwell IT Consulting to provide independent business and technical leadership for the Geospatial community. His leadership at Autodesk, where he was a Senior Business Development Manager and Director of Product Management, provided the path for advanced GIS initiatives. Before joining Autodesk, Stephen was on the team for SHL VISION* Solutions, developers of the first all-relational GIS based on Oracle. Qwest Communications and First Energy, among others, still use the underlying technology he developed. Recently, Stephen has been involved in enterprise-level projects for Nevada Energy and First Energy; field mobility projects for City of Alexandria and Welland Hydro; and product development for Autodesk. With his experience in the Geospatial industry including government and private sector, Stephen has been a regular instructor at Autodesk University. He is committed to efficient, low-cost solutions to implement GIS technology for infrastructure design.
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Transcript

STEPHEN BROCKWELL: Can people hear me OK? I had a flu a couple of weeks back and my voice hasn't fully recovered. With the mic it's OK?

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: OK. All right.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: Well for a while-- I should have come and done it about two weeks ago because I had a total Leonard Cohen kind of base level voice. I could have been a radio DJ for about two weeks of my life when I had this thing in the start. But, yeah, now it's sort of normalizing.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: Probably should have that tomorrow.

Ready to go?

CREW: Yeah, you're good.

STEPHEN BROCKWELL: OK. Perfect, thanks. All right so this is all about Citywide Reality Capture using a particular approach that's used by a number of companies. It's sort of using a vehicle to drive it with spherical cameras and capture both photo realistic imagery plus LiDAR at that scale. And the integration issues around using it within Autodesk products, and also thinking about how such a workflow and how such a kind of data collection methodology fits into the bigger picture of collecting data at the full city scale, but also for specific projects.

So we're going to talk about the issues, in general, in the current collection trends, most of which many of you will know. And then we're going to talk about, specifically, a tool we've used with Pete who now works for a company called CycloMedia. They're a Dutch company that has a US operation. They have a kind of borderline revolutionary approach to it that has some fantastic tools and APIs that you can use.

I'd like to thank Shaun Kinahan who is our developer here. He's done a lot of the work on the product that we have that links the two together. And then we'll just conclude with some final thoughts on priorities and challenges that we faced putting this in place.

So that's essentially the class summary. So hopefully you'll come away understanding what, specifically, this kind of method of data collection and capture can do to reduce costs and increase quality of the data you're collecting. It can show you how the photorealistic aspect of the data plus the LiDAR together, can be really great for measuring for new projects and for engineering.

We'll learn, specifically, how to use it in AutoCAD Civil 3D. Essentially our plug-in works in all versions of AutoCAD. We have a prototype that's working in Revit. And we were unsuccessful in getting our thing to work in InfraWorks. So we'll talk a little bit about that. So that last one, which is what we sort of originally pitched for this class, we did not fulfill that and I'll explain a little bit why toward the end.

So, bios. Stephen Brockwell, that's me. I worked for a company called-- well, actually, I started-- there's some ArcGIS Esri folks in the room here-- I started working at Stats Canada in '87 doing automated districting on VAX VMS, if you can believe it, with ArcInfo 3. And I was like I'm-- just incredibly wonderful challenging project. So welcome to the Esri folks in the room who really defined this space, you have to say, the geospatial space, really.

Anyway, and then we had a company in Ottawa that was building their own, sort of, 100% Oracle-based GIS system called Vision and I joined that. That was acquired by Autodesk. I was a director of product management there for a number of years with Map Civil 3D. And then moved into a sales role.

And then started to say, hey, I would like to do this on my own, and started a small company called Brockwell IT. Where we do consulting with utilities, telecoms, municipalities, just in broad terms of how to strategically plan data collection, design engineering efforts, and also specific implementations around this kind of technology.

Pete. Please introduce yourself.

PETER SOUTHWOOD: I didn't actually do the bio, because I was silly and I didn't get in to Stephen in time. So whatever Stephen's written up there, I believe it to be the truth, and I haven't seen it so I can't really comment on it. Pete Southwood. The accent, by the way, is from England. So please don't wonder what part of Australia I'm from, because I'm English. So we'll get that out of the way to begin with.

Formerly with a company called Autodesk, 20 years, predominately is the early stage of my career there as the GIS evangelist for Autodesk. For the last 16, 17 months, I've been working for a company called CycloMedia based in Berkeley. I'll let the solution and how it interacts with the Autodesk based solutions speak for itself a little later on.

But I've had an interesting juxtaposition over the last, almost two years, where I've predominately been dealing with Autodesk customers. But actually in the last two years, it's been almost solely Esri customers. So it's nice to actually be in both camps, both huge amount of value on both sides. I'm not sure what else you put in there.

STEPHEN BROCKWELL: No, that's fine. That's perfect.

PETER SOUTHWOOD: So I think that's fine. I'm going to sit down.

STEPHEN BROCKWELL: Essentially, like me, Pete has been doing GIS things and founding companies like Convergent, being a founding partner in that, for 30 years or so. So he's been around. He was in Ask Pete. He had his own kind of blog and he was someone that people would go to inside the customer base to get solutions to problems with Autodesk GIS world for a long time. He's a great resource that's been a great colleague for many years. I'm glad to be doing this with you.

PETER SOUTHWOOD: Thank you. Me too.

STEPHEN BROCKWELL: OK. So typical options for collecting this kind of stuff-- most of us know them, I mean all of the technology out there to do it is ubiquitous-- but the key thing is asset management it's incredibly useful. Sending crews to the field back and forth with web apps and phones is fine. It can be very expensive. It can be somewhat unpredictable. Data quality can be variable. And even data collection techniques, like some people will get GPS coordinates close, some have different level of skill sets in collecting that data. So there's some issues there.

But to asset management, which is becoming more and more critical-- I'll give you a quick case in point, which is what drove this customer project that this is based on. City of Alexandria, Louisiana, has a fire rating which is at risk of changing because their collection data on hydrants is inadequate and out of date. So they don't know the full inventory of their hydrants. They don't know the fire flow at each hydrant. And they don't know the quality and the age and all of the conditions of the piping that is feeding all those hydrants.

So, as a result of that, they're at risk of dropping one or two fire insurance ratings, which could mean liability problems and also just vastly increased insurance payments, and especially serious problems if there were an incident. So that's what's driven Alexandria to embark on a really massive data collection effort. And that, fundamentally, is an asset management problem at the full city scale. So that's the kind of thing we're talking about here.

But also planning, assessment, Pete will go into some of the details here. Emergency management, as well, and public safety. Are right of ways being obeyed. Are there encumbrances to pedestrians, to traffic, these kinds of things. This is a perfect way to do a objective survey where you can intervene and look at the state of affairs at a certain point in time from a desktop or mobile environment. But not have to be in the field at that location to get the very close picture of the reality of the site you're working on.

Transportation and road condition assessment. That's another major opportunity that we're working on together. CycloMedia has actually already closed that opportunity in the City of New York for-- one of the applications there is pavement marking data quality. So the pavement mark-- well not the data quality, the quality of pavement markings. Some of which, as you'll see here, for DC area, too. You can see in temporal view the difference in quality year over year.

So the data that is available now, there's a workflow to collecting it, to putting it together, to stitching it, and then to making it available. So that tends to be a sort of cumbersome and time consuming process, especially at this full city scale. It's not bad at a project level, like let's say you're doing a substation, a water plant, or something. Some single specific project, it tends to be perfectly fine. But at the full city scale to get a decent consistent data quality across the whole thing can be an extremely expensive and time consuming operation.

And I want to emphasize the time consuming part, because a lot of municipalities they just don't have resources to get into the field. So that at a certain point in time, like within a window of a week or a month or something like that, they can get the whole city inventoried from a high quality LiDAR point of view. That's just not something they have the resources to do. They wouldn't have the people they could send into the field to get that done in a timely fashion.

The imagery processing can be time consuming and cumbersome. And you need, often, very expert resources to be able to do that. And often, those are not available or you hire an engineering contractor to do that for you.

So, at the end of the day, you've got a perfectly fine solution using this technique for small areas or pockets that you're assembling. But not necessarily one that is at the full scale.

The other aspect of this is, of course, the limits of the point cloud data that you're dealing with. And this is going to turn out to be one of the challenges that I talk about at the end of it. Because, for just the city of Alexandria, which is about 80,000 people, there are 100,000 or more, I think it's 300,000, which is going to shrink because they were changing the tile size. But 100,000 files of ReCap data, if you can imagine, right. So if you had to manually process that ReCap data, you would never finish. So there's a tool we'll talk about later that allows you to do that. But, fundamentally, processing that volume of LiDAR data is something that is really difficult, if not impossible, for a municipal scale organization to undertake.

And, again, the second part here the positioning and data consistency, different people, different crews doing it are going to get different data qualities. How you reconcile those data qualities. How do you ensure that the measurements that you're getting off of the resulting data are going to be accurate and reflect ground truth in a consistent way across the municipality.

