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From Flat to Fab: Greening Landscapes with Landscape Information Modeling and Artificial Intelligence

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Description

It is time for landscape to step up its digital game and claim a bigger slice of the design spotlight alongside architects and engineers. This session will explore the direct leap from 2D to landscape information modeling (LIM), with a twist: envisioning AI's role in landscape. This session will explore how Henning Larsen architects transition from traditional 2D design to LIM with Revit tools and workflows, while pointing out both challenges and opportunities. Toward the end of the session, we'll explore how using AI can empower landscapers to optimize resource management, enhance water efficiency, and propel sustainable practices. Furthermore, we'll explore the potential of AI-driven LIM for critical sustainability metrics, such as energy consumption, carbon footprint, and ecological equilibrium. This session will advocate for landscape design's digital evolution, transitioning to LIM with AI possibility, as exemplified by the experiences of Henning Larsen architects.

Key Learnings

  • Explore the process of transitioning from 2D landscape designs to landscape information modeling.
  • Gain practical insights and tools to incorporate LIM workflows into your own projects.
  • Discover the possible role of AI in streamlining the conversion process and enhancing sustainability outcomes.

Speakers

  • Diana Cristina Binciu
    Diana Cristina Binciu is an Urban Designer at Henning Larsen Architects, where she plays a key role in transitioning the firm from traditional 2D design to Landscape Information Modeling (LIM). Diana has an urban planning background, focusing on Blue-Green Infrastructure, Nature-Based Solutions and Integrated Infrastructure and she is passionate about spaces and nature. During her previous tenure at Rambøll, she contributed to urban planning projects and supported BIM coordination for Scan to Revit modeling on large scale projects. Passionate about digital innovation in landscape design, she is dedicated to integrating LIM and AI to optimize resource management, enhance efficiency, and drive sustainable practices.
  • David Andrew Fink
    David Fink is the Digital Manager at Henning Larsen Architects where he is responsible for the development of the office’s digital platform. Before starting at Henning Larsen, he worked in the Integrated Digital Solutions department at Ramboll and as a BIM Manager at Schmidt Hammer Lassen. He is interested in anything digital and is constantly looking for ways to expand the boundaries and use of BIM and digitalization. David is interested in finding ways to increasing project quality and efficiency while having some fun in the process. David is the chairperson, and one of the founding members of the BIM Copenhagen network group. David is originally from the Denver but has been based in Copenhagen since 2001.
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Transcript

DIANA CRISTINA BINCIU: Hi, and welcome to our session-- From Flat to Fab, Greening Landscapes with AI and LIM. About the session, shortly, we think it's time for landscape to step up the digital game and claim a bigger slice of the design spotlight with architects and engineers. The From Flat to Fab session focuses on exploring the process of transitioning from 2D landscape designs to Landscape Information Modeling, as in LIM, to gaining insights on how we can use AI at the moment and discover the possible role of AI in enhancing sustainability outcomes and processes.

My name is Diana. I am an urban architect and the BIM specialist for Henning Larsen Architects in the landscape team. And I've been working in this role for the past two years, trying to help the team enhance their 3D capabilities. And this is my colleague, David.

DAVID FINK: Hi, my name is David Fink. I'm the digital manager here at Henning Larsen. So I work with our digital platform for developing projects and also help with the tools, develop working processes, and look for the new technologies that are coming up in the future. So I've been involved with digitalization since around 2007 with the Danish BIM mandate, which was a requirement that all public buildings be done in BIM. So I have a pretty good background in digitalization now.

DIANA CRISTINA BINCIU: Now, we both work at Henning Larsen. For those of you who don't know, the company was founded in '59 by the Danish architect Henning Larsen, whose legacy of creativity and learning we still carry today. We navigate through complex connections that aim to bind together our built environment, ecological systems, and societies in large.

We work across multiple disciplines and we try to maintain acute attention to details whilst never losing sight of the bigger picture. We're also quite big. We have over 600 employees across studios in eight countries.

Like I'd mentioned before, we have a big portfolio. It spans through a range of disciplines-- through architecture, urbanism, landscape, interior, and graphic design, and then research and innovation.

