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Preparing Data for CityEngine (2016.0 Update)

The CityEngine 2016.0 release was a ground breaking release. For those who are new to CityEngine and unfamiliar with the workflows, the new release cuts out a significant amount of time required to access and prepare data for use in CityEngine. For those who still want to use their own data…great!

You can find more information about preparing your data here.

For those who want a quick way of getting data and getting a CityEngine project up and running as quickly as possible, CityEngine 2016 is here to help with the Get map data wizard.

The Get map data wizard can be accessed by opening a scene in CityEngine and going to File > Get map data.  In the wizard, use the map to zoom to and select the area you want data from. The area selected can be from 500 m x 500 m to 100,000 m x 100,000 m. Three types of data can be grabbed by the wizard: Basemap, Esri World Elevation, and OpenStreetMap.

The basemap data is used as imagery (also referred to as texture) to be draped over the elevation layer. The basemap options allow you to select the basemap you want (Satellite, Streets, Topo, Dark Gray, or Light Gray), along with the image resolution. The resolution you select will determine the number of pixels the imagery has. The actual resolution of the imagery will depend on both the extent and resolution. For example, a 500 meter extent with a low resolution of 1024 pixels wide is less grainy than a 1,000 meter extent with the same resolution. As high resolution is not available across the globe, you may need to choose a lower resolution or larger area to get data. The Esri World Elevation will pull terrain data, or digital elevation model (DEM) data, from the Esri elevation service. OpenStreetMap data includes networks (streets) and polygons (building footprints).  OpenStreetMap is an open source, user-produced dataset, so coverage cannot be guaranteed, particularly for building footprints.

One thing to keep in mind when selecting data is that CityEngine is a memory intensive program and the larger the area and the higher the resolution, the more memory is required. If the computer has limited memory, you must consider that when selecting data to import.

With each new version of CityEngine, the software gets smarter, easier, and more groundbreaking. This blog only touched on the Get map data portion of the software, but there are more exciting features to check out, such as sharing scene packages, alembic modeling, and accessing data from ArcGIS Online. For a full list of new features, go here.

Rebecca R. & Andrew J. – Desktop Support

Original author: Andrew J

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New ArcGIS Pro Add-in for Performance Monitoring: PerfTools

Are you curious to know how long ArcGIS Pro takes to render a particular bookmark or spatial extent, or play an animation? Or have you needed to monitor how long it takes to make a spatial selection from your underlying data?

A new add-in for ArcGIS Pro, PerfTools, can help users capture this pivotal performance information. With PerfTools, a comprehensive scripting capability allows you to edit, run, and log performance scenarios as you make underlying changes to your hardware, virtualization environments, spatial data, or other key user workflows. You’ll soon be able to discover any performance ramifications, make adjustments, and re-run. To get you started, we’ve enclosed several sample scripts with the PerfTools download.

For power users and GIS developers, the scripting language of PerfTools is extensible, allowing you to develop custom commands. Samples and documentation from PerfTools will guide you through the process of adding your custom code and timers.

PerfTools is a free add-in that can run on ArcGIS Pro 1.2 and 1.3. In future blog posts, we will highlight how to use some of its crucial functionality.

Download PerfTools for ArcGIS Pro 1.2 and 1.3

Disclaimer: This add-in is not supported by Esri Support Services; any questions or feedback regarding PerfTools should be forwarded to This email address is being protected from spambots. You need JavaScript enabled to view it.

Ian S. – Performance Engineer

Product Engineer at Esri (Redlands). Originally from Warwickshire, England. Exploring, mapping, and photographing the world are my biggest passions.
Original author: Ian Sims

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Regarding Vector Tile Layers and ArcGIS for Server

At Esri, we are always looking to provide you with cutting-edge capabilities that help turn GIS ideas into a powerful reality. One of our newer features is the ability to create, serve, and consume vector tiles, and it is exciting to see users taking advantage of this technology. We would like to take this opportunity to address some of the system requirements needed to implement these services.

As per our Vector tile services documentation, in order to share vector tile layers within your own infrastructure, you will need ArcGIS 10.4 for Server or higher – including the full Web GIS stack. This is comprised of Portal for ArcGIS, a hosting server using ArcGIS Server, and ArcGIS Data Store. To create the vector tiles, you will use ArcGIS Pro 1.2 or higher.

You might ask: Why are ArcGIS Pro and Portal for ArcGIS required to accomplish this task?

ArcGIS Pro offers a robust system of authoring capabilities, which allows content creators to build beautiful maps; and it has the tools to store these maps as vector tile packages for sharing with portals. These vector tile packages are then uploaded and shared through your portal, whether that is ArcGIS Online or Portal for ArcGIS.

Vector Restyle Example

Vector tile layers can be restyled without needing to rebuild the vector tile package.

Vector tile layers have unique capabilities. For example, they can be easily restyled with a custom look and feel, which happens dynamically without needing to rebuild the vector tile package. This is possible because Portal for ArcGIS takes advantage of the ArcGIS geoinformation model, which allows you to use, create, and share geographic information throughout your organization, the community, and openly on the web. For ArcGIS Server users who are used to sharing map layers with a portal by registering the REST endpoints as an item, or by creating custom applications directly against those REST endpoints, the geoinformation model provides a more flexible framework whereby geographic information is arranged into map and scene layers. These can be combined to build maps and scenes that can also be used in apps, in analysis, shared with groups, and so on.

