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Refining FEMA Flood-Risk Maps Using Appropriate Technology: A Case Study of Brookings, South Dakota

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说明

In updating the Master Drainage Plan for Brookings, South Dakota, ISG faced challenges reconciling flood risk maps with community experiences. Despite a doubling of the flood-risk area, doubts arose due to discrepancies with historical records and incomplete evaluations of existing flood-reduction efforts. Using InfoWorks ICM software, ISG employed advanced modeling techniques to generate a more accurate flood map, enabling prioritization of mitigation projects. Stakeholder engagement ensured realistic modeling, leading to actionable results and strategies for water quality improvement. This approach underscores the importance of informed modeling in building resilient communities amid climate change uncertainties.

主要学习内容

  • Learn about how InfoWorks ICM software's advanced modeling capabilities can provide an advantage to master drainage plan studies.
  • Learn about engaging FEMA and communities early to foster active involvement and communication, addressing climate change uncertainty.
  • Learn how to use data tracking, visual flood mapping, and enhanced efficiency when modeling flood scenarios.

讲师

  • Jacob Rischmiller
    Jacob holds a Bachelor of Science in Civil Engineering from Minnesota State University, Mankato, and has been with ISG, a nationally recognized architecture, engineering, environmental, and planning firm since 2015. As the Water Resources Practice Group Leader, he brings extensive expertise in watershed modeling, planning, and policy discussions. Guiding a team of skilled engineers, he fosters an environment of critical thinking and project rationale, resulting in thoughtful and tailored solutions to public and private clients. Specializing in surface water design and implementation, he identifies opportunities to maximize water quality and ecological diversity to mitigate impact on natural resources. Jacob's proficiency in hydraulic and hydrology modeling, using Autodesk InfoWorks ICM/XPSWMM software, is a key component of his work, encompassing projects from stormwater mitigation to wetland restoration. He will present ISG's advanced InfoWorks ICM modeling and discuss how his team reconciled flood risk maps with community experiences to create a Master Drainage Plan for Brookings, South Dakota.
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      Transcript

      JACOB RISCHMILLER: Welcome to my presentation today. I'll be talking about refining the FEMA flood-risk maps using the appropriate technology. I want to first acknowledge and thank the entire project team for this effort, especially the city of Brookings and their staff that contributed to this product as well as their master drainage plan. I am Jacob Rischmiller. I lead our water resource group here at ISG. I've been in the industry for nine years and worked over 300 projects, all from a planning stage, concept level, all the way through design and implementation within those projects.

      ISG is a full-service firm based out of the Midwest with clients that are nationwide. We have 14 offices in total with 12 business units that break up the company. We do everything in the A&E world except for the geotechnical services and specialty services as well. Within the water resource world, I primarily interact with our water business unit, public works business unit, as well as the sports and rec business unit. ISG, like I said, is a multi-service firm. In the water resource, we use Infoworks ICM on a daily basis.

      So we have 12 total licenses to an additional two XPSWMM licenses that utilize this modeling capabilities in both the rural setting on the left hand side here, left hand screen, as well as the urban setting on the right hand side. The rural setting and modeling, we really focus on the inundation times and how long that flooding takes on the surface within the Midwest, especially the upper Midwest, to where crop production is very prominent and that duration of inundation is a key factor to how much yields all of the crop products and crop production will handle.

      On the other hand, we do dabble and do a lot of work in the urban setting as well. As you can see on the right, here is a screenshot of an urban model, that I'll be talking about a little bit later today, on all the buildings that are interacting with the flooding events that are happening, as well as the main ditches and culverts and how does that all interact, all in one view, to get a full picture and holistic approach on how we model. I do want to point out I do have another presentation with Mel Meng on climate resiliency and urban sustainability. So if you've got additional time, please go check that out.

      Today my objective is to show these three lessons learned throughout my presentation. The first lesson is how ICM advanced modeling capabilities can provide advantages to massive drainage plans and massive studies. The second lesson is how to engage FEMA in the community to foster an active dialogue between both the modeling as well as the results of that modeling. And lastly, the third objective here is how visuals and data tracking can really enhance the efficiency within these models and how we really develop the models as we progress through the modeling.