The agenda for today-- we will start by selecting a study case, in this case, a project called Faelledby that we will take from 2D to 3D to LIM. And then we will show how we use artificial intelligence technology that exists today and then what we envision for the future.

The project is called the-- Faelledby. So the Faelledby project is designed by Henning Larsen, is a sustainable residential development located in Copenhagen, Denmark. It envisions an eco-friendly community where urban living harmonizes with nature. The design aims to preserve 30% of the natural landscape and integrates local biodiversity. It's aiming to blending greening spaces with residential areas, and biodiversity, and so on.

It's modeled after small villages. Faelledby fosters social interactions while emphasizing environmental sustainability throughout the use of eco-conscious, materials, renewable energy, and nature-based solution. This project aims to strike a balance between the human factor and the natural environment.

In short, it's Copenhagen's first timber neighborhood. Faelledby brings the urban environment into harmony with the natural landscapes, establishing a community of ecologically responsive housing units organized according to the model of a rural village.

Some facts about the project. It's around 44 to 45 acres in land area. 2 million square feet of building area that's supposed to house around 7,000 residents. 80% of the structural materials should be timber. And 45% of the site is used for natural habitats. And, of course, a project so complex involves multiple stakeholders.

This is how we envision the project. All buildings are supposed to use sustainable materials, primary timber construction-- primarily timber construction. It's supposed to mix nature into the building environment and increase biodiversity.

And, of course, we are actually building it. It's on its way. You could also see the Ramboll building there in the background. So, hopefully, in a few years maybe you can come visit and actually see the project.

DAVID FINK: So-- but before we get into looking at AI and how we use it for our landscape workflows, I thought we'd just take a little journey to see how we implemented using 3D landscape and LIM in-- at Henning Larsen.

So we started this journey about five years ago. Before, we were primarily using MicroStation and AutoCAD for landscape design. But now we've moved to Revit and Rhino as our primary platform. So this move was not easy. And we're still not finished, but we've definitely come a long way since the beginning.

So the idea is, at Henning Larsen, that most of our projects start with a story. In the case of Faelledby, it was the merging of the small town and the big city and nature areas. So we started with, of course, hand sketches designed-- from a design architect. And this was quickly translated into a Rhino model, which was the primary sketching tool for the early phases.

We use Rhino primarily because it was easier to sketch and generate designs and also produce multiple design iterations. Then after the design became more fixed, we moved the project to Revit. But Revit, as we know, is a tool designed for building, so there's a lot of functionality that's missing in the tool. So, therefore, we have to use Revit with a series of plugins in order to make Revit a landscape tool.

So some of the advantages of using these plugins is it gives us the ability to do some fine terrain adjustments, model roadways and sidewalks, and then also get the graphic outputs that we need as far as deliverables. The benefit of having the entire model in Revit is it gives us a platform for coordinating with our architects and engineers.

And you can ask yourselves-- a project like this-- why didn't we do it in Civil. And the simple answer is just because Civil does not offer the tools and the workflows that work for our landscape architects. So we decided to use Revit.

So when we started, we pretty much started with an idea of how we wanted to model in the software. But we need to find out how we can use the software. And when we look at software today, software is the tools of our trades. It's the-- the tools that traditional architects have used in the past is their pens and pencils. And we see the software, as our-- the tools of our craftsmen. And we treat our architects and designers as craftsmen.

So, for this, we decided to reduce our software portfolio to Revit and Rhino, eliminate the MicroStation, and then use AutoCAD only when necessary for importing and exporting information. Then we had to align the entire workforce in the office of how to use the tools more effectively. So we developed a set of whitepapers that describe how we work, and the tools that we use, and how do we use the tools.

And this-- these whitepapers became the basis of our education, of how do we upskill, and how do we teach the landscape designers how to use Revit and Rhino in the most effective way. So, with this, we also had to explain what we wanted in the end. So we have a set of sample models that we use. And these sample models are from different levels of development and different phases so that everyone is aware of what we need to deliver, when we need to deliver it, and how detailed the models should be so we don't re-- minimize the risk of overmodeling or undermodeling and we can meet the demands of our clients.

But then, of course, we still need to deliver drawings and drawings is probably the biggest challenge because we have a lot of project architects in the office and they have their own idea of what a good drawing looks like. So we've also tried to make an office standard of the way the drawings should look after we come out of Revit. And this is also how we communicate to the craftsmen on-site many times.