And then, again, the stitching. One of the nice things about what we've got here, as you see, when you navigate in AutoCAD it's pretty seamless. So you're going from spherical image to spherical image. It's fairly continuous and seamless. And it has incredibly extensive metadata about it, too.

OK, so these are the familiar ways of doing it. Of course, terrestrial data capture, drones, which are actually really useful in concert with this. Because the one thing is, when you drive your collection, you're looking up. Which is incredibly useful.

I'll show a screenshot of how in Civil 3D, you can actually capture the underside of a bridge or an overpass and take measurements of that. So that street level view is incredibly useful. But it's not giving you the roof, it's not getting any assets that are on the roof, any kind of other things that are on aerial level: antennas and that kind of thing.

So it's important that you look at this, not as a single mechanism of doing it, but as a sort of really great baseline mechanism for establishing-- in a very cost effective way, I want to emphasize that-- a complete ground truth for the entire city that is affordable and usable across a wide range of applications. But it's not necessarily complete. That's not to say-- you know, gaps where there's large park land and these kinds of things. It gives you a full picture of the driveable area, not the entire city. So aerial, LiDAR, and other things, it's part of the whole picture.

Now there are other solutions for this. And we have done some work with them. So Google Street View has really cool API. It's extremely easy to implement and put into AutoCAD or anything. If you have any ObjectARX type developers or autoless developers, it's actually really easy to do. But it's limited in functionality.

The metadata that they have inside Google Street View is kind of limited to yes, there's data there and this is where it is. It's not data quality metrics that are in there. It's not kind of pickable, measurable imagery that you can use in that way. But it does give you sort of a site level view of what's going on.

It also isn't as inexpensive as you might think. If you're going to use it on a large scale for large scale projects you can end up spending money for data that you don't control the collection of. You can't control when it was collected. And you don't necessarily know the vintage and whether certain data was collected at certain points in time. But it is still a viable approach.

So CycloMedia has a really unique way to do it. I'm not going to get into all the details, but the panoramas they have are incredibly detailed. They're about the highest density of any vendor in the industry. And the way they represent the application to partners and developers-- of course, Esri is one of them. They have some really great integrations on that platform, as well as, now, AutoCAD Map. So the kinds of things you can do with this data are really pretty impressive.

So I'll let Pete take over from here. And then I'll come back to talk about some of the implementation details.

PETER SOUTHWOOD: Thanks Stephen. This isn't meant as a commercial for CycloMedia, but I think it's just important to highlight what we do as an organization. Berkeley based, fairly young in the United States, but what we do is capture professional-grade street level imagery.

And I want to just share a couple of examples, I guess from Esri. You might recognize the screen there. We got ArcGIS-- I don't recognize that screen. Is ArcGIS-- Online, thank you. Who said online? Good man. ArcGIS online.

But more importantly, just emphasizing the client itself. In this particular case, customer is Washington, DC, DDOT. At about four years ago, they decided they wanted to capture the whole of DC to manage their Department of Transportation based assets for a complete asset capture for the city.

Now in this particular case, they were so enthralled with the imagery, they've continued capturing every year for the last four years. And this year alone, they actually requested that we capture information about the street level imagery. Sorry, street level signage. That might be better. So using our imagery we actually extracted 350,000 street signs that matched with the MUTCD database, the National Signage Database. DDOT actually thought they had over a million signs.

So now we're getting into the situation where we have this real world situation, a real world source of truth, where you've got an organization that actually thought and budgeting around certain assets. But reality, that I consider really, also took on board parking meters and parking bays. So just in the way of Washington, DC, street sign imagery, but taking it further through to-- I should've practiced this. Is it a shift key or an enter key?

STEPHEN BROCKWELL: Hit enter.

PETER SOUTHWOOD: Enter. That's what I thought I did.

[INAUDIBLE]

There we go. Down arrow. Even better. But in one of Stephen's previous slides, he talked to organizations wishing to recover costs.

Now I'm going to be very, sort of sensitive talking about this. But a number of our customers actually use the imagery to reassess properties around the United States. Anyone here lives in Maricopa County? I know you two, both [INAUDIBLE]. Well, your taxes, unfortunately, are assessed based upon our street level imagery. So I'll buy you a drink later. But I'm not sure I can make up for whole amount of taxes.

But this is an interesting screenshot of an important integration. This is actually Esri's tax assessor. A solution that comes out of Esri, Canada. And our little contribution is this tiny little bit in the bottom right corner of the screen. The rest of it are coming from pictometry, other vendors of Oblique and Ortho Photography. But putting that source of truth together, this is where Brockwell IT does that extremely well. Pulling the source of truth together. That people like auditors, tax assessors, can actually go through the process of recovering tax dollars.

We do other special things, but I just wanted to highlight that for you. Not so special, unfortunately, is when we get into situations like this. Understanding that real world source of truth. Couple of years ago, I think it was two years ago, the regional municipality of Wood Buffalo in Alberta. Got to get that right. The township of Fort McMurray had a pretty awful-- Was it classified as a natural disaster?

AUDIENCE: It was, absolutely. It's one of the worst national disasters [INAUDIBLE] Alberta history.

PETER SOUTHWOOD: A number of homes were destroyed because a wildfire went through. By the way, I live just south of a place called Napa in Sonoma County. We had a, just a little fire a month or so back. Similar sort of carnage that happened. But we were asked to actually drive this by the township. Because they wanted to have a full understanding of what was actually physically left. Show us. We need to understand this. Because, guess what? All sorts of, respectfully, crazies come out of the woodwork. Well, you know, I lost my Ferrari because it was burnt and--

You know the Canadian equivalent of FEMA having to deal with all sorts of different requests to help and money. But truly, what's happened there-- And also taking the situation because there's a lot of contaminants here. They're having to remove all sorts of information-- Remove all sorts of terrible stuff that's there, contaminants from properties and all sorts of things.

So as within that of source of truth, understanding exactly what's going on within that environment. With some minor irony we, as a company, captured a place called Monroe County one week before an event called Hurricane Irma went screaming through. And if you know Monroe County-- Anyone here from Florida? Monroe County is from Key Largo down to Key West. So we were actually engaged by the county to actually-- usual things with a look in assessment and looking all sorts of different departments, public works, to understand what assets they have within the county. And about a week, week and a half later Irma went screaming through. So what we have now is a complete record of pre-Irma. And they want us to go back in and look at post-Irma because, again-- I shouldn't say that-- but the crazies are coming out--

AUDIENCE: [INAUDIBLE] it is a liability and management issue for municipalities is really serious because you're paying more than you need to pay.

PETER SOUTHWOOD: Absolutely.

AUDIENCE: It's a substantial thing.

PETER SOUTHWOOD: Absolutely. You're miked by the way. Because you're coming up with some valid observations. But please, mic.

So if I may, I'm just going to-- like I said, it's not a commercial-- but I'd like to just spend 10 minutes helping you understand how we actually go through that process of capturing street level imagery. Stephen, quite rightly, mentioned Google Street View. Fantastic solution. Please be careful around with licensing because you can use it, but it can be not as cost effective as you thought. But there are other ways, again, Stephen alluded to them with sort of hero, handheld based cameras through to drones, and such alike. We use things slightly differently. We use almost exclusively Ford Escapes. We have the spoked five camera system that sits on top of the vehicle. GPS. IMUs. So we understand if the vehicle's tipping over on its side. We run Dead Reckoning. We actually drill in to the axle of the vehicle. So if we can't get proper GPS coverage, for example, we just captured the five Boroughs of New York. Urban canyons can cause problems with GPS coverage. So we actually drill into the axle of the vehicle and can run on Dead Reckoning. So speed, distance, and things like this. So getting that sort of calibration of the imagery straight off the bat.

The driver sits in a vehicle. He follows, or she follows, Pac-Man. It's following a screen for the recording. Obviously me holding up my hand pretending to be a Pac-Man. But follows the route on the screen and just captures the imagery that the client wants. So the camera on the top-- we capture it, by the way, every 15 feet, approximately five meters. And you'll see that imagery in play in the integration with the AutoCAD based solutions, momentarily.

But imagine as a recording location, I mentioned the five cameras on top of the vehicle, each of those, as they pass over that one recording location takes top, left, right-- top, left, right, front, and back images. We take those images, and we don't stitch them, but we've got algorithms to actually pull the images together. So when you're looking at other solutions, just be aware. Anyone ever seen Google Street View with crooked people, crooked buildings, parallax? None of that occurs in our particular environment.

So we take those captured images, we stitch them-- I shouldn't use the word stitch because we don't actually physically stitch. But take the images together, put them together, and we end up with our four 360 degree, 180 degree, 106 megapixel, sub four inch positional accuracy, sub inch measurement accuracy to 19 millimeters.

