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If you use Concurrent Use licensing for ArcGIS Pro or ArcMap, you will need to upgrade your ArcGIS License Manager to 2019.0 before upgrading to ArcGIS Pro 2.4 or ArcMap 10.7.1. If you are using ArcGIS Enterprise to manage your ArcGIS Pro Named User license, you will need to upgrade your ArcGIS License Manager to 2019.0 before upgrading to ArcGIS Pro 2.4. ArcGIS License Manager 2019.0 is backwards compatible with earlier versions of ArcGIS Pro and ArcMap. Concurrent Use licensing enables multiple users to share access to ArcGIS Desktop applications (ArcGIS Pro and ArcMap) on a network. ArcGIS License Manager, installed on the network, manages access and use of the Concurrent Use licenses for the users of the network. Learn how to upgrade ArcGIS License Manager.
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06-24-2019
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On January 14, 2020, Microsoft is ending support for their Windows 7, Windows Server 2008, and Windows Server 2008 R2 operating systems. As are we. After January 14, 2020, we will no longer support Windows 7, Windows Server 2008, and Windows Server 2008 R2 for Esri software. If you are still using Esri software on these operating systems, we highly recommend that you upgrade to Windows 10 or newer version of Windows Server, such as Windows Server 2016 or Windows Server 2019, before January 2020. If you stay on these older operating systems after January 14, 2020, you can continue to use Esri software, but it will not be supported. Esri will not be able to address defects related to Microsoft operating systems no longer supported. These changes are published in the Deprecated Features – Year-End 2018 document on the Esri Support site.
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05-02-2019
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Hi Ben Leslie, Sorry for the delayed response. I just saw this post today. For the LiveDVD to boot from the disk drive, you have to make sure your bios boot order is set to boot from the disk before it boots from the hard drive. If your bios is set to boot from the hard drive first, it'll never check to boot from the disk. With most Windows machines, you can press a key like F10 or F12 when you first boot (before the Windows logo appears) to enter the bios settings and change the boot order. Alternatively, you can boot the ISO or disk into a virtual machine. This is how I usually boot up the LiveDVD. In either VirtualBox or VMWare Player, I'll setup a blank virtual machine. Then in the settings for the machine, I'll point its virtual disk drive to either the .iso file or the actual physical disk drive if I have the disk in there. When I boot up the virtual machine, the LiveDVD should boot automatically. It won't boot from the hard drive because the hard drive is blank. Please note that the Esri Geoportal Server LiveDVD is a retired demo. It still works but it does run an older version of the Geoportal software. Also, the informational webpage that popped up when the LiveDVD started no longer works because it lived on the esri.com web servers and was taken down. Instead the Geoportal Server product page will appear. To get to the actual Esri Geoportal Server demo instance on the LiveDVD, you have to close the web browser and click the Geoportal icon on the desktop. The Geoportal, web servlet, database, and everything else required for the Geoportal demo to run all lives on the LiveDVD and should run even without an internet connection. Since it is retired, there are no guarantees about how well it works. I hope this was helpful. Thanks! Richard
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07-17-2018
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ArcGIS Pro 2.2, Esri’s flagship 64-bit desktop GIS, has been released and is available. Now is the perfect time to migrate to ArcGIS Pro ArcGIS Pro 2.2 is the largest update to ArcGIS Pro yet and brings a slew of new features and functionality. It adds and improves your highly requested workflows, features new innovations that take advantage of ArcGIS Pro’s unique 3D and 64-bit environment, and connects your desktop more tightly with the rest of the ArcGIS platform. Slice Tool Explore content hidden behind or within other content with the new interactive 3D exploration tool, Slice. You can slice through content in your scenes using planes or volumetric shapes. Slice is included among the other Interactive Analysis tools in the 3D Exploratory Analysis tools introduced in ArcGIS Pro 2.1. Full Motion Video (FMV) Play and analyze full-motion video (FMV) data that is geospatially enabled with the ArcGIS Image Analyst extension. Enable the projection and display of the video frame footprint and sensor position on the map while the video plays. You can also collect features in the video player and visualize them on the map, or collect features in the map and see them displayed in the video player. New Styles Inferno, Magma, Plasma, and Viridis scientific color schemes are now included in the ArcGIS Colors system style. These color schemes are particularly useful with imagery, LAS symbology, unclassed, and graduated colors symbology. They are also effective for grey scale environments and color-blind users. Additional Innovations and Updates ArcGIS Pro 2.2 is a big release. Here are some more new features: Support for reading Autodesk® Revit™ files enabling access to architectural model data inside ArcGIS. Stream layers: a new layer type that displays real-time streaming data. Apply photographic textures when interactively editing 3D objects. Pause drawing of a map or scene and still interact with it. While paused, you can navigate, add layers, or change the symbology; the state of the map will not refresh until paused drawing is turned off. Clip a measured grid to only show coordinates within its UTM zone boundary. This is especially useful when mapping areas that cross UTM boundaries. 50 new geoprocessing tools and batch geoprocessing to automate the running of a tool multiple times using many input datasets or different parameter settings. Get the full details and watch video from the ArcGIS Pro developers on what’s new in ArcGIS Pro 2.2.