So when we look at our design processes, we start with competitions. A lot of our work is won by competition. And then these particular projects go very fast. So we use Rhino as our sketching tool. And this is the tools that our designers know.

In Denmark we have two different schools of architecture. There's the design school and the technical school. The technical schools use Revit, the design schools use Rhino, and the two schools don't talk to each other. So we have to rely on the tools that they know when they come into the field.

So, with this, we also have this set of sample drawings to show what we want because it's not easy to explain in words. It's much better to explain in graphics so we can harmonize the office standards by having these office standards of what we want the drawings to look like out of a competition and the conceptual design phases. So this is-- we found to be the most effective way, especially when you're communicating with architects who think more graphically.

Then as we move through the design phase to schematic design, this is typically where the geometry of the project becomes more fixed and we make this migration from Rhino to Revit. If you are-- there's many ways you can make this transition, but we found that we have some pretty good workflows for moving geometry from Revit to Rhino for landscape. So this is also where we make the move from the design teams to our production teams.

And then, also, when we have projects at this phase, we have BIM execution plans involved-- or in place for all of our projects so we know what to deliver, when we deliver it, and we've also calculated the extra cost of some of the services we provided into our contracts.

And then when we move into final design, we're mostly using Revit. I have to say we still use Rhino for some design sketching, but it's-- Revit is our primary tool and this is what is handed over to the client. And because we're in Denmark and we have these BIM requirements, many times we hand over IFC models in addition to our Revit models, and still some of the contractors are asking for DWG exports. These DWG exports are often used for grading the site.

So when we have this Revit in model-- Revit model in and-- in our common data environment, which we use Autodesk Construction Cloud, we have the ability to do a high level of coordination between the architectural and landscape. So we know exactly how the landscape will behave when it meets the buildings. We can ensure that we have the right slopes, we have the right access, and we also have the ability to visualize the project quickly, merging both the landscape and architectural models together.

But in order to get this to work, we also need to have a good content library. And this is easier said than done. We try to develop our content from projects because that way we are ensured that the content we develop is used on projects. But this is very complex and tedious task. And like our drawings, the 3D models and the objects, they also are under scrutiny about what they look like. So we've also decided for the office of what the graphic appearance should be for the 3D objects in the models.

And one of the hot topics is what a tree should look like in 3D and what a tree should look like in 2D. And this is just one of the examples of the discussions we have.

So, recently, we've started using something from Autodesk called Content Catalog. And this is an online, cloud-based content management system, previously known as UNIFI. And we see this is a real game changer for us. Because-- since we're an international office, we can have these collections tailored to the different geographies and we're moving from where we previously stored our content on our file server to a more graphic way that's searchable and more easily used by the entire office.

So we're in the process of rolling it out. It's just come off of beta about a month ago. So we're pretty excited about this. And I think everybody in the office is excited as well to start using this.

So we need to take a look at also how we start in Revit. And when we start with our projects, we always start with a site survey. And this site survey becomes the basic foundation for the project. Because this has-- its positioned on Earth and we have the coordinate systems, so all of our projects start with some site survey.

And we use this site survey from the start and it's important that we can round trip back to the site survey in the end, especially on a site like Faelledby, where we have the 45 acres of land with the GPS-controlled machinery to grade the site. It's really important that we end up in the same place that we started.

At the same time, we also use this 3D information that's captured in the site surveys to make our initial topographies that we use for developing the site. So this site survey is the word in the office of where we start. And without the site survey, we don't know where we are in space. Of course, we can start a project without it, but we will quickly position the site in space.

And one thing we've learned is this site survey-- the survey point in Revit is the controlling point of everything for landscape. We found out that modeling the terrain from the site survey elevation and also the position in relationship to the site is critical.

So you can move the site survey close to the site by unpinning it and moving it. But we found out that it's much more easy for all of our landscape architects to understand if we leave it at the 0, 0. That means they know exactly where the origin point in the AutoCAD site surveys are and they know exactly where the site is in space.

