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Using Entwine LiDAR Data Sets Within Civil 3D and ReCap Pro

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说明

In this session, we’ll explore Entwine Point Tiles (40+ trillion points), and how to consume them in mere minutes inside of Civil 3D software as a surface (via point cloud). As large point cloud data sets become ubiquitous in the architecture, engineering, and construction (AEC) community, open-source libraries and software dedicated to manipulating these data sets are valuable tools for civil engineers and those in the geospatial community. Entwine is a free online cloud repository that anyone can access, and with this technical instruction, you can go from zero (knowledge) to office hero in minutes!

主要学习内容

  • Learn how to quickly find LiDAR data for your area of work or even business development.
  • Learn how to clean and generate a consumable point cloud within Civil 3d in minutes.
  • Learn how to use better informed proposals before a contract is created.
  • Gain a more refined understanding of grading constraints early in your project lifecycle.

讲师

  • Justin Ehart 的头像
    Justin Ehart
    My career journey has taken me on a wild ride through the realms of architecture, land surveying, civil engineering and now into the exciting world of data science, software development, & GIS applications. 29 years ago, I stepped into the professional arena armed with nothing but a drafting board, a roll of trace paper, and a dream of creating structures that would make even gravity raise an eyebrow. I transformed my vision into tangible masterpieces. But as time went on, I realized that my passion for problem-solving and pushing boundaries extended far beyond subdivision design. I found myself drawn to the allure of data and the hidden stories it held. So, I bid farewell to the world of scales & electric erasers and embarked on an adventure into the realm of digital transformation. Now, I'm a self-proclaimed data wrangler, taming unruly datasets and making sense of the numbers that haunt the dreams of others. Armed with algorithms and an uncanny ability to spot patterns, I lead a team that weave tales of insight and enlightenment from the tangled web of information. Who would've thought that python had a split personality as both a reptile & a coding genius? I've even caught a few pie charts plotting to overthrow bar graphs. But my quest for knowledge didn't stop there. In a bid to conquer new frontiers, I dove into the realm of software development, where we turn ideas into digital marvels. We've became virtuosos of code, composing symphonies of ones and zeros that danced on screens like a pixelated ballet. Bugs tremble at the mention of my name, for we are the ultimate bug whisperer's – their worst nightmare and greatest nemesis. As the Studio Manager of Ware Malcomb's Digital Transformation team, a jack of all trades (and master-of-none), a relentless problem-solver who thrives on out-of-the-box thinking. If you've made it this far looking for a touch of creativity, a dash of technical wizardry, and a hearty dose of humor, you've come to the right place.
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      Transcript

      JUSTIN EHART: Thanks for joining me. Today, we're going to be covering leveraging Entwine LiDAR datasets within Civil 3D and ReCap Pro. I know that's a mouthful, so if you want to go with the Too Long; Didn't Read, we're going to exploit online data for use in Civil 3D.

      Who am I? Why am I teaching you this session? Well, my name is Justin Ehart, and I work with Ware Malcomb. I'm their engineering BIM manager. And I've spent the last 28 years working in architecture, civil engineering, and land surveying.

      I've previously spoken at AU & BiLT Europe in 2019 over Drone 2 Design. And most recently, I spoke at BiLT North America this year covering Dynamo for Civil 3D. And I absolutely love coding, whether it's Python, IFC.js, .NET, PowerShell, Dynamo, LiSP. You name it, I probably have my fingers in.

      And most recently, I've created an engineering and Revit chat bot in Microsoft Teams that answers questions for users. I'm currently working on some Civil 3D TensorFlow, artificial intelligence integration. But primarily, I'm focused on geospatial and data science applications. And I'm an avid comic book collector and sneakerhead.

      So a little bit about Ware Malcomb. In 2022, we had our 50th anniversary of the firm. We are a full service architecture, interior, civil engineering, branding, sustainability, building measurement services, land surveying, and a workplace strategy and change management firm. It's a mouthful, I know. We have 28 offices across North and South America and we pride ourselves on not being like every other firm. We have our own R&D lab called WM Future.

      So what can you as a participant expect from this session? So our course overview. In this session, we'll explore Entwine Point Tiles. And currently, there's over 43 trillion with a t individual points. And we're going to figure out how we can consume them quickly inside of Civil 3D as a surface.