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06-26-2018
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ArcGIS Pro 2.1.X requires the ArcGIS License Manager 10.6. However, you can use LM 10.6 to manage 10.5.1 licenses of ArcMap. Here is the system requirement for ArcGIS Pro that talks about LM requirements: ArcGIS Pro 2.1 system requirements—ArcGIS Pro | ArcGIS Desktop Here is the list of software and versions supported by ArcGIS License Manager 10.6: Supported software products—License Manager Guide | ArcGIS Desktop
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04-17-2018
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ArcGIS Image Analyst for ArcGIS Pro is now available for ArcGIS Personal Use and ArcGIS for Student Use ArcGIS for Personal Use and ArcGIS for Student Use subscriptions have just gotten more powerful with the addition of ArcGIS Image Analyst at no additional cost. The Image Analyst extension provides powerful image visualization, interpretation, and classification tools to efficiently unlock the potential of every pixel. ArcGIS Image Analyst extends ArcGIS Pro making it an image analysis workstation. Based on years of cross-domain experience in remote sensing and GIS, ArcGIS Image Analyst is designed for analysts, scientists, and photogrammetrists. It gives an advantage when working with image processing, interpretation, exploitation, analysis, and the creation of information products from remotely sensed data. ArcGIS Image Analyst provides intuitive image visualization and tools for advanced interpretation of imagery and other raster data. There’s no need to switch between remote sensing and GIS software. Gain access to stereo and image space visualization, powerful image processing, advanced mensuration and 3D feature compilation tools, plus machine learning classification tools creating an environment to an interpret, analyze, and exploit imagery with simplicity and speed. Learn more about ArcGIS Image Analyst
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04-04-2018
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Guest Post by Dmitry Kudinov, Esri Calculating travel times is a foundational piece in transportation logistics, urban design, asset management, retail, etc. At Esri, we just completed a research project where we used artificial intelligence (AI) and machine learning to train an artificial neural network to predict travel times for transportation networks with a large number of complex, hard-to-model, and hidden variables. For this project, we partnered with NVIDIA, who provided us with GP100 and GV100 cards, which made this experiment feasible form the computation standpoint. In this blog post, we will briefly discuss the details of this project, including the neural network architecture, training data format, efficient ways to evaluate training quality, and overall results which allow for a flexibility modelling intricate transportation aspects, and a significant throughput of the trained network. Introduction Building a route from A to B these days is trivial: numerous services and applications can do this for you quickly and for free. But what if you need to build a route that’s a little more complex? One which starts at your home, then goes to 3 different friends in various parts of town, then to a local produce store where you need to pick up an order you placed yesterday? But wait, the grocery store expects you at about 5pm and gets closed at 5:30pm, and it is actually located near one of the friends along the way whom you initially planned to visit first in the morning… and there is also that pesky road traffic which always gets in the way and ruins the plans. Things become suddenly quite trickier when you want to find the best visiting sequence which also has expected arrival times. Challenge 1: Computational complexity Logistics companies work with even more challenging requirements, scheduling not just multiple stops, but for multiple vehicles simultaneously. Large companies, while doing next day planning, schedule thousands of stops with hundreds of vehicles per day as a single optimization problem. Now you can start getting a sense of the computational complexity and resources involved in such operations. Challenge 2: Model complexity Another challenging area is hard-to-model aspects of transportation: Changes in road speeds caused by seasonality and periodic weather patterns, User preferred routes, Individual driving habits and/or vehicle features affecting performance, Individual commute preferences (especially important in urban areas and multimodal transportation, e.g. predict how long will it take a person to get to a chosen store to pick up her online-placed order), etc. Although some of these aspects are even hard to formalize and even harder to represent with traditional algorithms, these are the integral properties of modern transportation and are already captured but buried deep inside individual GPS tracks. The experiment While the former large-scale logistics challenge asks for high throughput computations, the