So as I mentioned before, we need to use additional tools to model landscape. And one of the packages of-- plugins that we use and we really appreciate is Environment. And Environment, we found out, fills in many of the holes in Revit, especially for landscape. I mean, if you look at the landscape tools in Revit, there aren't that many. There's just maybe four or five. But with the-- a tool like Environment, we get an entire portfolio of tools.

And one thing about Environment that we really appreciate is it's a tool designed by landscape architects for landscape architects. So the workflows in this-- the plugin really work for the landscape architects that we have in-house. It's easy for them to understand and easy for them to use. So we really like to use a tool like this. And it's also easy to explain the workflows because it's a lot of the same terms that we use in our daily work when working in the software.

We also found out that just because we have landscape doesn't mean, we can't-- we don't need to do coordination. We do a lot of coordination for our architectural projects. And we need to do the same for our landscape projects.

The sites are getting more and more complex with underground utilities. And we want to also ensure that we can place our trees without placing them on top of the utilities. So the coordination of landscape models is just as important as the building models. And, for this, many of the time-- many of the projects use Navisworks. But because we have these IFC models that we need to deliver, we also use Solibri. So we have our rule sets all set up for landscape. So when we need to do these coordinations, we can quickly set up a Navisworks project or a Solibri model and do this coordination.

So, also, the tools that we use need to play together. So interoperability between Revit and Rhino is really important. So we have some tools in Environment that can help us out with this interoperability, but we also use other tools as well. We're looking at some cloud-based tools and also like Speckle and as well as the Data Exchange.

So we use a tool called Beam. And we see Beam is really good for single objects. So if we want to move those from Rhino to Revit-- and we have a interiors department and design departments and their primary tool is designing in Rhino. So we use Beam to push the geometry we get in Rhino to Revit as families. So it's a good way of trying to keep as many native objects in our Revit models as possible.

But when we talk about larger projects and larger amounts of data, Beam is not so good for moving entire projects, so we use tools like Speckle. And this is how we migrate our geometry back and forth between Revit and Rhino for landscape. We're looking at using the Data Exchanges for topography, but we haven't developed our plugins for that as well.

So if you're interested in seeing how you can use Data Exchange for Revit and Rhino, you can see the session that I also presented at this year's Autodesk University. So now I'll hand it back to Diana and she'll talk about some of the challenges we have in landscape.

DIANA CRISTINA BINCIU: Yes, looking back at what we did from 2D to LIM, we found out we have some challenges. So one of them is that there is a limited scale. So big data and big scale projects more than 32 kilometers we cannot-- we simply cannot work with.

While Revit has a Toposurface tool, it is relatively basic compared to the needs that the landscape architects need. It lacks the ability to handle more and more complex grading, more detailed terrain, precise contour modeling. So advanced tools for retaining walls, slope analysis, and cut and fill calculations are limited.

There are no native tools for managing or analyzing soil types, subgrades, or creating detailed planting bed designs that could reflect soil depth, for example, or drainage conditions. This type of information is quite critical for a landscape architect in order to ensure proper plant health and site sustainability. Simply, Revit does not allow for multilayered terrain models that will show different types of soils or layers beneath the surface, which is also important for planting, for the root depth, for stormwater infiltration, and hardscape foundations.

And regarding irrigation, Revit doesn't include specialized tools for designing irrigation systems, such as automatic piping layout for sprinkles-- sprinklers, sorry, drip systems, or controllers. So integrating water-efficient landscape design elements is quite difficult without third party add-ons.

And landscape architects often rely on GIS data to inform their designs. So Revit has some ability to import external data, but it lacks the integration with GIS and this-- it's making it hard to incorporate topographic surveys.

Another thing is that it's quite difficult to produce the plans and the sections that are graphically appealing to landscape designers-- the lack of comprehensive plant databases. Revit includes some basic plant families. But these are limited in variety and don't reflect the diversity of real world species. They don't really have advanced information like growth patterns, seasonal changes, or maintenance needs.

It doesn't allow for material assignments-- but it does allow for material assignments, but doesn't have a rich library or customization tools specific to landscape paving materials. And, overall, the smoothness of elements is quite limited.

Some pros and cons. We think it's quite powerful for integrating landscape with architectural designs and engineering models. It's also great for 3D modeling and BIM workflows, but I do think we need some plugins for advanced landscape features.