      First, I would to set the stage of where in the world we are. Brookings is located in the Midwest, as outlined in the black, and the yellow is the city of Brookings' approximate location. It is 20 miles to the west of the Minnesota South Dakota border. The city has a population of 23,000 people and is home of the largest secondary school and University of South Dakota State University. The city is located in a unique position that's surrounded by two creeks, Six Mile Creek to the northwest, Deer Creek to the east, as well as the Big Sioux River.

      So flooding is a critical aspect to understand for the city, and how that flooding will impact their city storm sewer, as well as the residents and flood insurance. So this project really started as a flood map update. On the left here is the original flood maps for FEMA, FEMA flood maps and insurance maps, that the city and residents would have to obtain by. The proposed map on the right is the updated proposed draft map, with the yellow and blue really encroaching in the city limits, and showed additional flooding and additional insurance requirements gonna be met by all of the residents.

      So that's where this project really started from and adapted into a master drainage plan study to really understand the entire city scope and how does the city interact with both rivers and creeks as well as flooding locally within the city limits. So you may be asking, why is there a big difference between the original map, back in the FEMA maps, and the proposed FEMA maps? Well, really, it has to do with the modeling differences.

      The original maps were developed based on a one-dimensional steady-state model. That is really just taking the channel and cross-sections of the creek and the rivers, and sending flow down it and developing a flow regime and flood extents from there, versus the updated FEMA maps are actually taking the LiDAR data and topography data that is current as of the modeling that was completed.

      So this is a good example here is on the left is more of the results that you would see out of the original HEC-RAS model versus the right is really utilizing that topo, that LiDAR data, to convey the flow down the channel as well as any backwater channels, or conveyance areas, that would be occurring based on the rain event here.

      So the city saw those big differences, like I showed earlier, and really wanted to make sure that flood extents was correct. They invested their own money based on their stormwater fees and distribution, that I'll go into later, to develop a full, in-depth, one-dimensional and two-dimensional model for the entire city. Here are the results. The one on the left, the images on the left here, that I'll be going through a couple of different models, are the draft FEMA maps versus the image on the right is actually the ICM map.

      As you can see, there's a pretty substantial difference between where the flooding was occurring, how much flooding had happened, as well as that encountered how much insurance would actually have to be within the city, and updated flood insurance for residents within the city. So really that conveyance of the stormwater infrastructure on the ICM model of the 1D, 2D model really made that difference from a 2D only model to a fully one-dimensional and two-dimensional model, and where that water is going first versus just spilling over land.

      This next example here is a unique one to where it's a strip mall and a bigger building area on the left hand side of both of these maps to where the flooding that was occurring would have encompassed an entire building, or a couple buildings here, versus the technical and actual data in our ICM map show the flooding around the building and not encompassing the entire building. Because that is not actually representing what is truly happening out there with the HEC-RAS model.

      And the last one that I want to present here is really of that university campus and how much difference is the modeling actually showed within the regular parking lot of the city or of the infrastructure. So understanding how does that stormwater drain, how does that function, and how quickly that functions is a pretty key aspect to understand, especially the student and the student population that would increase, where that goes through and how does that interact with each other.

      If this modeling effort wasn't obtained or the city didn't invest their resources into it, there'd have been a lot more residents having to purchase flood insurance for their own properties, which would have increased costs for residents within the entire city. So like I said, the original plan was that just the FEMA stuff, just looking at their FEMA map updates. But the city saw a bigger perspective and opportunity to look at their entire master drainage plan within the entire city limits and show how can we utilize this 1D, 2D model to the best of its ability.

      Part of that plan was to develop and reduce flooding in key areas and strategic areas for the entire city. So that was part of our goal. Another goal, sub goal, was to enhance the water quality and recreation and aesthetics within the city limits. Those were some of those key concept plans that we developed. As well as within developing those, really focused on updating their modeling platform that was done in 2008 via EPA SWMM model and conditioned our new model to rain events that have actually been occurring.