So now, imagine this solution in your Autodesk based environment. We've happily been in an Esri environment benefiting from that for a while. Then have to deliver that sort of level of precision, if you wish, to that Autodesk based desktop solution. I don't like that image, Stephen. We'll have to change it.

STEPHEN BROCKWELL: My apologies.

PETER SOUTHWOOD: That's all right. But a full 360 image. The fact that I can be in a car, hold my left mouse key down, and read the asset tag on the side of a transformer. Being able to validate addresses. Being able to measure ramps for ADA compliance. Anyone here going through the ADA saga within their municipality? Slopes. Percentage average on that slope. Areas. Things like this. Again, being able to take the imagery and being able to gather all that information from the imagery.

At the same time, our camera system can also capture point clouds. There is no, "Hey, the imagery is more accurate than the point clouds." It's either captured with the imagery or not captured with the imagery. It's purely as per request by the client. Stephen's client at Alexandria, city of Alexandria in Louisiana required point clouds because they're able to use that to take in to consideration-- Rick if you're going to ask me a question, you hold your hand up.

RICK: [INAUDIBLE] If you don't mind.

[PETER LAUGHS]

PETER SOUTHWOOD: OK.

RICK: Is it actual LiDAR or is it Fodar?

PETER SOUTHWOOD: Sorry. Repeat that. I'm not sure-- The question--

RICK: Are you capturing that [INAUDIBLE] with a scanner? Or is it Fodar from the [INAUDIBLE]?

PETER SOUTHWOOD: All right, so the question are we capturing actual LiDAR or Fodar? I've not heard that. It sounds like false chicken or something like-- [INAUDIBLE].

STEPHEN BROCKWELL: It's photographed.

PETER SOUTHWOOD: No. It's proper LiDAR.

STEPHEN BROCKWELL: Oh, is it?

PETER SOUTHWOOD: Yeah. We use the Velodyne, high-end Velodyne HD32-- and a whole bunch of other number on the back of it-- LiDAR unit. It's like a very expensive baked bean can that sits on the back of the imagery. Exactly the same positional measurement accuracy. No different. In fact, what we do is we benefit from the LiDAR imagery, because we do a couple of extra things which benefits ReCap Autodesk users and in this particular case, an Esri user using ArcGIS Pro were taking that LiDAR.

We also incorporate additional attribute information and, in particular, the RGB value. And we get the RGB value from every single pixel on the 106 megapixel image. You're looking at a screen shot of the city of Redlands in California. They've just recently completed-- I need to talk to our friends at Esri, because you need the benefit from this data, too. City of Redlands in California wanted to capture their complete city. Including the local city of Redlands airports, their wastewater plants, things like this.

That was Stephen's clients city of Alexandria LiDAR imagery that they took straight into ArcGIS probe because they had the RGB value. Instant colorization. You need to see this on a proper screen, but it almost looks like a photograph. You can see leaves and bits of dirt, some snow on the left side, and slightly to the right. 'Cause I only gave a small subsection of LiDAR, so some of the data is actually missing. And using tools within the Esri platform to actually do line following, asset extraction, capture of information.

So we capture imagery plus LiDAR. Like I said, it wasn't, necessarily, supposed to be a commercial. But more importantly, and maybe in this situation is taking that same LiDAR with that same RGB value, so you get instant gratification-- that sounded wrong, but you where coming from-- where we're taking that LiDAR into an AutoCAD based solution. And seeing, hey, if there's a stop sign there, guess what? It's a stop sign. It's red. It's got the word stop on it. If it's a tree, it's green. It's maybe got sort of multicolored speckled barks. But you know what you're dealing with.

And it's where Brockwell IT bring immense value to this whole workflow, in, well, what can you do with this afterwards. Rick, not necessarily just for you, but if anyone's interested, it is the high end Velodyne unit that's used by the likes of-- Who are the autonomous vehicle companies I should notice? Who are the ones that went in San Francisco that went to--

AUDIENCE: Uber?

PETER SOUTHWOOD: Uber. Went to Phoenix immediately had an accident because somebody t-boned the vehicle. Great. But, again, you got your same positional accuracy as the imagery. No different. But we have that ability to capture LiDAR on behalf of our clients, as well. So I'll leave that there just for a few seconds. [INAUDIBLE] returned, by the way, so these things just flash through extremely quickly. And truly, the unit itself just looks like an oversized baked bean tin for goodness knows how many hundreds of thousands of dollars.

So taking all that lovely imagery-- and thanks for not hopefully too much of a commercial-- but just the fact there are other ways of extracting content from the field that doesn't necessarily need to be a handheld device or a drone. That can complement the whole workflow, don't get me wrong, I'm not asking you not to do that. But then what can you do with this in Autodesk based solutions? So, Stephen, I'm going to pass this over to you.

STEPHEN BROCKWELL: All right. Thank you, Peter. Great. We'll go into the integrations we've done with various different AutoCAD products, or Autodesk products. And then we'll talk about just some closing thoughts on experience from this project. What we learned, you know things to change next time, challenges that some of you probably had with ReCap on really large, not individual data sets, it's fine if you have enough memory for a single data set. But processing hundreds or thousands of data sets in an automated fashion is not the easiest thing to do.

So Google Street View, as I said, it's pretty easy to integrate. It's got a very nicely documented JavaScript API. I am providing the link for it there. So I suggest you have a look. It's a really easy to use JavaScript API. With AutoCAD you can create a form, pop that thing, you know the JavaScript viewer into a form, and then just use it right there. So this is actually just inside AutoCAD Map. And it's fully navigable, too. But, again, it doesn't have the same quality of measurement tools or metadata that allows you to do engineering right off of that image. But it's got some of the same features.

So for knowing sort of a current state of affairs at a site. That's very useful. So some of our telecom customers use this to sort of see, OK, what's at a particular site. You know, their data is in different states of repair. So it's not worth it to them. They don't do the same degree of physical engineering that they need to capture the level of detail and imagery that someone would in a municipality. So that's something that's really quite easy to do and we can talk to you about how that's done.

Now, within AutoCAD, we put in place a number of different things. So it's just a dockable panel that has the Cyclorama on it. You can see there, there's a measurement being done right on the surface of the pavement. It's integrated inside the Autodesk environment and there's a bunch of tools on it. So the toolbar's not showing there. There's the toolbar. So you've got the ability to sort of follow. There's a tool that's not showing up in here. Is this one on that video one. No, I don't think it is.

But anyway, so you can navigate. You can show the view cone and shrink it and stretch it inside AutoCAD. You can rotate it in AutoCAD. When you rotate it in there it stays synchronized. And then you can go into measurement mode which is where the real power of this comes in for an AutoCAD user.

So here, for example. I love this one. I'm glad Shaun did this because you see you're measuring the underside of a bridge. So let's say you have a concrete condition problem or something like that, and you want to get some estimates of the size of that problem and what you're going to have to do for it. Or even, let's say, you want to take the underside of a surface through a 3D model. You can actually do that from there. So that's a great perspective. And there you see the view cone, it's under the overpass there. And you can see the sort of view port. So you can stretch that and everything like that.

And even though there's multiple cycloramas, so you see here, it goes into-- there's a depth of field to the image, which is actually quite substantial. Those measurements can be made right into the deeper parts of it. So it's actually an incredibly powerful tool for that kind of thing. Oh, in this case here, we're creating a circular arc for a curb, measured right off of the actual imagery and getting both-- And when you're using the 3D side, the image is underpinned by the LiDAR data, if you've collected it. So you can actually collect the 3D elevations of those points as well. So you're not just collecting 2D vector data, you're actually collecting 3D elements.

So this is an example of using it in Civil 3D. So it gives you a better idea of it. This is actually in an area where there's no mapping data that's been collected, it's a shopping mall parking lot. But let's say that we wanted to collect alignments and that kind of thing here, this gives you an idea of the user experience. So you just choose which kind of measurement you want to go in.

And you can collect surfaces. You can actually measure 3D surfaces off of this, or points for blocks and attributes, text and that kind of thing. Or you can do 2D or 3D vector polylines. In this case here, we're just doing a simple straight alignment from one end to the other.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: Yeah, exactly. Yeah, yeah. And it actually will create a real Civil 3D alignment right in the drawing from that.

AUDIENCE: [INAUDIBLE] one of those pictures [INAUDIBLE] by views from the camera? [INAUDIBLE]

STEPHEN BROCKWELL: At any point, in any position, they're all unified. So it's full 360 orbit from that position.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: Yep, absolutely. He's doing that now. Yeah, yeah. No, no. So in that one photo, you're measuring into the distance, but using the other cycloramas. But you can actually switch cycloramas, move to the next one, to get more precise at the endpoint if you want to do that. And that happens all seamlessly. And it can even be done inside the viewing tool or inside the AutoCAD part, whichever way you want to do it.

PETER SOUTHWOOD: So may I add?

STEPHEN BROCKWELL: Yeah.