You might ask if it's enough to have a good workflow, enough to have a good database, or a good program, or good tools? I would say no. We need to deal with the new hot topic of today, which is AI. So I'm going to show you how we use the current AI technology in order to promote and to choose our design ideas.

We've been looking at multiple platforms for it and we came-- we selected these three that match our studio identity-- Midjourney, RunDiffusion, and Runway, which are cutting edge platforms that leverage AI to create stunning renders.

So Midjourney is known for its ability to generate detailed and artistic visuals from text prompts, making it a powerful tool for exploring landscape designs. In this case, for example, we can decide to change the sky and the atmosphere. It's also a very flexible platform to bring new ideas to life with basically a blink of an eye and have good results, could add cyclists or children. Overall, Midjourney simplifies the rendering process and it's enabling us to produce impressive visuals.

The second tool is called the RunDiffusion, which offers similar capabilities to Midjourney, but it's more focused on realistic landscape. And we use this tool to create scenarios for client meetings, for example, or for media content, or just analyzing different camera views for our submissions.

Then we go through Runway, that goes a bit further. And it's combining AI with video and 3D rendering. And its-- allows the designers to create a more dynamic and interactive landscape scene.

Together, these platforms are revolutionizing, in a way, let's say, the way landscapes are visualized and designs. It's offering both efficiency and creativity in the rendering process. So combining all of these three provides quite a powerful tool kit in the office for visual content.

These were the results. But how did we get there, you might ask? Pretty simple. We start with this level of information. We can take it from our Revit-- can take a Rivet snip, we can take an Enscape view or just any view from any model that it's quite simplified.

And then after we select the desired view, we start dissecting by using text prompts. This case, because the project is in Denmark, we might want a Danish summer sky with clouds because we have maybe 90% of the time cloudy summers. Then we do want that sustainable timber wood facade look and to just populate with some more in the background. And then when we get to the desired outcome, we can go in deep and work with landscape.

So, for example, let's say we want some landscape designs featuring lush Danish vegetation. Then we could get some different scenarios that we can talk and create upon. Or maybe we want some windy, green fields with the Danish environment and then we get different options. And, at the end, maybe we would like to explore with water. Therefore, you could see that there are multiple and it's quite a variation of what we can get. All of this was obtained in 10 to 15 minutes of work.

The pros and the cons regarding AI, I would say it saves time. Like I mentioned, it took me 15 minutes to generate some images from a Revit model. I would say it saves money because we don't have to outsource this to-- outsource it to other offices. It increases the quality. Sometimes it can elevate from what we did before. It's quite easy to rendering iterations and upscaling.

The cons. I would say the result might not be what we expect. I don't know if you've noticed, I posted this picture earlier, but underneath the car we have a rock. Hopefully it's a rock and not something more dark. But, yeah, so you could get some bugs here and there.

You also risk that everything will look the same. If all the offices in Denmark will use Danish vegetation, windy and cloudy Danish skies, I assume we will get the same vibe. So, yeah, it could risk into going into that.

And then, of course, it does save time, but it also took me a lot of time to explore and research. So you do need to think about investing in that.

Next, I will show you how we envision a bit AI in-- yeah, in the future and how it could help us with the project so complex as Faelledby. We came up with the four main points. We think that AI systems could analyze real time weather data to optimize, for example, the irrigation schedules to ensure that landscape receives just the right amount of water without wasting any.

Models that predict plant growth patterns, enabling landscapers to allocate resources efficiently based on seasons. Or we could have AI-driven soil sensors that monitor moisture level, for example, and automatically adjust the water distribution.

We could also have empowering sustainability practices. So we could have tools that are integrated with LIM that automatically generate sustainable landscape design that could-- for example, by analyzing site-specific environmental data, such as wind or sun. AI-driven LIM platforms that recommend plant species based on the local biodiversity, depending on where you are on the globe and could be promoting ecological harmony and reducing costs.

The third point is about enhancing ecological balance and reducing the carbon footprint in landscape. Maybe some AI algorithms that can calculate the carbon sequestration of different plant schemes because sometimes when you think about landscape, you might think that it's all green and natural and forget that designing landscape also produces carbon. So, yeah, this could be taken into consideration.