      Part of our recommendations at the end of this entire plan was recommended concepts for each of these projects that we looked at, revising the city's stormwater fees. I'll get into more details towards the end of the presentation about what their stormwater fees entailed, as well as developing new standards and inspection protocols for all of the stormwater ponds and infrastructure throughout the entire city.

      As you can see on the right is the precipitation trends in Brookings, South Dakota. In the 1900s, we're about 17 inches of rain versus now in 2020, we're above 25 inches. So an increase of over 25% in the last 100, 120 years have been occurring. So flooding is being more and more critical with climate change and with other resources changing and adapting. The city saw this and wanted to invest their money and to understand what can they do within the city limits, and what projects can they do, and develop a capital improvement plan for reducing flooding for their residents.

      First, I want to take you through some of the modeling updates and how we develop the new model converting EPA SWMM into ICM and how that function, some of the data analytics behind it as well, and really show you the complexity of these models and how much data is truly in the model itself. So first, the EPA SWMM model here, as you can see, it's laid out pretty simplistic. You have catchments laying out throughout the watershed. You do have some pipe networks, and some routing of these pipe networks, and some very small, little storage basins and culverts to utilize that infrastructure.

      But that's about all of the EPA SWMM model. There is parameters in each of these areas to calibrate rainfall and conveyance of the system, but it doesn't take into account that LiDAR and 2D surface just like ICM would. So here's that same area of the EPA model into InfoWorks ICM here. So as you can tell, there's a lot more layers, a lot more conditions within this modeling software then that basic EPA SWMM model is.

      Luckily for us, each of these layers can be turned on and off. And we can create more themes to really validate and compare different models, different results, making sure we have all of those data to accurate as the best we can. In developing those, we developed some standards working with the city of how do we calibrate, how much time do we put in investing into spatial data referencing, and how does that all work? Impervious areas was a key aspect within the entire city. I'll go into more detail about the impervious areas and land use in the future in a couple slides here.

      The subwatersheds were delineated throughout the entire city. These are your basic delineations based on just the elevation data that's available and what's going to what storm sewer network. Land use is also being developed or was utilized. The building footprints were all utilized as well. As you can see it pointed out here, that is all identified and carved out using spatial data. The storm sewer network was utilized in our modeling.

      Throughout the model, there was some manipulation and assumptions made as the city did not have all of their stormwater networks laid out appropriately. And then we used some concept designs and level zones, mesh level zones, within the modeling software to really show future scenarios, future plans of what can be done to help out flooding and reduce flooding throughout the entire watershed. So like I mentioned, their storm sewer network was decently or pretty well developed, but didn't have all of the information.

      So the blue here on the chart is their missing data versus the orange is the available data that they had available, or they had with already in their GIS system. So really that missing data, we work with the city, hand-in-hand, to either go and find that data and actually go take shots, understand what's happening on it, or made some assumptions on pipe condition or the year and based on other values and other networks around that area.

      So it was a hand-in-hand and then wrote a memo on what links and what nodes were assumed and what is not assumed, so that we can really enhance that in the future. And when the city does inspections, they can go off and check off and make sure that the data that was used for our modeling was assumed for our modeling or is the actual data out there.

      The Autodesk team and engineers that we work with to vet some of this stuff really played a key factor in this, as they have wrote scripts for ICM to really showcase what areas you're missing data, what areas are offset. Some assumptions can be made throughout that scripting process to fill in missing parts of the data as well as using data flags within the ICM software.

      So we understand now, and in the future when we have to go back and we look at it, that this data link or this pipe was assumed to be an 18 inch or assumed to be a 6 inch or whatever size that might be, versus seeing the data and not having any of that context when it's passed on three or four different engineers. Another key aspect, like I mentioned earlier, is the LiDAR data and that land use data that we developed or that we utilized.