PETER SOUTHWOOD: You're hearing a term bandied around called cycloramas. That's just purely the 360, 180 degree image. That's what we call a GeoCyclorama. But you hear exactly right that it's recording locations thats taken every 15 feet. Thank you. 5 meters [INAUDIBLE] roughly. So you can see this is the recording locations of those blue dots. So you can actually pick a different recording location. It becomes particularly useful for clients that have assets, that have considerable distance between them.

As an example, San Jose Water [INAUDIBLE] is banning clients around the San Jose Water. It was very important for them to know the distance between hydrants. Because they're going in doing remedial work, need to understand the materials they needed to go in and put new pipes in, what information, materials they needed to repair the roads with, things like this.

But being able to go between one image and another and just get that, again, sub-inch accuracy measurement and is feeding Civil 3D, as you can see on the right of the screen. Thank you, Stephen.

STEPHEN BROCKWELL: Yeah. Thanks, Pete. So this is actual Civil 3D alignment features. We haven't really expanded beyond alignments, because alignments are so fundamental. But, in principle, that's something we're looking at doing for the future. And you can see how you're collecting data here that is very accurate.

And the other thing about the CycloMedia data that I really like is it has metadata on your measurements, too. So the image itself has a certain level data quality, but every point on it also has a level of data quality. So it will tell you whether the data quality on the point that you're measuring is sufficient to be able to use it. And that's incredibly important from a kind of engineering and design, if you're calculating areas or that kind of thing.

PETER SOUTHWOOD: Forgive me. May I add again?

STEPHEN BROCKWELL: Yeah, please.

PETER SOUTHWOOD: I promise not to be too much of a jack-in-the-box.

STEPHEN BROCKWELL: No, no, this is great. This is the whole idea.

PETER SOUTHWOOD: On the left side of the screen, just above that measuring bar that appeared, you will see a date. Stephen talks about the metadata. It's become actually surprisingly important.

STEPHEN BROCKWELL: There's another example.

PETER SOUTHWOOD: There you go. Metadata is surprisingly important to organizations that want to use this in litigation. There's some weird things that happened to trees in Washington, DC that I didn't know could happen to trees in DC. Somebody's apartment, the sun doesn't come into the windows because, guess what, there's a tree in the way. It's been known the trees disappear over weekends. This truly happens. And tiny little urban gardens appear where the tree used to be. So now you've got neighbors complaining there was an oak tree there, it's now gone. The owner of the property complaining, hey, there was never an oak tree there. Because everything's time and date stamped. Litigation. Hey. There was a tree there. You're lying. You should have got a permit. And such alike. So it's actually some strange cases where we're using that for litigation purposes. Stop signs going missing because some drunk has run them over. Question.

AUDIENCE: How long does it take to get this set up [INAUDIBLE] I mean, basically compared to [INAUDIBLE].

PETER SOUTHWOOD: So the question being, how long does it take to set this up. I'm repeating the question for the audio people. Good question. One vehicle, on average, can capture 40 linear miles per day. And it captures everything. And we have to abide by speed rules. We don't have drivers just going nuts, terrorizing down the road. I shouldn't have said that word--

STEPHEN BROCKWELL: No, but I mean, it's so important because if you think about, again, that idea of having consistent data, right. So I mean, climate conditions, temperature, all of these things. You know, that you're gathering the data at a consistent time has a huge impact on the data quality that results from that. So that's really important. Yeah.

AUDIENCE: And not to hit around too much, but I mean, the same vehicle following you for five miles or directly in front of you for five miles, do you find a need to go back out and pick up that data again?

PETER SOUTHWOOD: So the question being, and I'll paraphrase, you know strange things happen with the vehicle driving along and all sorts of things happen. Yes. Not a commercial, but we quality assure. We make sure that the best possible product gets in front of the client. But we do have crazies. I got to-- I'm sorry.

[LAUGHTER]

I'm pointing you now. That gentleman who sat in the front who is from Cleveland, but Ohio, our drivers in Columbus had to be deputized. And I got a great photograph of a member of the public who is drawing a gun on our driver. So we had to go back and re-drive that area, because the driver had to move out quickly.

But people throwing themselves on vehicles. Strange things in alleyways in DC, which is a mixed audience. I really don't want to go into now. But, yeah, we get some strange-o stuff-o that we have to go back and re-record.

STEPHEN BROCKWELL: Just to reiterate what Pete had mentioned about dates, too. This video clip has been played. But in this image here, you can just very briefly see 'cause it's too bright, but you see the date at the top of that slider. So you can slide that down, that's what we're doing.

And in this case, this is really useful because this is one of the problems, it's pavement markings, right. There were no pavement markings here the year before and they put them in. So they can say, yeah, that problem has been addressed. So that ability to do temporal examination of your data. And then measure differences between last year and this year for degradation and other things. It's a lot of different applications of that temporal aspect.

So we've also got a prototype of it in Revit. And we're working on how does it all fit in, how does it work. But the idea would be, for example, if you've got an existing building, what are the surface areas. What are the measurements, if you're using it as a starting point.

Or let's say you've got some baseline construction foundation, that kind of thing, you want to measure off of that. This is the kind of tool that would allow you to do that. But this is a bit of a work in progress, to be honest. But the idea is the same.

And I'll talk a little bit more about the significance of doing it and what some of the issues were that we were facing. But this is absolutely one of our top priorities now that we have all of the AutoCAD kind of things. And, you know, it wouldn't work for specific plant features. But this is something we've been talking about here, like water plants, airports, other facilities that are reasonably accessible.

Even, like in Los Angeles, all the canals and waterways, drainage, doesn't have to necessarily be a public roadway. Anywhere where you can use it to get a decent legitimate scan is something that is possible to do. So there's a lot of possibilities here from that perspective.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: Well you know, the data's real-- this data is not in Revit, per se. It's in a container that's using the cloud service to visualize it, right? So that's true in AutoCAD, too. So, in fact actually, one of the beautiful things that we've done about it in AutoCAD, which Shaun's going to-- he's just worked on the ArcGIS white paper that we're going to be showing tomorrow about some other integration issues with ArcGIS online and how to do that. But Shaun will be writing up a bit of a white paper, some of the technical issues in doing this.

But the nice thing about AutoCAD, we leave nothing around. So that widget that moves around and, you know, the WMS feature service where you're showing all the recording locations, we use transitory graphics to do that. So we're not leaving any features behind in your drawings or anything like that. We leave them completely clean at the end of it. And we're going to try to do the same thing in Revit. The only thing you're left with, as an artifact, is a 3D surface of that wall and a proper Revit object from it. Yeah.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: Here we're reading measurement content. And we're going to be creating, in the future, this is a prototype, but an actual wall, or depending upon the level of the architecture that you're dealing with, the discipline and so on. There's a lot of user interface issues to that because the Revit model itself for data creation is so much more complex, than the one for just vanilla AutoCAD, really. Obviously.

OK. So concluding remarks. Some of these are really important, I think. Just the, sort of, this project, what we learn from it collectively. 3D data collection. I mean, the thing is this customer of ours, we've been working with them for at least, I think, eight years now. 2D cost estimates for data conversion, the old fashioned way, they literally come in in the half a million, million dollar range for one discipline, like gas, water for the whole city. To vectorize data from all sorts of paper records and all of this stuff.

And what's the real benefit of that after having done it? Can they engineer off that? Well, yes, but there's still uncertainty in data quality issues in that. 3D data capture is so cost effective. Like a 10:1 difference and I'm not joking about that, cost wise to collect data at the entire city scale. Of course, limitations, it's visible, it's street level, and that kind of thing, sure. But 99% of infrastructure in a municipality is at street level anyway.

So it is a process, not an event. And what I mean by that is a lot of data collection that takes place right now is for a project. The LiDAR files that get captured, the imagery that gets captured if you're using something like ReCap Photo, which is the sort of photogrammetric way of getting LiDAR, or you know, point clouds anyway. That data tends to be sort of used, and then is it discarded? Is it archived? How is it accessed in the future? Is the temporal thing there? What's the metadata?

So, process-wise, this is a very helpful way to look at it because it changes it from just an event-based, sort of project-level thing, to a sort of system-wide, kind of change in philosophy about how you use 3D reality capture data at a scale of the city. And it is possible to do, and it's possible to do very cost effectively. For the price of some drones, you can get this data collected in a small city scale.

Asset management of applications are obviously incredible. And it's repeatable. And the other thing about it, it's metadata. Like the level of metadata that you have is really incredibly useful. Yeah.

AUDIENCE: Is any of the data becoming public source? Like a lot of public municipal GIS data is public source. So is any of this data that [INAUDIBLE]

STEPHEN BROCKWELL: Right.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: That's an excellent question. So the question is, if the customer collects this data, is this data being then used on public websites and that kind of thing. And Pete will correct me if I'm wrong, but I don't think there will be anything to prohibit a municipality from using it in that way. So that's a very good question. And I would say that you could do that. Is that true?