      As more large data point cloud sets become available or ubiquitous inside of the AEC community, we've got to find different open source libraries and software that's dedicated to manipulating the data sets so that we can harness those. And so this session, we're going to learn how we can quickly find that LiDAR data for your area of work or even a business development case.

      We're going to quickly clean and generate a consumable point cloud within Civil 3D. And we're going to learn how we can create better-informed proposals before a contract is created. And we're going to gain a more refined understanding of the grading constraints early in our project lifecycle.

      So how are we going to accomplish this? Well, we're going to geolocate and create a new drawing in Civil 3D from the map and save it. That's step one. Step two, we're going to gather. So we're going to browse to the national map and define an area of interest and download those LiDAR files. Then we're going to clean it using an open source software called Cloud Compare.

      We're going to use this to filter, subsample, and merge our tiles based on the ground point, classification 2. Then we're going to create a ReCap Pro project with an LAS file and convert it to ReCap. And then finally, we're going to round robin. We're going to go back into Civil 3D, import that ReCap file, and create a service from the point cloud.

      You might be asking yourself, great, but what is Entwine? Well, it's a data organization library for these massive point clouds. They're designed to handle data sets of trillions of points. That data that's stored there is completely lossless, even in terabyte-scale datasets. So what that means, there's no point loss. There's no metadata loss. And there's no precision loss. And even better yet, Amazon has agreed to pay to convert the files to EPT and to store the data on their AWS S3 servers.

      So step one, geolocate. So we're going to geolocate a new drawing. You can save it as a drawing template or a DWG. You can use the command EDITDRAWINGSETTINGS on your command line. Or you can browse to the tool space settings, right click your drawing name, and edit the drawing settings.

      So what I like to do is set it to the correct coordinate system that I'm working in. I usually do a lot of work in state plane coordinates, so a NAD83 in the US. You might be using UTM elsewhere. And if you have a boundary, you can use that to import your partial shape files. So let's kind of see how that's done.

      So right here, I've got just a blank brand new drawing. You can see that the coordinates down here are 0, 0. And so this is where I talked about you can go into tool space and right click, Edit Drawing, and browse to your coordinate system. And here, I'm going to set it for New Jersey. NAD83, New Jersey State Planes, US Foot.

      I like to script everything, so I'm going to just go to my trusty tool palette. I'm going to click go to Elizabeth, New Jersey. It turns on my map, zooms me to that area. So I'm going to scroll down here. The site we're going to explore today is on the border of Delaware, New Jersey, and Pennsylvania, just to kind of see how much data is available to us.

      And this site looks good. We're going to trim the map. And then we're going to turn off the background map, so that it doesn't try to recalibrate as we zoom in and out on the site. And now I'm going to go ahead and import-- oh, I can show you that the drawing settings were set correct here. Great.

      Next, let's import our boundary. Again, I'm going to use a shapefile, because I'm going to trim my surface to this later. And we can see that the coordinate system of my drawing is New Jersey, but the input of the shapefile I'm bringing in is lat, long, 84. So I'm going to assign my drawing layer. I'm going to add my object data. Import polygons as closed polygons. And there's my boundary.

      So if we go over to the Properties, we're going to see that it is set to lat and long. And I'm always a little clunky. I always have to find it. Every GIS information is a little bit different. So we can see that it's lat and long. And the acreage of this site is 462.16 acres.

      Great. So now that we've geolocated our drawing, we can go ahead and gather our LiDAR data. And so what we're going to do is browse to the national map. So apps.nationalmap.gov. And then if we scroll down, we're going to see LiDAR Explorer. Once we go into the website, we're going to define an area of interest and download those LAZ files. Let's look at that now.

      So here's my drawing of this Delaware River Project in New Jersey. And if we go to the usgs.entwine.io website, we can see this is where we have coverage of LiDAR data. And as of August 4, 2022, there are 43 trillion points of data on there.

      Here is the apps.nationalmap.gov. It's the 3DEP, so 3D Elevation Point LiDAR Explorer. And if we click here where it says show where LiDAR data is available, it's going to mimic that JSON file from the other website. And so we can see this is where we have coverage.

      So I like to work with a satellite image. So I'm changing the base map right now. I'm going to zoom in to the area. So again, this is where Delaware, Pennsylvania, and New Jersey border along the Delaware River. So that looks like our site from Civil 3D.