      An example here is the city invested back in 2019, a very heavy amount into a full 8 centimeters vertical accuracy LiDAR data for the entire city aspect. As you can see, it captures all of the runways, walking paths, all the trees and shadows of the trees, so it's very detailed and very thorough throughout the entire city. That really utilized in two aspects, one, for this modeling procedure, and two, impact their stormwater fees. So the process of their stormwater fees I'll talk about later.

      But utilizing this data compared to the publicly available land use was another key aspect of enhancing the modeling capabilities. So the right is more of that publicly available data on land use, as you can see on the left is that LiDAR, really defined and precise data. Here it's more of an assumed on highly intense versus the low intensity and doesn't give you that context of no, this is actually grass, this is pervious, versus we're just assuming it's this and not really very accurate detail.

      So the city is broken up into five watersheds, five unique subwatersheds, that drain around all the areas into either Deer Creek, the Big Sioux River, or Six Mile Creek. So really, the center point of the city is almost the high point within this area of the landscape. The watersheds that we really focus our efforts on was the South Dakota State University or SDSU watershed, the Central watershed, and the Medary watershed here.

      One interesting aspect that I want to point out is the SDSU watershed flows into Six Mile Creek to the north, and that actually flows to the southwest through the edge of the Central watershed, and impacts the stormwater within the Central watershed. So the key aspect that we really had to understand was that central water impact of stormwater that came from that SDSU watershed. We did break these up into two subwatersheds versus one because of the sheer size of the Six Mile Creek watershed coming throughout that landscape.

      Each of these subwatersheds had very unique characteristics to it that I'll be talking through and talking into more complexities of each of the modeling. So the SDSU watershed, the one very to the north, is approximately 2,200 acres in size, has 66,000 feet of pipe, 20 stormwater ponds throughout the area, and pretty even distributed of land use from residential to green spaces.

      The map on the right here shows the blue is the inundation based on a major storm event of 5.7 inch rain in 24 hours, while the green is actually the city storm sewer pipes, and these yellow arrows is really where the conveyance of the flow goes. So you can see back in the upper portion of the watershed, there's not a lot of flow or a lot of flooding within the residence areas. But once you get into the main conveyance system and into the channels, you can really see the flow regime and flow inundation expand out and get wider as you bring in more watershed.

      This watershed, like I said, is a little unique as it's pretty large. It's the smallest of the three watersheds, but it is also has the most impactful to the university. The modeling dynamics and complexity of the model has 906 nodes, 776 pipes, and 265,000 elements. So that terrain meshing, as you can see in the actual mesh zone itself of ICM, plays a key aspect of how many elements we have and really shows where that flooding can occur and where it can really be a hillside.

      The central area is also a relatively small watershed at just around 2000 acres in footprint, 75,000 feet of pipe, has four stormwater ponds throughout it. And the land use of this watershed is mixed between residential and airport. So on this watershed, a pretty key aspect was to understand the flooding that occurs around the airport, as well as what comes through Six Mile Creek and how does that impact what would happen in the airport. This watershed also has some industrial sites next to airport as well so that it could-- that understanding was key as well.

      This watershed has 912 nodes, 822 pipes, and just over 260,000 elements throughout the watershed. As you can see, there's more dynamics of the elements, which is every triangulation throughout that 2D zone. But the watershed isn't as large, so it really lessens the amount of elements that are there. The Medary watershed has a key, a unique factor as well. This watershed is 3410 acres in size, 101,000 feet of pipe, 52 stormwater ponds, and is predominantly residential land use.

      The key aspect of this Medary watershed is the wetland complex to the southern portion of the watershed, and how's that complex really inundate the flooding that is happening upstream, as well as how does the city storm sewer interact with that flooding? Now, the tricky part when we were evaluating proposed options in this watershed, was to not starve the wetland of water, but also providing enough water and enough storage to enhance that wetland complex for the best of its use and best of its ability.

      The 52 stormwater ponds that are in this watershed, which is the largest amount of ponds that are there, are really based on the development. Every development had their own stormwater pond to really capture what was happening throughout that area. As you can imagine, this one is a very complex model as it's 1065 nodes, 987 pipes, and just over 290,000 elements. So quite a bit of elements and quite a bit of topography changes throughout the entire watershed as you really got to understand the roads and the characteristics of each house for that 2D modeling and 2D landscape.