Then we could maybe have some AI-powered analysis of urban heat islands, guiding the placement of greenery to reduce temperatures and improve the air quality. Or some predictive models that assess the long-term ecological impact of the landscape.

So the fourth point is regarding new standards for sustainability metrics. There could be some platforms that benchmark landscape energy consumption. This would help designers minimize the energy use through strategic planning. We could use maybe AI to track and reduce the carbon footprint of landscape construction and the maintenance, setting the-- setting new industry standards regards to sustainability or systems that ensure the ecological equilibrium by optimizing the balance between built and natural environments.

I would like to close the session by raising up a question, and that would be, can we create better landscape designs with AI? Thank you. If you have more questions, maybe you reach us out on LinkedIn.

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We use Google Analytics (Strictly Necessary) to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Google Analytics (Strictly Necessary) Privacy Policy
Typepad Stats
We use Typepad Stats to collect data about your behaviour on our sites. This may include pages you’ve visited. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our platform to provide the most relevant content. This allows us to enhance your overall user experience. Typepad Stats Privacy Policy
Geo Targetly
We use Geo Targetly to direct website visitors to the most appropriate web page and/or serve tailored content based on their location. Geo Targetly uses the IP address of a website visitor to determine the approximate location of the visitor’s device. This helps ensure that the visitor views content in their (most likely) local language.Geo Targetly Privacy Policy
SpeedCurve
We use SpeedCurve to monitor and measure the performance of your website experience by measuring web page load times as well as the responsiveness of subsequent elements such as images, scripts, and text.SpeedCurve Privacy Policy
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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Improve your experience – allows us to show you what is relevant to you

Google Optimize
We use Google Optimize to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Google Optimize Privacy Policy
ClickTale
We use ClickTale to better understand where you may encounter difficulties with our sites. We use session recording to help us see how you interact with our sites, including any elements on our pages. Your Personally Identifiable Information is masked and is not collected. ClickTale Privacy Policy
OneSignal
We use OneSignal to deploy digital advertising on sites supported by OneSignal. Ads are based on both OneSignal data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that OneSignal has collected from you. We use the data that we provide to OneSignal to better customize your digital advertising experience and present you with more relevant ads. OneSignal Privacy Policy
Optimizely
We use Optimizely to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Optimizely Privacy Policy
Amplitude
We use Amplitude to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Amplitude Privacy Policy
Snowplow
We use Snowplow to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Snowplow Privacy Policy
UserVoice
We use UserVoice to collect data about your behaviour on our sites. This may include pages you’ve visited. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our platform to provide the most relevant content. This allows us to enhance your overall user experience. UserVoice Privacy Policy
Clearbit
Clearbit allows real-time data enrichment to provide a personalized and relevant experience to our customers. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID.Clearbit Privacy Policy
YouTube
YouTube is a video sharing platform which allows users to view and share embedded videos on our websites. YouTube provides viewership metrics on video performance. YouTube Privacy Policy

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Customize your advertising – permits us to offer targeted advertising to you