latter scenarios demand greater degree of flexibility without increasing complexity of the model. Here at Esri, we decided to see if both requirements can be met with the help of machine learning. We used a simulated set of 300 million “journeys” (GPS tracks represented only by two locations - departure and destination, departure time, and how many minutes it took to travel, Figure 1) covering the region of California and Nevada roads to train an artificial neural network to predict travel times on the transportation graph. Figure 1. "Journeys" used to train the neural network. X1, Y1 – coordinates of the departure location; X2, Y2 – destination location; START_TIME – departure time in UTC milliseconds since Jan 1 st , 1970; NA_COST – time it took to travel in minutes. Despite the simplicity of the input data, the neural network, after being trained, was able accurately predict travel times between any two locations in California and Nevada taking departure time into account, effectively embedding the road congestion factor into its function. Once trained, the neural network can produce predictions with enormous throughput: a single desktop machine with a NVIDIA GV100 card can calculate over 300,000 ETAs per second, which is two-to-three orders of magnitude faster than common traditional deterministic algorithms. Of course, a prediction produced by a neural network is an approximation, but with a controllable accuracy - we will talk more about it below. For now, it is important to mention, that such a throughout may address the first challenge: logistics companies use various algorithms to solve multivehicle scheduling problems, and at the core of most of them lies the so-called Origin-Destination Cost matrix which needs to be calculated first, filled with ETAs for any possible combination of two stops, i.e. if we need to visit 1,000 stops, the OD Cost Matrix will have 1,000,000 ETAs. Our neural network can completely populate this matrix in only three seconds! The second challenge, flexibility, has a promising future too: while being trained with just simple two-point GPS tracks, the neural network successfully figured out accurate representation of road congestion patterns, which makes it flexible enough for further finetuning with user preferred routes, or adapt to individual driving habits, or commuter preferences. The details For this experiment we partnered up with NVIDIA team, who provided us with multiple GP100 and GV100 cards. The strong GPUs gave us the ability to train neural networks of realistic size and the various experiment times were shortened by twenty to fifty-plus times (thanks to massive parallelization of matrix operations needed for training). This made the search for optimal neural network architecture and numerous hyperparameter values feasible and effective. A simple example: we spent about eight months of running one of the GP100 cards 24-7 in a search for an efficient architecture, spatial and statistical distributions of the training set, good values for multiple hyperparameters. The machine had 4 (8 hyperthreaded) Xeon(R) CPU E5-1620 v2 @ 3.70GHz CPU cores. After we compared single epoch training time between the GP100 and of the same machine CPU – the difference was over fifty times! This translates the above eight months of GPU time into over 30 years of CPU! OK, let’s get back to the details. We used TensorFlow + Keras libraries to build a dense fully connected neural network (multilayer perceptron (MLP)) with sixteen hidden layers and ten million trainable parameters total. To reduce the overfitting, we added a Dropout node right before the output layer. The input was represented by normalized pairs of coordinates for departure and destination locations, and departure time; the output – single value showing the number of minutes it took to travel from A to B at given time. We used Mean Squared Error (MSE) as the loss function, and Adamax optimizer with initial learning rate of 1e-3. Training was performed for 4,000 epochs total on consecutive subsets of 20 million journeys, simulating “online” training. By the end of training, the MSE value on validation set was at about ~13.5. But how good is MSE of ~13.5? Can the neural network be usable at this point? Well, MSE of 13.5 translates into 3.7 minutes of standard deviation of predicted values being off from the ground truth… but the routes in California-Nevada region may differ significantly in size: 3.7 minutes difference may be OK for an hour-long route, but for a route which is under 10 minutes - that’s a big difference. So, a chart showing how prediction accuracy varies depending on route length can tell a better story – Figure 2. Figure 2. Variation of prediction accuracy as a function of route length. Another great tool for evaluating prediction accuracy which we built here in Esri, is a WMS REST service endpoint wrapping our trained neural network. The service returns a geographically bound PNG containing travel time surface, where every pixel is colored proportionally to the time it takes to reach it from the