      Another key aspect that we utilized within the entire scope of this drainage plan was the pipe capacity analysis within ICM. The stormwater pipe capacity really gave us a nice visual to really to forecast where can we actually implement and increase conveyance within the system or where is it OK to where it will handle it for now, but they know in the future they're going to have to do something. So here on the map is the color gradients. This green is adequate capacity. This is a five year storm event.

      The orange is potentially inadequate, it was on the edge of if it would be adequate or not. And the red is not adequate at all. So you can see here that there's some areas that are green, then we hit some orange, and then a red. That pipe itself is the restricting factor within that pipe channel and the pipe network. As you can see over here on the eastern side, this entire area is potentially inadequate for the current capacities that they need in themselves.

      Another key aspect within ICM and the visuals that can be presented are the modeling videos. This here, the green is their stormwater sewer network. The blue is your rainfall depth. And really what we're playing here is the duration and inundation times throughout the landscape. It's a short video, but it really forecasts and shows you what residents could get flooded and what residents do not have to worry about the flooding.

      The interaction with the community was a key aspect to show this so that they understood that some of the flooding is going to happen, but not all of it's going to be in one spot. It's spread throughout the watershed. As well as residents up in the upper portion of the watershed, they should experience some as maybe their pipe capacity is too small. But it's for a limited time, limited duration. Maybe it's 10 minutes, maybe it's 30 minutes, but it's not there for weeks, so that they get that visual as well.

      So you may be wondering, how do we quality assurance and quality control these models and really validate what kind of flooding aspects occur throughout the watershed and throughout the city? This is where we utilize drone services. The city of Brookings, after one of their rain events, went out and flew a drone in some strategic areas that we wanted to validate modeling. On the lower left, we were concerned about this flooding area and how close it was getting to these residents that were around this development.

      So getting this picture, and then showing the modeling results that are right next to it, and comparing them side-by-side was a key aspect to the project, versus the upper right is really that we knew it flooded and we knew how long it would flood there as well. The community, we brought the community in from the initial start of the meetings and an initial start of the project. They did set up a subcommittee.

      That committee involved city staff, the university stakeholders, their municipal utility officials, community representatives as well. The municipal utilities officials were really focusing on that stormwater revision fee that I'll go into at the very end. We did have three town hall meetings throughout this planning process that really opened it up to the public and provided additional feedback. The online survey that we had enhanced those town hall meetings to allow residents that weren't actively there to be able to participate as well.

      So that was a lot of the modeling portion. Now, I'm going to briefly talk about their priority project matrix and how do we utilize that to the best of its ability. During our project plan, they wanted to revise how do they look at each individual projects on a very non-biased weighting scale. So we ended up looking at their previous categories and how they weighted them and recommended some changes and added a column to identify multi-use.

      So if a project was benefiting not just flooding, but also benefiting water quality and the aesthetics, it got a higher ranking and higher score than a project that would just benefit flooding that wasn't previously being captured in their previous categories. The weighting system also played a factor into it, of how much weight do we want each of these categories to hold. So when you score the project, it gets compounded.

      So let's take an example here of citizen safety as an example. Each category, for example, has a weight of 5. Well, one project, if we're doing a pond or a storage that reduces flooding over land and reduces a road for overtopping, gets a score of 5. Well, you multiply those two and get a total ranking score for citizen safety of 25. You do this for each of those categories, like I mentioned earlier, and you really get a non-biased score system of that project.

      So then you're capturing that project and not on a non-biased setting, and not unintentionally ranking it higher because you want that project to go. When we looked at these plans and looked at our concept plans, we really strategically planned on three areas. One, to provide storage upstream of any flooding location. Two, to increase the conveyance of that flooding area. And three, to provide storage downstream of where we can increase conveyance. So really put this into perspective is this concept plan here.