PETER SOUTHWOOD: It is true to a certain extent. So, give you an example, assessment websites, Maricopa County in Arizona, we mentioned earlier, and Franklin County in Ohio, their public accessor website actually has a cut down version, if you wish, of the viewer. But allows a member of the public to see exactly what the assessor is seeing, and why that property was accessed with that particular capture of that time. So the answer is yes. Are we giving full functionality-- Does a member of the public need to understand how to measure between two points? No. But, yes and no to that answer.

AUDIENCE: [INAUDIBLE]

PETER SOUTHWOOD: Yeah, likely. Yep. So a question.

AUDIENCE: Yeah, I have question about the photos that you're using. What's the overlap of the stationary cameras, the five cameras themselves, how much are they overlapping? I guess your intervals [INAUDIBLE] anyway, or were they driven by some sort of range of the photos, how far they can actually perceive depth?

PETER SOUTHWOOD: So to understand the question fully, just to repeat, and if I get it wrong, please stop me. The overlap of the camera. I don't know. I could find out for you. We talked about-- we have algorithms that take-- that there's quite a substantial bit of overlap between the cameras and the images through our algorithms.

I hate using the word stitching, because it isn't actually physically stitching it. There is no parallax. There is no crooked lines or crooked people. But I can get you a more definitive answer on what the overlap is. I truly don't know. But second part of the question was based around--

AUDIENCE: I guess it has to do with interval and range. So the intervals at which you're taking pictures are ranged at how far they have to be apart from each other for you to create the proper amount of depth.

PETER SOUTHWOOD: I think we stand to [INAUDIBLE] 90 feet accuracy when it comes to visualizing the assets like on a pole. Which can be quite substantially distanced. We only drive based upon clients requirements. So we typically take an Esri shape file as the road center line and that's what we drive.

If you are a DOT or an organization with wide roads, multiple directions, multiple passes, so that gives you passes in both directions. And like I said, it's just purely based upon the client requirements. We don't go out going crazy capturing everything. I believe it's approximately 90 feet. I should know that, but I will find out for you.

STEPHEN BROCKWELL: But there was a question about the capture interval, too. That's generally quite fixed, isn't it?

PETER SOUTHWOOD: The capture interval is fixed, every 15 feet, every five meters. That's it.

AUDIENCE: And is it-- do you have to be stopped?

PETER SOUTHWOOD: No, the vehicle just carries on. That's what a vehicle, one vehicle, on average 40 linear miles per day.

AUDIENCE: Is there a speed limit [INAUDIBLE] do like highway driving [INAUDIBLE].

PETER SOUTHWOOD: For the LiDAR unit, I think there's a-- and I'll need to check on the specification-- I think it's around 60.

STEPHEN BROCKWELL: By the way, you do have that information on there. This question was about speed limits and data collection. So for the LiDAR, that data is in the presentation, which will be up on the website for this, along with [? Shaun's ?] little white paper about some of the tricks we did use to do this.

PETER SOUTHWOOD: And we typically, obviously, drive to the speed limit.

AUDIENCE: Right, right. [INAUDIBLE]

PETER SOUTHWOOD: Big smiley face on us at that. So a question.

AUDIENCE: I had a question about, you know, the [INAUDIBLE] applications where, like, you mentioned the example of signages, traffic signages. So right now, you probably have to go through all this, all the photographs, [INAUDIBLE] oh, there's a signage there. Or do you have technology where you can actually recognize and give me like a [INAUDIBLE] of the signages [INAUDIBLE]?

PETER SOUTHWOOD: So the question around the OCR, optical character recognition. By the way, Raster Design, if you ever seen that from Autodesk, it does great OCR. I used to train that years ago. Anyway, we digress.

We have an automated, semi-automated solution. Algorithms actually go through the imagery. Because if you look at the imagery, it's a full 360 panoramic image. So you could have a license plate that we blur in one image, but you may find the same license plate in 10 other images.

So we do have algorithms specifically for street signage. That actually checks with the MUTC database. So we'll do a little bit of OCR, it reads stop. It checks with the database, the national database for the signage, and we'll actually build that database around that.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: You know, it has to be fit into a QA process, as well. Because the quality of that is not as easy to define. But that is a proposal that is in front of the city of Alexandria right now, especially for their hydrants. And you tend to have to be very specific, right. Like you can't say find everything. Find hydrants. That's a manageable problem that has a boundary that can be contained.

PETER SOUTHWOOD: In a practical example, I'm finding that a lot at the moment with 5G buildouts for various cities around the United States. Not sure if anyone's aware of this. Reverse engineering firms have been asked to do 5G permits for communication organizations.

But when they talk about aerial, it's everything on a utility pole. Because they're looking at the ownership of the pole and where they can put their assets in. Through, to what they call underground, which is quite simply not underground in a traditional sense, is looking at like a manhole cover, and reading the manhole cover to see what this says, level 3 AT&T, because they're run by different organizations. So using similar algorithms to go, quickly, Pete, let's get this information together. So, yes.

STEPHEN BROCKWELL: I've talked about the multiple sources and how they have to fit together. So, I mean, this is not a complete view. Obviously, we talked about that. And you have to complement it with other things if you want the absolute complete picture.

Challenges. So, Civil 3D and all the AutoCAD derivatives have a pretty consistent API and approach. And it's a really well-known one, really well supported APIs. And that was really a lot of fun to do. We were able to use really good techniques to do it.

Revit presented some unexpected challenges. So that is something that we're going to be looking at in the future to see how we can do that a little bit better. There's some beautiful things about the Revit API, just in terms of plugging it in and the interfaces that are defined for it. But there's some challenges, especially because of the way there is no real GIS data in there. So that's something we're looking at.

On the InfraWorks side, the API that's available today is quite limited and JavaScript focused. I don't know if there's anyone here who does have experience writing their own plug-ins for InfraWorks. It's something the development team tends to do. But if you do, I'd really like to talk to you. Because we had issues with that.

That was one of the products that it would go really well into. And the LiDAR can, of course, the LiDAR can absolutely go right into InfraWorks. And then be used with some of the new web services they have for feature extraction and that kind of thing. So that the LiDAR is usable inside InfraWorks, no problem.

But getting a plug-in for InfraWorks with that same interface is something that we have not been able to do. The other problem is, right now, our initial spec, the LiDAR files are just too small. They're very dense. But if you're going to be using them on a computer that's of appropriate size, they need to be bigger, even, to be able to sort of fit in seamlessly at a usable level. Because there is a lot of data there. So the LiDAR itself cannot be used just willy-nilly on any kind of platform. To be used effectively, it has to be a little bit better organized. So we're working on that to make about a quarter as many files, at least, that are a little bit more usable in groups. Because we have to do grouping and aggregation, that's the whole art of it.

And then the other part of that is on the ReCap side. Processing 300,000 ReCap files or LES files into ReCap files was an enormous challenge for us, just in terms of reality. There's a DeCap. I don't know if you folks know this, but there is a command-line tool that will do it. It's called DeCap. It comes with ReCap. But performance of it, we wrote a little-- I guess it's node sort of application.

AUDIENCE: Python.

STEPHEN BROCKWELL: Python. Application driver for it. So it just scans all of the files that are there. Tries to see if they have any data in them. And then runs ReCap on a project level and does all that work. That was extremely time consuming. The vast majority of time in the project was actually that part of the process, the post-processing of the captured LiDAR files.

Now this is-- we didn't talk about this really. How is this deployed? All that data is in the cloud. So that panoramic imagery is in the cloud and it's all available. The LiDAR is typically shipped, up until recently, on disk to you as a customer. Which is useful and that probably is something that we'll still be able to do.

But they're putting in place an on demand cloud service for the LiDAR data, as well. Which is really convenient because you can go out and get it as needed. And depending upon your bandwidth and everything like that, you can get it for a certain area and then process the ReCap files on the fly and load them.

But interestingly enough the ReCap DeCap part of the project was, by far, the most difficult for us to manage. So that's something we're going to be trying to talk to the Autodesk ReCap team about. To get-- just better improvement. I think they now support LAZ directly. So you can just load an LAZ file, whereas it used to be LAS. So that should improve it. But we'd like to get something a little bit more reliable, even, than that. So we'll see. Yeah.

AUDIENCE: Question from earlier. When you were picking on the left to create line work on the right. When you pick, you're actually selecting a point from the LiDAR point cloud? Or is it [INAUDIBLE]?

STEPHEN BROCKWELL: I mean, I don't know the precise answer for that. I'll let Pete answer that one. Because it's a bit of both, actually.

PETER SOUTHWOOD: Yes.

[LAUGHTER]

STEPHEN BROCKWELL: Right. [INAUDIBLE] It's sort of, the way I understand, and Pete and I may not be able to give you the right answer. We may have to go get it. But the question is, when measuring, what is the source of measurement? Is it just photogrammetric calculation off the image? Or is it from the LiDAR, or what? And the answer is, it's a bit of both. So if you've got the LiDAR collection, there's an algorithm that is using some of that data in the backend processing to underpin the measurement with the LiDAR data, so you can get elevations and those kinds of things. But I don't know the details.