central pixel. Once constrained by a maximum travel time value, such surface looks like a isochron polygon. Figure 3 shows isochrones being built around San Francisco: Figure 3. Isochron polygon constrained by 20-minute travel time. If such isochrones remind you of Network Analyst Service Area polygons, you are not mistaken: ultimately, in such form, both represent “reachability” zones and, if our neural network was trained well, these two should match closely. Figure 4 shows how neural network produced isochron (blue) matches Service Area polygon (red) built for the same departure location and time of day. Note how closely the boundaries of two polygons match in places where they both intersect with the streets. It is also important to note, that San Francisco area is particularly challenging due to an intricate coastal line and uneven distribution of transportation graph elements, and nevertheless, our neural network gets a good grasp on this complexity producing very compelling predictions. Figure 4. Neural network isochrons (blue) and matching Service Area polygons (red). So, what about the road congestions which we mentioned before? Here is the last animation for today, Figure 5, showing how a 25-minute isochron changes over a 24 hours period. You can see how the isochron shrinks during the business hours, and how it expands back during the night. Figure 5. Road congestion patterns captured by the neural network during training. There corlor rings were added for visualization purposes and are similar to isolines of a continuous 3d surface, where the 3rd dimension is time. That animation has 12 rings colored (or left transparent) for ranges of travel times falling into 125 second buckets – 12 total, summing up to 25 minutes. The road ahead Although we have achieved here some impressive results, there is room for improvement. One particular path which we want to explore further down the road, is to check the applicability of one-dimensional convolutional network instead of MLP. The reason for this is simple: there is a strong correlation between the coordinates, and multiple repeating patterns in the training data – this makes our scenario a good candidate for a convolutional architecture which will scale better for larger geographical areas. Another area of improvement can be illustrated with the Figure 2 above: we want smaller standard deviation values for shorter routes, and this can be achieved by more accurate selection of the training data, giving shorter routes a bigger share in the training set. And, of course, the final step – using the trained neural network in transportation analysis and planning. We will keep you updated on the progress.
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03-27-2018
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The Esri User Conference features many presentations and events covering hundreds of topics. Here is handy guide to navigate the ArcGIS Pro sessions at the UC.
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06-28-2017
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Nope, updating the License Manager is required for both. Let us know how it goes. Thanks!
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06-27-2017
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ArcGIS Pro 2.0 requires the ArcGIS 10.5.1 License Manager for Concurrent Use or Named User via Portal for ArcGIS licenses. To upgrade your license manager, please contact your organization’s system administrator, or follow these simple steps: Log into the computer as a user with administrative privileges. Log into MyEsri and go to My Organizations > Downloads. From the Downloads section, select Product Components, search for “License Manager” and click “Download” beside the ArcGIS License Manager product. Download the appropriate license manager platform your organization uses. The license manager will download and run the setup program. Follow the instructions on the screen. The ArcGIS License Manager 10.5.1 is backwards compatible with previous versions of ArcGIS Pro and ArcMap. Details about managing ArcGIS Pro with Concurrent Use licenses are available in the ArcGIS Pro Online Help.
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06-27-2017
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Here is the support doc from Microsoft about error code 1603: https://support.microsoft.com/en-us/kb/834484. My guess is it's a permissions issue. Make sure you're running the update as an administrator. You can also try uninstalling 1.3 before installing 1.4.
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01-13-2017
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Did you bring a sample of your data to the Esri User Conference for an Esri expert to review? It is a quick and easy way to gain insight into whether your data is optimized for your industry. This year Esri is offering health checks to users in the following industries: Water/wastewater/stormwater Electric & gas Roads & highways Land records and addressing 3D Tell us about your data check-up here.