Adobe Analytics
We use Adobe Analytics to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Adobe Analytics Privacy Policy
Google Analytics (Web Analytics)
We use Google Analytics (Web Analytics) to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Google Analytics (Web Analytics) Privacy Policy
AdWords
We use AdWords to deploy digital advertising on sites supported by AdWords. Ads are based on both AdWords data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AdWords has collected from you. We use the data that we provide to AdWords to better customize your digital advertising experience and present you with more relevant ads. AdWords Privacy Policy
Marketo
We use Marketo to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. We may combine this data with data collected from other sources to offer you improved sales or customer service experiences, as well as more relevant content based on advanced analytics processing. Marketo Privacy Policy
Doubleclick
We use Doubleclick to deploy digital advertising on sites supported by Doubleclick. Ads are based on both Doubleclick data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Doubleclick has collected from you. We use the data that we provide to Doubleclick to better customize your digital advertising experience and present you with more relevant ads. Doubleclick Privacy Policy
HubSpot
We use HubSpot to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. HubSpot Privacy Policy
Twitter
We use Twitter to deploy digital advertising on sites supported by Twitter. Ads are based on both Twitter data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Twitter has collected from you. We use the data that we provide to Twitter to better customize your digital advertising experience and present you with more relevant ads. Twitter Privacy Policy
Facebook
We use Facebook to deploy digital advertising on sites supported by Facebook. Ads are based on both Facebook data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Facebook has collected from you. We use the data that we provide to Facebook to better customize your digital advertising experience and present you with more relevant ads. Facebook Privacy Policy
LinkedIn
We use LinkedIn to deploy digital advertising on sites supported by LinkedIn. Ads are based on both LinkedIn data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that LinkedIn has collected from you. We use the data that we provide to LinkedIn to better customize your digital advertising experience and present you with more relevant ads. LinkedIn Privacy Policy
Yahoo! Japan
We use Yahoo! Japan to deploy digital advertising on sites supported by Yahoo! Japan. Ads are based on both Yahoo! Japan data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Yahoo! Japan has collected from you. We use the data that we provide to Yahoo! Japan to better customize your digital advertising experience and present you with more relevant ads. Yahoo! Japan Privacy Policy
Naver
We use Naver to deploy digital advertising on sites supported by Naver. Ads are based on both Naver data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Naver has collected from you. We use the data that we provide to Naver to better customize your digital advertising experience and present you with more relevant ads. Naver Privacy Policy
Quantcast
We use Quantcast to deploy digital advertising on sites supported by Quantcast. Ads are based on both Quantcast data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Quantcast has collected from you. We use the data that we provide to Quantcast to better customize your digital advertising experience and present you with more relevant ads. Quantcast Privacy Policy
Call Tracking
We use Call Tracking to provide customized phone numbers for our campaigns. This gives you faster access to our agents and helps us more accurately evaluate our performance. We may collect data about your behavior on our sites based on the phone number provided. Call Tracking Privacy Policy
Wunderkind
We use Wunderkind to deploy digital advertising on sites supported by Wunderkind. Ads are based on both Wunderkind data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Wunderkind has collected from you. We use the data that we provide to Wunderkind to better customize your digital advertising experience and present you with more relevant ads. Wunderkind Privacy Policy
ADC Media
We use ADC Media to deploy digital advertising on sites supported by ADC Media. Ads are based on both ADC Media data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that ADC Media has collected from you. We use the data that we provide to ADC Media to better customize your digital advertising experience and present you with more relevant ads. ADC Media Privacy Policy
AgrantSEM
We use AgrantSEM to deploy digital advertising on sites supported by AgrantSEM. Ads are based on both AgrantSEM data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AgrantSEM has collected from you. We use the data that we provide to AgrantSEM to better customize your digital advertising experience and present you with more relevant ads. AgrantSEM Privacy Policy
Bidtellect
We use Bidtellect to deploy digital advertising on sites supported by Bidtellect. Ads are based on both Bidtellect data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bidtellect has collected from you. We use the data that we provide to Bidtellect to better customize your digital advertising experience and present you with more relevant ads. Bidtellect Privacy Policy
Bing
We use Bing to deploy digital advertising on sites supported by Bing. Ads are based on both Bing data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bing has collected from you. We use the data that we provide to Bing to better customize your digital advertising experience and present you with more relevant ads. Bing Privacy Policy
G2Crowd
We use G2Crowd to deploy digital advertising on sites supported by G2Crowd. Ads are based on both G2Crowd data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that G2Crowd has collected from you. We use the data that we provide to G2Crowd to better customize your digital advertising experience and present you with more relevant ads. G2Crowd Privacy Policy
NMPI Display
We use NMPI Display to deploy digital advertising on sites supported by NMPI Display. Ads are based on both NMPI Display data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that NMPI Display has collected from you. We use the data that we provide to NMPI Display to better customize your digital advertising experience and present you with more relevant ads. NMPI Display Privacy Policy
VK
We use VK to deploy digital advertising on sites supported by VK. Ads are based on both VK data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that VK has collected from you. We use the data that we provide to VK to better customize your digital advertising experience and present you with more relevant ads. VK Privacy Policy
Adobe Target
We use Adobe Target to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Adobe Target Privacy Policy
Google Analytics (Advertising)
We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
Trendkite
We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
Hotjar
We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
6 Sense
We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
Terminus
We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
StackAdapt
We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
The Trade Desk
We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

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