      Let's go ahead and define our area of interest. You can do that by holding down the Control key and dragging your cursor or clicking this draw area of interest icon. I like to use the icon, because then it starts aggregating the data for me automatically.

      And while it's thinking, you can see the progress bar across the top. So the downloadable products within our area of interest, let's zoom out a little bit. You can see the different LiDAR projects that are available. And some of the dates on these, 2006, 2015, 2013, '07. A little out of date.

      So we're going to try to get the most recent LiDAR data that we can. And so by clicking the LiDAR with an area of interest, it will aggregate that information for us. We can use the filter at the top, if we wanted to make sure we get New Jersey. Or we can just start scrolling down and seeing what the most current is for this area.

      Down at the bottom, we're going to get there now. We'll see New Jersey and we'll see five different tiles. We're going to go ahead and download all of those. It's a quick download. Some of these .laz files have 15 million points in them. You can see how quick they download.

      Great. I think those are downloaded. And so you can zoom out and see the area that we're working in. So again, Pennsylvania, New Jersey, and Delaware. Awesome. So next, we're going to clean the data.

      So we are able to clean the data in Civil 3D. But we kind of have to round robin. So what I like to do is there's this open source software called Cloud Compare. And we're going to browse to this. And I'll show you in a moment. You can just search on Google for it.

      And once installed, we're going to open and browse to our downloaded files. And we're going to filter by a value. And then we're going to do a subsample and a merge and then save it as an uncompressed LAS file. So let's go ahead and click on this.

      So here I am on just your favorite search engine. You can just type Cloud Compare. And we're going to go to the results. And we'll see this one here, Cloud Compare. It's an open source project. On the Download tab, you can go here and select the download for your operating system.

      And it is freeware. So if you are able to donate, it might be in your best interest. You're going to use the software a lot. We've launched the software here. And so I'm going to go to File and Open. And I'm going to browse to my LAZ files. Those are the compressed files that we downloaded.

      And we're going to toggle off all of these check boxes here on the standard fields, with the exception of classification. And we're going to go over classification in just a moment. And since we have multiple tiles, we're going to say apply all.

      So this first tile we're importing, there's 24 million points in it. That's a large area of work that has a lot of detail. This is the longest portion of this. So it's going to take just a few moments for us to aggregate and bring those five tiles in.

      And so this software gives us the ability to merge all of these tiles into one. It gives us the ability to filter them by the classification. And all LiDAR information does have a classification. So classification 2 is bare ground. It's the common ground shots.

      If you were wanting just the buildings on the site, you could use seven. Those are classified for buildings. And here in a moment, we're going to go to a website that shows all the different classifications of the files. So we'll just let it work and do its thing here.

      We're almost done, down to the last one. Great. So what we're going to do is we're going to go over and select. And using the Control button, select each of the point clouds individually. You can see them highlight on screen.

      Then we're going to go to Edit. And then we're going to apply a scalar field filter by value. And again, 2 is the common ground shots. So that's what we're going to filter by. And we're going to see all of the tree canopies and everything go away here. Those are automatically highlighted.

      So let's go ahead and do a subsample. We don't necessarily need 100 million points of this point cloud. So we're going to do a minimum space in there. And so we're going to take it down from about 100 million points to roughly 10 million points.

      All right. This is the last one. Now, we could export these five individually. But while we're here, we might as well merge them together. So they're all highlighted again, because we just made an edit. And we'll just go down to merge.

      And we can see the five point files in there. Honestly, that looks better. 5.3 million points in this area. We can handle that very well in Civil 3D. And if we click, we can see that that one is 10. We see just the different values that are there. So let's go ahead and save this.

      And saving the file format as a .las. So it's an uncompressed file of what we downloaded from the web. We're just going to click OK, set the original resolution. And then ultimately, we're saving a point file that has about 10 million points.

      And while you're in here, you're able to explore around and view some of the features on it. Just quickly orbit, just like you can in Civil 3D. And then here, we're going to show the lighter classification. So you'll see that 2 is ground, 3 is low vegetation.

      And they've kind of changed some of the classification flags. They've updated it to allow for some more. But 2 is still going to be ground. That's why we filtered by classification 2. We'll see in here that there's bridge decks, road surfaces, water. But yeah, any LiDAR formatted point cloud should be classified to make it easier on you.