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06-26-2014
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Hi Artie123, It sounds like you didn't restart Tomcat after changing the gpt.xml files. Any changes that you make to the Geoportal's configuration won't take effect until you restart the Tomcat server. If that didn't work, reopen the gpt.xml file to see if your new password is still there. If your old password is there instead, the gpt.xml file might be set to Read Only or you might have some kind of permission issue with changing the file. I hope that this helped. Thanks, Richard
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06-12-2013
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Hi David, One more thing. Since you referenced that you used the Linux quick start guide, I wanted to point you to a few more resources. 1. Here is the full documentation for installing the Geoportal Server. In particular, look at the Geoportal Server Installation Guide and Geoportal Server Linux Installation Guide PDFs. They provide a lot more detail than what I put into the quick start guide, such as LDAP authentication and additional configurations. 2. Take a look at the Esri Geoportal Server LiveDVD Demo. I built it using the same setup as the How to Set Up an Esri Geoportal Server on Linux quick start guide. You can compare the set up on the LiveDVD with your installation to see what's different. It runs the 1.2.0 version of the Geoportal, so it won't be exactly the same. You can run the LiveDVD as a virtual machine using VMWare, Oracle Virtualbox, or KVM, which will let you access both the LiveDVD and your host machine if you only have access to one computer. -Richard
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04-12-2013
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Hi David, I can't tell exactly what's going on with your install based on the stacktrace.txt file alone. I can't tell if your Geoportal install isn't happy about moving from Windows to Linux or if there are problems with your Linux machine connecting to the PostgreSQL machine. There are several things that could be causing your problems. 1. Are you using the same version of Tomcat on the Linux machine that you used on the Windows machine? 2. How did you install Tomcat on the Linux machine? Did you install it by using the Linux distribution's repositoriesor by using the core binary distribution from tomcat.apache.org? Hopefully you used the core binary distribution and not the repositories. If you used core binary then all of the Tomcat directories will be stored within one directory, which mirrors the Windows install. If you used the repositories, the Tomcat directories are generally spread throughout the Linux OS instead of kept together in one directory. For example, the <Tomcat>/bin directory will be in the /bin directory, the <Tomcat>/lib directory will be in one of the /lib directories, and the <Tomcat>/webapps and other Tomcat directories will be spread throughout the /usr, /var, /home, or other directories. 3. Are you using the same version of Tomcat on both machines? 4. What did you actually migrate over from the Windows machine to the Linux machine? Was is just the Geoportal app, the webapps folder, or the entire Tomcat install? 5. Did you include the geoportal.xml file from the <Tomcat>/conf/Catalina/localhost directory and the appropriate postgresql jdbc file from the <Tomcat>/lib directory in your migration? 6. Did you update your geoportal.xml and gpt.xml (<Tomcat>/webapps/geoportal/WEB-INF/classes/gpt/config) files for your Linux install? For example, in the gpt.xml file, did you update the location of your lucene directories, such as changing the lucene indexLocation from something like "c:\lucene" for Windows to something like "/usr/local/etc/lucene" for Linux. 7. Did you double check the permissions for the Linux user that's running Tomcat? Does it own or have full permissions to run and modify all the files and directories it needs to, such as all of the Tomcat and lucene directories? 8. Is there anything different/special about your Linux machine's connection to your network that you need to take into account, such as proxies or authentication settings? 9. Is your Linux machine able to access the PostgreSQL machine and the PostgreSQL database at all? If you can, install the PostgreSQL client software (postgresql-client or similar in most Linux distro's repositories) and see if you can connect to the PostgreSQL database using psql. See PostgreSQL's documentation on psql (www.postgresql.org/docs/9.2/static/app-psql.html) for details on how to connect to remote database via psql. 10. Are there any permissions that you need to set on the PostgreSQL machine for it to accept connections from your Linux machine? For example, the pg_hba.conf file in your PostgreSQL machine's <PostgreSQL>/<version>/data directory might be configured to accept connections from your Windows machine but not your Linux machine. Sorry that there's a lot there and I hope that it all makes sense. There are probably some other things going on that I'm not thinking of right now. I hope this helps you figure out what's going on. Let me know if you need something clarified or have any questions. -Richard
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