PETER SOUTHWOOD: Behind the scenes, what it's doing is actually taking triangulation from three different recording locations. So you pick a point somewhere on the screen. It's actually going off, finding the other point from two other recording locations. Giving that triangulation. So you're actually getting the correct point. Not just picking a point in some random area.

AUDIENCE: [INAUDIBLE] follows to that is, what is your accuracy again? [INAUDIBLE]

PETER SOUTHWOOD: Positional accuracy is sub-four inches. That can be tightened up with ground stations.

AUDIENCE: X, Y, and Z?

PETER SOUTHWOOD: Yes. And measurement accuracy is sub-inch, is 19 millimeters that we stand to. So I'm sorry about the 1.9-- Yeah, 19 millimeters. That's what, a half inch? [INAUDIBLE] Just over--

STEPHEN BROCKWELL: Little bit more than that.

PETER SOUTHWOOD: Yeah, a little over a half inch.

AUDIENCE: It's confusing. You're saying that the accuracy of the data is sub-four inch, but measurement--

PETER SOUTHWOOD: Positional accuracy.

AUDIENCE: OK.

[INAUDIBLE]

PETER SOUTHWOOD: Relative positional accuracy.

AUDIENCE: OK. And the final question is that line I never thought I'd ever see, is LiDAR files are too small.

[LAUGHTER]

STEPHEN BROCKWELL: Yeah. Sorry, yeah.

AUDIENCE: I'm not quite sure [INAUDIBLE].

STEPHEN BROCKWELL: It's the size they're captured at. In Europe, they're more manageable because they're 50 meters in size. In North America, until recently, they were 50 feet in size. A 50 foot LiDAR file isn't the most useful thing when you're using it inside a design tool, to be honest. 50 feet at a street level is just barely-- it's not even a block. You know what I mean? So from that point of view, they are quite seriously, they're too small to be really a practical use. Because then what you have to do is in your ReCap file-- And we have a tool that helps you navigate that.

So we have a tool that shows you what LiDAR files are available, and we have to sort of bundle them together to make them come in in a way that they actually look meaningful and can be used for design. And that is a problem because-- but then once you've got it loaded--

And AutoCAD with ReCap files, it's one of the virtues of ReCap as a format. Autodesk products do incredible things with them. Like once it's in AutoCAD with the ReCap data that's behind it, you know, setting the level of detail, setting colors, you know all of that kind of thing is really easy to do.

So performance can be tweaked, essentially, for you. And you can do surface and other kinds of things with it, with the point cloud toolbar that is right in AutoCAD. But yeah, 50 feet is too small. So I think it's 150 feet now, that you're doing, which is closer to 50 meters.

PETER SOUTHWOOD: I believe so.

STEPHEN BROCKWELL: Which is done in Europe.

AUDIENCE: You mentioned API?

STEPHEN BROCKWELL: Yeah.

AUDIENCE: Is there some sort of API [INAUDIBLE]?

STEPHEN BROCKWELL: Yeah, yeah. Yeah. Yeah. So the Street Smart tool has a fully supported API. That is what we used to do it. And it's actually great. I mean, we've worked with them a lot on it. Because using it in AutoCAD, events and threading, and all that stuff caused a lot of problems. Sean can talk to you about some of that. And we do plan to put up, with the PowerPoint, which will be up on the AU website. A little white paper on some of the details, not all of them, of course, but a handful of them.

AUDIENCE: Are you guys going to be in the expo?

STEPHEN BROCKWELL: Yeah, we are.

PETER SOUTHWOOD: Sorry.

STEPHEN BROCKWELL: The question was are we at the expo.

PETER SOUTHWOOD: Yes.

AUDIENCE: I was wondering can you [INAUDIBLE] spin around the view? Can you like look down at the ground?

STEPHEN BROCKWELL: Yeah. Up. Down. Yep.

PETER SOUTHWOOD: Full 180, 360.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: Yeah.

PETER SOUTHWOOD: I shouldn't say this, a normal reaction I get when we showed this is wow. Because you're reading asset tags on the side of a transformer from about 100 feet away.

AUDIENCE: [INAUDIBLE]

STEPHEN BROCKWELL: I will say, you know like it is, we're now at the point where the tools and the data and the data processing on the back-end-- And this is-- you know, people complain about the cloud and everything like that, and, for some, security, all the issues that are around it-- but this is one of the better uses of cloud technology I've seen. Because the volumes of data are something you don't want to store internally. You don't want to have to install a server for this and manage it yourself, and everything like that and create a whole sort of ecosystem internally around that. This is just a service that you use. Yeah.

AUDIENCE: Not to be critical, but maybe you can verify. This isn't, at this point in time, a survey grade tool? [INAUDIBLE] To be able to back it up through, not the type of litigation that you're talking about, but if you were to rectify an alignment on a roadway design for DOT. There are different standard practices that we would have to follow [INAUDIBLE] verify that.

PETER SOUTHWOOD: So without repeating the question, but I think my statement is going to hopefully help answer the question, and we call it professional grade imagery. You are still going to need your PE, your professional engineer to have that stamp. Do we do ADA compliance? Look at the imagery and that's ADA compliant. No. It's still going to require a professional engineer to say, hey, that doesn't comply.

But what you can get with that, level of accuracy and a high level of confidence. That's whole point. 'Cause you've got a source of truth. High level of confidence what's going on there. Then make that decision. But you're absolutely right. It's going to require that professional. And using professional tools from Esri or Autodesk to actually use--

Very happy Esri customers that actually have the imagery embedded into an ArcGIS based solution. And they're using the imagery as a source of truth to validate the data they've already had in there. And respectfully, to our Esri customers, the old joke, you know what GIS stands for?

STEPHEN BROCKWELL: Yeah. And one thing it's important, too, [INAUDIBLE] it is transparent about the quality you're getting. So that's important, actually.

PETER SOUTHWOOD: Yes.

STEPHEN BROCKWELL: So you can tell what your level of confidence is. That's got the sigma on each point as part of your measurements. So if you're measuring a line, or like that curve, you're getting-- And the curve, we actually do create curves from it. Arcs. We don't just create interpolated points. But you know what the measurement inaccuracies are. So that's also incredibly valuable. But you're totally right. And it's not a criticism, really, as I see it.

PETER SOUTHWOOD: Yeah, didn't take it--

STEPHEN BROCKWELL: Totally fair point.

PETER SOUTHWOOD: Absolutely.

STEPHEN BROCKWELL: Yep.

PETER SOUTHWOOD: Yep.

AUDIENCE: We're starting to allude to adding additional controls online? You were mentioning-- You were starting to say we could have additional control with [INAUDIBLE]

STEPHEN BROCKWELL: No, no. It's just that it's transparent about the accuracy you have. So in other words, it reports out as you're doing your measurement for every point what the level of accuracy is, and the standard deviation of that, too.

AUDIENCE: [INAUDIBLE]

PETER SOUTHWOOD: Yeah. So taking into consideration the question around sort of tightening up the position of accuracy from ground control points. Yes. So clients like the city of New York will supply that and we can tighten up the imagery even further. Yes.

STEPHEN BROCKWELL: OK. Thanks very much folks.

[APPLAUSE]

______
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오토데스크에서 사용하는타사 서비스개인정보 처리방침 정책을 자세히 알아보십시오.

반드시 필요 - 사이트가 제대로 작동하고 사용자에게 서비스를 원활하게 제공하기 위해 필수적임

이 쿠키는 오토데스크에서 사용자 기본 설정 또는 로그인 정보를 저장하거나, 사용자 요청에 응답하거나, 장바구니의 품목을 처리하기 위해 필요합니다.

사용자 경험 향상 – 사용자와 관련된 항목을 표시할 수 있게 해 줌

이 쿠키는 오토데스크가 보다 향상된 기능을 제공하고 사용자에게 맞는 정보를 제공할 수 있게 해 줍니다. 사용자에게 맞는 정보 및 환경을 제공하기 위해 오토데스크 또는 서비스를 제공하는 협력업체에서 이 쿠키를 설정할 수 있습니다. 이 쿠키를 허용하지 않을 경우 이러한 서비스 중 일부 또는 전체를 이용하지 못하게 될 수 있습니다.

광고 수신 설정 – 사용자에게 타겟팅된 광고를 제공할 수 있게 해 줌

이 쿠키는 사용자와 관련성이 높은 광고를 표시하고 그 효과를 추적하기 위해 사용자 활동 및 관심 사항에 대한 데이터를 수집합니다. 이렇게 데이터를 수집함으로써 사용자의 관심 사항에 더 적합한 광고를 표시할 수 있습니다. 이 쿠키를 허용하지 않을 경우 관심 분야에 해당되지 않는 광고가 표시될 수 있습니다.