      Awesome. So now we're going to convert our data. We're going to take that LAS file that we saved and we're going to convert that to a ReCap Pro file format so that we can bring it into Civil 3D. And while in ReCap, we might as well do a little cleanup. So we're going to clean up that area outside of our area of interest and save and optimize.

      And then I also like to do a double check. I kind of like to export an additional ReCap file called Cleaned or Cropped so that if I need more data, I can go back to the original. There we go. So what we're going to do in ReCap Pro is we're going to launch a new project and import a point cloud.

      And give it a project name, if that's how your company works. Or if you're just doing this as an exploratory or BD tool, you might not have a project name yet. So we're going to go to-- I saved it in my Downloads folder. And then we're going to import files.

      ReCap is very simple to work with. And then this is the longest part, where it's indexing the scans. It could take 30 seconds. But some of the nice features of ReCap Pro is you're able to define areas that you want to include only or exclude. You can use a fence, a window. There's a lot of neat features in here. So if you're not familiar with it, you're going to get familiar with it fast using this workflow.

      I feel like we're going to get close on this. There we go. It's starting to do something now. Great. Now we can click Launch Project. And we're going to see that point cloud that we had merged and cleaned in Cloud Compare appear on our screen.

      I like to work top down. So we're going to go ahead and change this to a fence. This is essentially the lasso command. And we're just going to draw around this. What I'd like to do is I'd like to clip this inside area. So I'm left with this outer area that isn't relevant to my workflow on this particular site, so I'm go in here and delete it.

      And the functionality of ReCap is a little different from Civil 3D. I always have to find my bearings on it. So we're going to unclip the inside. I'm going to save it and optimize the point cloud. And as I mentioned, I do like to export, just as a double check sometimes, or if I want just the reassurance that I have everything that I need.

      So we'll just browse. I've got a ReCap folder that I like to save those items in. And again, this is where you could label it as your project tracking number, if your company uses one, or whatever that you need. And then we'll go ahead and unify your project scans. And this goes pretty quick. Even though it's under 10 million points, it still works well.

      Come on, baby. All right. We can close out the software. And to the last part, creating our surface. So we're going to, like I said, round robin right back into Civil 3D. And then on the ribbon, we're going to go to the Insert tab and Attach Point Cloud.

      Then once we have that attached, we'll be able to see the outline of it, and actually see it when we're in 3D view. Then on the Home tab, we're going to go to the ribbon and surfaces. And we're going to create a surface from the point cloud. And then after that, I'd like to generate a snapshot of the surface and delete the point cloud.

      So why don't we go in there and see how it's done? So here's our drawing. And on the Insert ribbon to the Point Cloud, Attach. We'll bring that over. And we'll browse to that ReCap file. Great. We see it come right in.

      And again, we are working in a coordinate system. This was all done in lat and long. It's really nice that the software has it just marry it perfectly. So we're going to go in here. We're going to name our surface. And I typically just use EG for Existing Ground. But I'm really particular on the description.

      I want to make sure that, should somebody go in there, they realize that this wasn't generated by a surveyor. The LiDAR information that we're putting in here is good to 0.24 meters or 0.8 feet. So it's sub 1 foot. But you still don't want to design from it.

      So I like to put in here when it was downloaded, where it was downloaded from, and that it should be used for preliminary design only, not for final design. So you cannot overcommunicate when you're working inside of design software. That's exactly my feelings on it. So I'll put in here preliminary design only.

      I'm going to change the style of my contours. Now I'm going to click Next. I've already done a selection on it. And then I changed it, because I don't want it to interpolate. I want it to just take the ground shots that I had given it.

      And so we can see down here that it's going to create the point cloud from the surface. And this process takes maybe 20 seconds. We'll start seeing it catch up, let's say. It'll start aggregating that information for us.

      And each point cloud is a little bit different. The bigger they are, the longer it takes. So we can see that now, to adding points to the surface. Three, numbering the points in the triangles. We're getting close. Computing contours. And we should have our surface now. Displaying the contours. Great.

      So what I like to do is go into the surface itself. And I want to set a boundary. And that's why we imported that shapefile in the first video-- in the first process of this. And so I'm just setting the boundaries from a shapefile. Super simple. I'm going to zoom in here. Or maybe I'll just cowboy it and select it from afar.

      And this shapefile, again, is 462 acres. But along the river, it's very jagged. There's a lot of different vertices that it's following, when it was established, this parcel. And I promise my Civil 3D won't crash. It's just really struggling to think.