      It is on the SDSU watershed, so the flow goes to the north. We really wanted to phase this from a construction aspect backwards. So you provide that storage downstream, provide that capacity for that system, increase your channel capacity in the middle portion, so it gets more conveyance downstream as well as take more of that overland flow into that channel. And then the last phase here on the very upper portion is increase the culvert capacity to reduce flooding in the resident area. So that really harnessed everything together and really put it all into perspective of yes, this is a better approach than what was previously done.

      Funding was a big opportunity. As the city wanted to and had a previous method of their land use and zoning flooding, or zone funding source, to where the city is in a special area to that they can do special assessments and tax every property based on their land use and zoning, to a new methodology using that LiDAR data that I talked about earlier in accuracy to really capture the impervious surface contribution to the stormwater infrastructure. So on the right here is that percent impervious cover and on the left is really the land use zoning.

      And you can see just in this one parcel in one area, how much difference the land use is. The LiDAR impervious cover is from the western part of the SDSU campus to the eastern part. And each of that really gives a bigger picture of how does that parcel utilize the thermal structure. So for a real in-depth example here, this property on the left hand side is more of an industrial property that encompasses 51 acres in size, 13 acres is impervious, so the ratio of that is 25% impervious.

      Well, their current bill, because of the size of that boundary, was $23,000, just over $23,000. But in comparison to the property on the right, it's 12.8 acres in size, has a similar impervious acres of 12.1, so it's 94% impervious by to the acres. Well, their current bill was almost $6,700. So how is it fair to have a majority of the land being impervious to have a smaller bill that contributes more stormwater than a larger property that contributes less stormwater?

      The city was trying to figure out an equitable way to capture how do we do this on a parcel by parcel basis and be fair throughout the entire city? City partnered with a financial firm to really enhance this and make sure it was credible, and approachable, and consistent throughout the entire city. So that financial firm also helped analyze multiple different methods throughout the planning efforts here.

      The city really used that impervious data and land use data that was captured in the 2019 LiDAR to enhance this aspect and really focus on what is green space? What is pervious versus non impervious or impervious? And what is contributing to the stormwater? So as you can see on the bottom of the blue text here is really more of that proposed even distribution based on the impervious acres for each of the parcels. So it's a lot more fair for an industrial site that's got a lot of green space versus a strip mall that's got the same amount of impervious areas.

      City does have plans to update this on a regular and frequent basis based on either new permits that come in for the site or potentially updating LiDARs on an annual or regular basis. So lastly, I want to recap on all the three lessons that I've talked about today. As you can see in the picture on the right is the Infoworks ICM modeling of the SDSU area, and how that model really enhanced the abilities and capabilities of the master drainage plan and the studies that were provided for the city itself.

      Secondly, engaging the FEMA and communities early on and often throughout the planning process addressed not only community concerns about the project, but also addressed their realities. So they made sure that flooding event was actually real, be like, OK, I can recall back in 2018, 2017, that water did get very close to what you're representing and showing that throughout your entire model. That really helps validate our concerns of are we overestimating flooding? Are we underestimating flooding? What is truly happening and how can we tweak modeling parameters to make sure we capture that entirely?

      And lastly, I want to show the data tracking and visuals. These modeling videos have been a huge aspect of the work we do at ISG here and be able to show and validate all of the modeling and get residents to really visually see it. As engineers, we really understand it and need to know the numbers and the data behind it. But that data only goes so far. These visuals really enhance all of our capabilities and show the efficiencies of what our modeling softwares are doing, how powerful our modeling softwares are, and enhances us as engineers throughout the entire project.

      So thank you, everyone, for watching my presentation. I hope you got some beneficial aspects of the takeaways from this as well.

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      我们通过 Dynatrace 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。. Dynatrace 隐私政策
      Khoros
      我们通过 Khoros 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。. Khoros 隐私政策
      Launch Darkly
      我们通过 Launch Darkly 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。. Launch Darkly 隐私政策
      New Relic
      我们通过 New Relic 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。. New Relic 隐私政策
      Salesforce Live Agent
      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。