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타사 서비스

각 범주에서 오토데스크가 사용하는 타사 서비스와 온라인에서 고객으로부터 수집하는 데이터를 사용하는 방식에 대해 자세히 알아보십시오.

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반드시 필요 - 사이트가 제대로 작동하고 사용자에게 서비스를 원활하게 제공하기 위해 필수적임

Qualtrics
오토데스크는 고객에게 더욱 시의적절하며 관련 있는 이메일 컨텐츠를 제공하기 위해 Qualtrics를 이용합니다. 이를 위해, 고객의 온라인 행동 및 오토데스크에서 전송하는 이메일과의 상호 작용에 관한 데이터를 수집합니다. 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID, 이메일 확인율, 클릭한 링크 등이 포함될 수 있습니다. 오토데스크는 이 데이터를 다른 소스에서 수집된 데이터와 결합하여 고객의 판매 또는 고객 서비스 경험을 개선하며, 고급 분석 처리에 기초하여 보다 관련 있는 컨텐츠를 제공합니다. Qualtrics 개인정보취급방침
Akamai mPulse
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Akamai mPulse를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Akamai mPulse 개인정보취급방침
Digital River
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Digital River를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Digital River 개인정보취급방침
Dynatrace
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Dynatrace를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Dynatrace 개인정보취급방침
Khoros
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Khoros를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Khoros 개인정보취급방침
Launch Darkly
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Launch Darkly를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Launch Darkly 개인정보취급방침
New Relic
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 New Relic를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. New Relic 개인정보취급방침
Salesforce Live Agent
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Salesforce Live Agent를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Salesforce Live Agent 개인정보취급방침
Wistia
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Wistia를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Wistia 개인정보취급방침
Tealium
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Tealium를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Upsellit
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Upsellit를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. CJ Affiliates
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 CJ Affiliates를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Commission Factory
Typepad Stats
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Typepad Stats를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Typepad Stats 개인정보취급방침
Geo Targetly
Autodesk는 Geo Targetly를 사용하여 웹 사이트 방문자를 가장 적합한 웹 페이지로 안내하거나 위치를 기반으로 맞춤형 콘텐츠를 제공합니다. Geo Targetly는 웹 사이트 방문자의 IP 주소를 사용하여 방문자 장치의 대략적인 위치를 파악합니다. 이렇게 하면 방문자가 (대부분의 경우) 현지 언어로 된 콘텐츠를 볼 수 있습니다.Geo Targetly 개인정보취급방침
SpeedCurve
Autodesk에서는 SpeedCurve를 사용하여 웹 페이지 로드 시간과 이미지, 스크립트, 텍스트 등의 후속 요소 응답성을 측정하여 웹 사이트 환경의 성능을 모니터링하고 측정합니다. SpeedCurve 개인정보취급방침
Qualified
Qualified is the Autodesk Live Chat agent platform. This platform provides services to allow our customers to communicate in real-time with Autodesk support. We may collect unique ID for specific browser sessions during a chat. Qualified Privacy Policy

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사용자 경험 향상 – 사용자와 관련된 항목을 표시할 수 있게 해 줌

Google Optimize
오토데스크는 사이트의 새 기능을 테스트하고 이러한 기능의 고객 경험을 사용자화하기 위해 Google Optimize을 이용합니다. 이를 위해, 고객이 사이트를 방문해 있는 동안 행동 데이터를 수집합니다. 이 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID, 오토데스크 ID 등이 포함될 수 있습니다. 고객은 기능 테스트를 바탕으로 여러 버전의 오토데스크 사이트를 경험하거나 방문자 특성을 바탕으로 개인화된 컨텐츠를 보게 될 수 있습니다. Google Optimize 개인정보취급방침
ClickTale
오토데스크는 고객이 사이트에서 겪을 수 있는 어려움을 더 잘 파악하기 위해 ClickTale을 이용합니다. 페이지의 모든 요소를 포함해 고객이 오토데스크 사이트와 상호 작용하는 방식을 이해하기 위해 세션 녹화를 사용합니다. 개인적으로 식별 가능한 정보는 가려지며 수집되지 않습니다. ClickTale 개인정보취급방침
OneSignal
오토데스크는 OneSignal가 지원하는 사이트에 디지털 광고를 배포하기 위해 OneSignal를 이용합니다. 광고는 OneSignal 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 OneSignal에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 OneSignal에 제공하는 데이터를 사용합니다. OneSignal 개인정보취급방침
Optimizely
오토데스크는 사이트의 새 기능을 테스트하고 이러한 기능의 고객 경험을 사용자화하기 위해 Optimizely을 이용합니다. 이를 위해, 고객이 사이트를 방문해 있는 동안 행동 데이터를 수집합니다. 이 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID, 오토데스크 ID 등이 포함될 수 있습니다. 고객은 기능 테스트를 바탕으로 여러 버전의 오토데스크 사이트를 경험하거나 방문자 특성을 바탕으로 개인화된 컨텐츠를 보게 될 수 있습니다. Optimizely 개인정보취급방침
Amplitude
오토데스크는 사이트의 새 기능을 테스트하고 이러한 기능의 고객 경험을 사용자화하기 위해 Amplitude을 이용합니다. 이를 위해, 고객이 사이트를 방문해 있는 동안 행동 데이터를 수집합니다. 이 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID, 오토데스크 ID 등이 포함될 수 있습니다. 고객은 기능 테스트를 바탕으로 여러 버전의 오토데스크 사이트를 경험하거나 방문자 특성을 바탕으로 개인화된 컨텐츠를 보게 될 수 있습니다. Amplitude 개인정보취급방침
Snowplow
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Snowplow를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Snowplow 개인정보취급방침
UserVoice
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 UserVoice를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. UserVoice 개인정보취급방침
Clearbit
Clearbit를 사용하면 실시간 데이터 보강 기능을 통해 고객에게 개인화되고 관련 있는 환경을 제공할 수 있습니다. Autodesk가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. Clearbit 개인정보취급방침
YouTube
YouTube는 사용자가 웹 사이트에 포함된 비디오를 보고 공유할 수 있도록 해주는 비디오 공유 플랫폼입니다. YouTube는 비디오 성능에 대한 시청 지표를 제공합니다. YouTube 개인정보보호 정책

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광고 수신 설정 – 사용자에게 타겟팅된 광고를 제공할 수 있게 해 줌