      I could have picked a different site that was more inland with a little cleaner boundary. But that wouldn't be fair to you. And so we should be pretty close to it. There we go. And so if we were to delete the point cloud now, the surface would go away. So what we need to do is go back into the surface and create a snapshot.

      Again, I'm working with a very temperamental parcel as a shapefile. Again, I should have probably picked something a little less-- with fewer vertices. But it just wouldn't be fair. We can't pick and choose our sites. That's our developer's role.

      Now that we've created our snapshot, we can go in here to our references and we can detach or unload our point cloud. I'm going to switch it back to a 2D wireframe. And since I've already done that snapshot, I'm going to go ahead and just delete it anyway.

      So there we go. In just a few moments, we were able to aggregate online information, generate a very accurate and clean surface from online data. So we've taken all these different sources of information, we've made this homogeneous surface that works well in a PD environment or Preliminary Design environment, while we're waiting on the surveyor to get us topography.

      So we'll just look at the object view of the surface. I do tend to extract the minor and major contours and then do a quick select to remove any of the little bit of noise in there. Some of those points could be two feet. But we know that you could kick the mound of rock and it would be lower.

      So like I said, I like to extract some of the major and minor contours. Do a quick select. And anything less than, say, 100 feet I'll delete. Then there you have it. It's just that simple. So very quickly, we were able to generate that surface. Thank you for taking time to watch Leveraging Entwine LiDAR Datasets Within Civil 3D and ReCap Pro.

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      我们通过 Salesforce Live Agent 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Salesforce Live Agent 隐私政策
      Wistia
      我们通过 Wistia 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Wistia 隐私政策
      Tealium
      我们通过 Tealium 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Tealium 隐私政策
      Upsellit
      我们通过 Upsellit 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Upsellit 隐私政策
      CJ Affiliates
      我们通过 CJ Affiliates 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. CJ Affiliates 隐私政策
      Commission Factory
      我们通过 Commission Factory 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Commission Factory 隐私政策
      Google Analytics (Strictly Necessary)
      我们通过 Google Analytics (Strictly Necessary) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Strictly Necessary) 隐私政策
      Typepad Stats
      我们通过 Typepad Stats 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Typepad Stats 隐私政策
      Geo Targetly
      我们使用 Geo Targetly 将网站访问者引导至最合适的网页并/或根据他们的位置提供量身定制的内容。 Geo Targetly 使用网站访问者的 IP 地址确定访问者设备的大致位置。 这有助于确保访问者以其(最有可能的)本地语言浏览内容。Geo Targetly 隐私政策
      SpeedCurve
      我们使用 SpeedCurve 来监控和衡量您的网站体验的性能,具体因素为网页加载时间以及后续元素(如图像、脚本和文本)的响应能力。SpeedCurve 隐私政策
      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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      改善您的体验 – 使我们能够为您展示与您相关的内容

      Google Optimize
      我们通过 Google Optimize 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Google Optimize 隐私政策
      ClickTale
      我们通过 ClickTale 更好地了解您可能会在站点的哪些方面遇到困难。我们通过会话记录来帮助了解您与站点的交互方式,包括页面上的各种元素。将隐藏可能会识别个人身份的信息,而不会收集此信息。. ClickTale 隐私政策
      OneSignal
      我们通过 OneSignal 在 OneSignal 提供支持的站点上投放数字广告。根据 OneSignal 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 OneSignal 收集的与您相关的数据相整合。我们利用发送给 OneSignal 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. OneSignal 隐私政策
      Optimizely
      我们通过 Optimizely 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Optimizely 隐私政策
      Amplitude
      我们通过 Amplitude 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Amplitude 隐私政策
      Snowplow
      我们通过 Snowplow 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Snowplow 隐私政策
      UserVoice
      我们通过 UserVoice 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. UserVoice 隐私政策
      Clearbit
      Clearbit 允许实时数据扩充,为客户提供个性化且相关的体验。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。Clearbit 隐私政策
      YouTube
      YouTube 是一个视频共享平台,允许用户在我们的网站上查看和共享嵌入视频。YouTube 提供关于视频性能的观看指标。 YouTube 隐私政策

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      定制您的广告 – 允许我们为您提供针对性的广告

      Adobe Analytics
      我们通过 Adobe Analytics 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Adobe Analytics 隐私政策
      Google Analytics (Web Analytics)
      我们通过 Google Analytics (Web Analytics) 收集与您在我们站点中的活动相关的数据。这可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。我们使用此数据来衡量我们站点的性能并评估联机体验的难易程度,以便我们改进相关功能。此外,我们还将使用高级分析方法来优化电子邮件体验、客户支持体验和销售体验。. Google Analytics (Web Analytics) 隐私政策
      AdWords
      我们通过 AdWords 在 AdWords 提供支持的站点上投放数字广告。根据 AdWords 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AdWords 收集的与您相关的数据相整合。我们利用发送给 AdWords 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AdWords 隐私政策
      Marketo
      我们通过 Marketo 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。我们可能会将此数据与从其他信息源收集的数据相整合,以根据高级分析处理方法向您提供改进的销售体验或客户服务体验以及更相关的内容。. Marketo 隐私政策
      Doubleclick
      我们通过 Doubleclick 在 Doubleclick 提供支持的站点上投放数字广告。根据 Doubleclick 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Doubleclick 收集的与您相关的数据相整合。我们利用发送给 Doubleclick 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Doubleclick 隐私政策
      HubSpot
      我们通过 HubSpot 更及时地向您发送相关电子邮件内容。为此,我们收集与以下各项相关的数据:您的网络活动,您对我们所发送电子邮件的响应。收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、电子邮件打开率、单击的链接等。. HubSpot 隐私政策
      Twitter
      我们通过 Twitter 在 Twitter 提供支持的站点上投放数字广告。根据 Twitter 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Twitter 收集的与您相关的数据相整合。我们利用发送给 Twitter 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Twitter 隐私政策
      Facebook
      我们通过 Facebook 在 Facebook 提供支持的站点上投放数字广告。根据 Facebook 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Facebook 收集的与您相关的数据相整合。我们利用发送给 Facebook 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Facebook 隐私政策
      LinkedIn
      我们通过 LinkedIn 在 LinkedIn 提供支持的站点上投放数字广告。根据 LinkedIn 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 LinkedIn 收集的与您相关的数据相整合。我们利用发送给 LinkedIn 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. LinkedIn 隐私政策
      Yahoo! Japan
      我们通过 Yahoo! Japan 在 Yahoo! Japan 提供支持的站点上投放数字广告。根据 Yahoo! Japan 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Yahoo! Japan 收集的与您相关的数据相整合。我们利用发送给 Yahoo! Japan 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Yahoo! Japan 隐私政策
      Naver
      我们通过 Naver 在 Naver 提供支持的站点上投放数字广告。根据 Naver 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Naver 收集的与您相关的数据相整合。我们利用发送给 Naver 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Naver 隐私政策
      Quantcast
      我们通过 Quantcast 在 Quantcast 提供支持的站点上投放数字广告。根据 Quantcast 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Quantcast 收集的与您相关的数据相整合。我们利用发送给 Quantcast 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Quantcast 隐私政策
      Call Tracking
      我们通过 Call Tracking 为推广活动提供专属的电话号码。从而,使您可以更快地联系我们的支持人员并帮助我们更精确地评估我们的表现。我们可能会通过提供的电话号码收集与您在站点中的活动相关的数据。. Call Tracking 隐私政策
      Wunderkind
      我们通过 Wunderkind 在 Wunderkind 提供支持的站点上投放数字广告。根据 Wunderkind 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Wunderkind 收集的与您相关的数据相整合。我们利用发送给 Wunderkind 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Wunderkind 隐私政策
      ADC Media
      我们通过 ADC Media 在 ADC Media 提供支持的站点上投放数字广告。根据 ADC Media 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 ADC Media 收集的与您相关的数据相整合。我们利用发送给 ADC Media 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. ADC Media 隐私政策
      AgrantSEM
      我们通过 AgrantSEM 在 AgrantSEM 提供支持的站点上投放数字广告。根据 AgrantSEM 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 AgrantSEM 收集的与您相关的数据相整合。我们利用发送给 AgrantSEM 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. AgrantSEM 隐私政策
      Bidtellect