Adobe Analytics
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Adobe Analytics를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID 및 오토데스크 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. Adobe Analytics 개인정보취급방침
Google Analytics (Web Analytics)
오토데스크 사이트에서 고객의 행동에 관한 데이터를 수집하기 위해 Google Analytics (Web Analytics)를 이용합니다. 여기에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 오토데스크는 사이트 성과를 측정하고 고객의 온라인 경험의 편리함을 평가하여 기능을 개선하기 위해 이러한 데이터를 이용합니다. 또한, 이메일, 고객 지원 및 판매와 관련된 고객 경험을 최적화하기 위해 고급 분석 방법도 사용하고 있습니다. AdWords
Marketo
오토데스크는 고객에게 더욱 시의적절하며 관련 있는 이메일 컨텐츠를 제공하기 위해 Marketo를 이용합니다. 이를 위해, 고객의 온라인 행동 및 오토데스크에서 전송하는 이메일과의 상호 작용에 관한 데이터를 수집합니다. 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID, 이메일 확인율, 클릭한 링크 등이 포함될 수 있습니다. 오토데스크는 이 데이터를 다른 소스에서 수집된 데이터와 결합하여 고객의 판매 또는 고객 서비스 경험을 개선하며, 고급 분석 처리에 기초하여 보다 관련 있는 컨텐츠를 제공합니다. Marketo 개인정보취급방침
Doubleclick
오토데스크는 Doubleclick가 지원하는 사이트에 디지털 광고를 배포하기 위해 Doubleclick를 이용합니다. 광고는 Doubleclick 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Doubleclick에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Doubleclick에 제공하는 데이터를 사용합니다. Doubleclick 개인정보취급방침
HubSpot
오토데스크는 고객에게 더욱 시의적절하며 관련 있는 이메일 컨텐츠를 제공하기 위해 HubSpot을 이용합니다. 이를 위해, 고객의 온라인 행동 및 오토데스크에서 전송하는 이메일과의 상호 작용에 관한 데이터를 수집합니다. 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID, 이메일 확인율, 클릭한 링크 등이 포함될 수 있습니다. HubSpot 개인정보취급방침
Twitter
오토데스크는 Twitter가 지원하는 사이트에 디지털 광고를 배포하기 위해 Twitter를 이용합니다. 광고는 Twitter 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Twitter에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Twitter에 제공하는 데이터를 사용합니다. Twitter 개인정보취급방침
Facebook
오토데스크는 Facebook가 지원하는 사이트에 디지털 광고를 배포하기 위해 Facebook를 이용합니다. 광고는 Facebook 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Facebook에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Facebook에 제공하는 데이터를 사용합니다. Facebook 개인정보취급방침
LinkedIn
오토데스크는 LinkedIn가 지원하는 사이트에 디지털 광고를 배포하기 위해 LinkedIn를 이용합니다. 광고는 LinkedIn 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 LinkedIn에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 LinkedIn에 제공하는 데이터를 사용합니다. LinkedIn 개인정보취급방침
Yahoo! Japan
오토데스크는 Yahoo! Japan가 지원하는 사이트에 디지털 광고를 배포하기 위해 Yahoo! Japan를 이용합니다. 광고는 Yahoo! Japan 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Yahoo! Japan에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Yahoo! Japan에 제공하는 데이터를 사용합니다. Yahoo! Japan 개인정보취급방침
Naver
오토데스크는 Naver가 지원하는 사이트에 디지털 광고를 배포하기 위해 Naver를 이용합니다. 광고는 Naver 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Naver에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Naver에 제공하는 데이터를 사용합니다. Naver 개인정보취급방침
Quantcast
오토데스크는 Quantcast가 지원하는 사이트에 디지털 광고를 배포하기 위해 Quantcast를 이용합니다. 광고는 Quantcast 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Quantcast에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Quantcast에 제공하는 데이터를 사용합니다. Quantcast 개인정보취급방침
Call Tracking
오토데스크는 캠페인을 위해 사용자화된 전화번호를 제공하기 위하여 Call Tracking을 이용합니다. 그렇게 하면 고객이 오토데스크 담당자에게 더욱 빠르게 액세스할 수 있으며, 오토데스크의 성과를 더욱 정확하게 평가하는 데 도움이 됩니다. 제공된 전화번호를 기준으로 사이트에서 고객 행동에 관한 데이터를 수집할 수도 있습니다. Call Tracking 개인정보취급방침
Wunderkind
오토데스크는 Wunderkind가 지원하는 사이트에 디지털 광고를 배포하기 위해 Wunderkind를 이용합니다. 광고는 Wunderkind 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Wunderkind에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Wunderkind에 제공하는 데이터를 사용합니다. Wunderkind 개인정보취급방침
ADC Media
오토데스크는 ADC Media가 지원하는 사이트에 디지털 광고를 배포하기 위해 ADC Media를 이용합니다. 광고는 ADC Media 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 ADC Media에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 ADC Media에 제공하는 데이터를 사용합니다. ADC Media 개인정보취급방침
AgrantSEM
오토데스크는 AgrantSEM가 지원하는 사이트에 디지털 광고를 배포하기 위해 AgrantSEM를 이용합니다. 광고는 AgrantSEM 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 AgrantSEM에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 AgrantSEM에 제공하는 데이터를 사용합니다. AgrantSEM 개인정보취급방침
Bidtellect
오토데스크는 Bidtellect가 지원하는 사이트에 디지털 광고를 배포하기 위해 Bidtellect를 이용합니다. 광고는 Bidtellect 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Bidtellect에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Bidtellect에 제공하는 데이터를 사용합니다. Bidtellect 개인정보취급방침
Bing
오토데스크는 Bing가 지원하는 사이트에 디지털 광고를 배포하기 위해 Bing를 이용합니다. 광고는 Bing 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Bing에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Bing에 제공하는 데이터를 사용합니다. Bing 개인정보취급방침
G2Crowd
오토데스크는 G2Crowd가 지원하는 사이트에 디지털 광고를 배포하기 위해 G2Crowd를 이용합니다. 광고는 G2Crowd 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 G2Crowd에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 G2Crowd에 제공하는 데이터를 사용합니다. G2Crowd 개인정보취급방침
NMPI Display
오토데스크는 NMPI Display가 지원하는 사이트에 디지털 광고를 배포하기 위해 NMPI Display를 이용합니다. 광고는 NMPI Display 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 NMPI Display에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 NMPI Display에 제공하는 데이터를 사용합니다. NMPI Display 개인정보취급방침
VK
오토데스크는 VK가 지원하는 사이트에 디지털 광고를 배포하기 위해 VK를 이용합니다. 광고는 VK 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 VK에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 VK에 제공하는 데이터를 사용합니다. VK 개인정보취급방침
Adobe Target
오토데스크는 사이트의 새 기능을 테스트하고 이러한 기능의 고객 경험을 사용자화하기 위해 Adobe Target을 이용합니다. 이를 위해, 고객이 사이트를 방문해 있는 동안 행동 데이터를 수집합니다. 이 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역, IP 주소 또는 장치 ID, 오토데스크 ID 등이 포함될 수 있습니다. 고객은 기능 테스트를 바탕으로 여러 버전의 오토데스크 사이트를 경험하거나 방문자 특성을 바탕으로 개인화된 컨텐츠를 보게 될 수 있습니다. Adobe Target 개인정보취급방침
Google Analytics (Advertising)
오토데스크는 Google Analytics (Advertising)가 지원하는 사이트에 디지털 광고를 배포하기 위해 Google Analytics (Advertising)를 이용합니다. 광고는 Google Analytics (Advertising) 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Google Analytics (Advertising)에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Google Analytics (Advertising)에 제공하는 데이터를 사용합니다. Google Analytics (Advertising) 개인정보취급방침
Trendkite
오토데스크는 Trendkite가 지원하는 사이트에 디지털 광고를 배포하기 위해 Trendkite를 이용합니다. 광고는 Trendkite 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Trendkite에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Trendkite에 제공하는 데이터를 사용합니다. Trendkite 개인정보취급방침
Hotjar
오토데스크는 Hotjar가 지원하는 사이트에 디지털 광고를 배포하기 위해 Hotjar를 이용합니다. 광고는 Hotjar 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Hotjar에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Hotjar에 제공하는 데이터를 사용합니다. Hotjar 개인정보취급방침
6 Sense
오토데스크는 6 Sense가 지원하는 사이트에 디지털 광고를 배포하기 위해 6 Sense를 이용합니다. 광고는 6 Sense 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 6 Sense에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 6 Sense에 제공하는 데이터를 사용합니다. 6 Sense 개인정보취급방침
Terminus
오토데스크는 Terminus가 지원하는 사이트에 디지털 광고를 배포하기 위해 Terminus를 이용합니다. 광고는 Terminus 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 Terminus에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 Terminus에 제공하는 데이터를 사용합니다. Terminus 개인정보취급방침
StackAdapt
오토데스크는 StackAdapt가 지원하는 사이트에 디지털 광고를 배포하기 위해 StackAdapt를 이용합니다. 광고는 StackAdapt 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 StackAdapt에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 StackAdapt에 제공하는 데이터를 사용합니다. StackAdapt 개인정보취급방침
The Trade Desk
오토데스크는 The Trade Desk가 지원하는 사이트에 디지털 광고를 배포하기 위해 The Trade Desk를 이용합니다. 광고는 The Trade Desk 데이터와 고객이 사이트를 방문하는 동안 오토데스크가 수집하는 행동 데이터 모두에 기초하여 제공됩니다. 오토데스크가 수집하는 데이터에는 고객이 방문한 페이지, 시작한 체험판, 재생한 동영상, 구매 내역 및 IP 주소 또는 장치 ID가 포함될 수 있습니다. 이 정보는 The Trade Desk에서 고객으로부터 수집한 데이터와 결합될 수 있습니다. 오토데스크는 디지털 광고 경험에 대한 사용자화를 개선하고 고객에게 더욱 관련 있는 광고를 제시하기 위해 The Trade Desk에 제공하는 데이터를 사용합니다. The Trade Desk 개인정보취급방침
RollWorks
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정말 더 적은 온라인 경험을 원하십니까?

오토데스크는 고객 여러분에게 좋은 경험을 드리고 싶습니다. 이전 화면의 범주에 대해 "예"를 선택하셨다면 오토데스크는 고객을 위해 고객 경험을 사용자화하고 향상된 응용프로그램을 제작하기 위해 귀하의 데이터를 수집하고 사용합니다. 언제든지 개인정보 처리방침을 방문해 설정을 변경할 수 있습니다.

고객의 경험. 고객의 선택.

오토데스크는 고객의 개인 정보 보호를 중요시합니다. 오토데스크에서 수집하는 정보는 오토데스크 제품 사용 방법, 고객이 관심을 가질 만한 정보, 오토데스크에서 더욱 뜻깊은 경험을 제공하기 위한 개선 사항을 이해하는 데 도움이 됩니다.

오토데스크에서 고객님께 적합한 경험을 제공해 드리기 위해 고객님의 데이터를 수집하고 사용하도록 허용하시겠습니까?

선택할 수 있는 옵션을 자세히 알아보려면 이 사이트의 개인 정보 설정을 관리해 사용자화된 경험으로 어떤 이점을 얻을 수 있는지 살펴보거나 오토데스크 개인정보 처리방침 정책을 확인해 보십시오.