      我们通过 Bidtellect 在 Bidtellect 提供支持的站点上投放数字广告。根据 Bidtellect 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bidtellect 收集的与您相关的数据相整合。我们利用发送给 Bidtellect 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bidtellect 隐私政策
      Bing
      我们通过 Bing 在 Bing 提供支持的站点上投放数字广告。根据 Bing 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Bing 收集的与您相关的数据相整合。我们利用发送给 Bing 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Bing 隐私政策
      G2Crowd
      我们通过 G2Crowd 在 G2Crowd 提供支持的站点上投放数字广告。根据 G2Crowd 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 G2Crowd 收集的与您相关的数据相整合。我们利用发送给 G2Crowd 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. G2Crowd 隐私政策
      NMPI Display
      我们通过 NMPI Display 在 NMPI Display 提供支持的站点上投放数字广告。根据 NMPI Display 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 NMPI Display 收集的与您相关的数据相整合。我们利用发送给 NMPI Display 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. NMPI Display 隐私政策
      VK
      我们通过 VK 在 VK 提供支持的站点上投放数字广告。根据 VK 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 VK 收集的与您相关的数据相整合。我们利用发送给 VK 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. VK 隐私政策
      Adobe Target
      我们通过 Adobe Target 测试站点上的新功能并自定义您对这些功能的体验。为此,我们将收集与您在站点中的活动相关的数据。此数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID、您的 Autodesk ID 等。根据功能测试,您可能会体验不同版本的站点;或者,根据访问者属性,您可能会查看个性化内容。. Adobe Target 隐私政策
      Google Analytics (Advertising)
      我们通过 Google Analytics (Advertising) 在 Google Analytics (Advertising) 提供支持的站点上投放数字广告。根据 Google Analytics (Advertising) 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Google Analytics (Advertising) 收集的与您相关的数据相整合。我们利用发送给 Google Analytics (Advertising) 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Google Analytics (Advertising) 隐私政策
      Trendkite
      我们通过 Trendkite 在 Trendkite 提供支持的站点上投放数字广告。根据 Trendkite 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Trendkite 收集的与您相关的数据相整合。我们利用发送给 Trendkite 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Trendkite 隐私政策
      Hotjar
      我们通过 Hotjar 在 Hotjar 提供支持的站点上投放数字广告。根据 Hotjar 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Hotjar 收集的与您相关的数据相整合。我们利用发送给 Hotjar 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Hotjar 隐私政策
      6 Sense
      我们通过 6 Sense 在 6 Sense 提供支持的站点上投放数字广告。根据 6 Sense 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 6 Sense 收集的与您相关的数据相整合。我们利用发送给 6 Sense 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. 6 Sense 隐私政策
      Terminus
      我们通过 Terminus 在 Terminus 提供支持的站点上投放数字广告。根据 Terminus 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 Terminus 收集的与您相关的数据相整合。我们利用发送给 Terminus 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. Terminus 隐私政策
      StackAdapt
      我们通过 StackAdapt 在 StackAdapt 提供支持的站点上投放数字广告。根据 StackAdapt 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 StackAdapt 收集的与您相关的数据相整合。我们利用发送给 StackAdapt 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. StackAdapt 隐私政策
      The Trade Desk
      我们通过 The Trade Desk 在 The Trade Desk 提供支持的站点上投放数字广告。根据 The Trade Desk 数据以及我们收集的与您在站点中的活动相关的数据,有针对性地提供广告。我们收集的数据可能包含您访问的页面、您启动的试用版、您播放的视频、您购买的东西、您的 IP 地址或设备 ID。可能会将此信息与 The Trade Desk 收集的与您相关的数据相整合。我们利用发送给 The Trade Desk 的数据为您提供更具个性化的数字广告体验并向您展现相关性更强的广告。. The Trade Desk 隐私政策
      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

      是否确定要简化联机体验?

      我们希望您能够从我们这里获得良好体验。对于上一屏幕中的类别,如果选择“是”,我们将收集并使用您的数据以自定义您的体验并为您构建更好的应用程序。您可以访问我们的“隐私声明”,根据需要更改您的设置。

      个性化您的体验,选择由您来做。

      我们重视隐私权。我们收集的数据可以帮助我们了解您对我们产品的使用情况、您可能感兴趣的信息以及我们可以在哪些方面做出改善以使您与 Autodesk 的沟通更为顺畅。

      我们是否可以收集并使用您的数据,从而为您打造个性化的体验?

      通过管理您在此站点的隐私设置来了解个性化体验的好处,或访问我们的隐私声明详细了解您的可用选项。