Geoinformation Model Diagram

The ArcGIS geoinformation model is a framework that lets users easily build informative and compelling maps by pulling different layers together.

To recap, create vector tile layers and vector tile packages using ArcGIS Pro. Share the vector tile packages with Portal for ArcGIS. The portal will automatically unpack the package so that other people can use those vector tile layers in their maps and apps.

Thomas E. – Server Advocacy Lead

Original author: Thomas Edghill

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Classifying Images in ArcGIS for Desktop 10.4

With the addition of the Train Random Trees Classifier, Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix tools in ArcMap 10.4, as well as all of the image classification tools in ArcGIS Pro 1.3, it is a great time to check out the image segmentation and classification tools in ArcGIS for Desktop. Here we discuss image segmentation, compare the four classifiers (Train Iso Cluster Classifier, Train Maximum Likelihood Classifier, random trees, and Support Vector Machine), and review the basic classification workflow.

Image Segmentation

Before you begin image classification, you may want to consider segmenting the image first. Segmentation groups similar pixels together and assigns the average value to all of the grouped pictures. This can improve classification significantly and remove speckles from the image.

Train Iso Cluster Classifier

The Iso Cluster is an unsupervised classifier (that is, it does not require a training sample), with which the user can set the number of classes and divide a multiband image into that number of classes. This classifier is the easiest of all the classifiers to use, as it does not require creating a training sample and can handle very large segmented images. However, this classifier is not as accurate as the other classifiers due to the lack of training sample.

Train Maximum Likelihood Classifier

The Maximum Likelihood Classifier (MLC) uses Bayes’ theorem of decision making and is a supervised classifier (that is, the classifier requires a training sample). The training data is used to create a class signature based on the variance and covariance. Additionally, the algorithm assumes a normal distribution of each class sample in the multidimensional space, where the number of dimensions equals the number of bands in the image. The classifier then compares each pixel to the multidimensional space for each class and assigns the pixel to the class that the pixel has the maximum likelihood of belonging to based on its location in the multidimensional space.

Train Random Trees Classifier

A supervised classifier that was introduced with ArcGIS 10.4, the random trees classifier, breaks the training data into a random sub-selection and creates classification decision trees for each sub-selection. The decision trees run for each pixel, and the class that gets assigned to the pixel most often by the trees is selected as the final classification. This method is resistant to over-fitting due to small numbers of training data and/or large numbers of bands. This classifier also allows the inclusion of auxillary data, including segmented images and digital elevation model (DEM) data.

Train Support Vector Machine Classifier

Support Vector Machine (SVM) is a supervised classifier similar to the MLC classifier, in that the classifier looks at multidimensional points defined by the band values of each training sample. However, instead of evaluating the maximum likelihood that a pixel belongs to a class cluster, the algorithm defines the multidimensional space in such a way that the gap between class clusters is as large as possible. This divides the space up into different sections separated by gaps. Each pixel is classified where it falls in the divide space.

Image Classification Workflow:

With the addition of the Create Accuracy Assessment Points, Update Accuracy Assessment Points, and Compute Confusion Matrix tools in ArcGIS 10.4, it is now possible to both create and assess image classification in ArcMap and ArcGIS Pro.

The general workflow for image classification and assessment in ArcGIS is:

If desired, use the Segment Mean Shift tool to segment your imagery. Create a training sample using the Image Classification toolbar (if you are using the Iso Cluster classification, you can skip this step). Use one of the four training tools (Train ISO Cluster Classifier, Train Maximum Likelihood Classifier, Train Random Trees Classifier, Train Support Vector Machine Classifier). Use the Create Accuracy Assessment Points tool on the classified image to create randomly placed points that have values extracted from the image. Either use the Update Accuracy Assessment Point tool to compare this classification to previously created classifications, or manually edit the points and visually assess a reference image. Use the Compute Confusion Matrix tool to create a confusion matrix from the accuracy points. Use the measures of accuracy (the user’s accuracy, producer’s accuracy, and Kappa index) calculated by the confusion matrix to assess the classification. Make changes to the training sample, as needed, to improve the classification.

The best part about this six-step process is that it makes it pretty easy to compare different classification methods, and it’s often important to compare the different methods. Getting your training sites nailed down (step 2) is usually the toughest part, but steps 3 through 7 fly by since the analysis is done for you. In the end, you have several classified raster images to use in your work and can choose the best result based on your personal objectives.

As an example, we used this workflow to classify a Landsat 8 image of the Ventura area in Southern California. We used the MLC, SVM, and Random Trees (RT) methods to classify a single Landsat 8 raster captured on February 15, 2016. We classified the image into nine classes and manually selected training samples and accuracy assessment (“ground truth”) points. Additionally, we used a segmented image as an additional input raster for each classifier. Once we classified the rasters, we computed a confusion matrix for each output to determine the accuracy of the classification when compared to ground truth points. The Kappa index in the Confusion Matrix gives us an overall idea of how accurate each classification method is.

The results showed that each method did pretty well in the classification when looking at the Kappa indexes, as well as based on a visual assessment. In order of accuracy (from the highest Kappa index to the lowest), we see that the SVM output was the most accurate (Kappa = 0.915), followed by Random Trees (Kappa = 0.88) and finally the MLC method (Kappa = 0.846).

Table showing the results of the confusion matrix

We can see from the Confusion Matrix that some methods did better than others for specific classes. For example, the MLC didn’t do too well with Bare Earth classification, but RT and SVM weren’t too much better. This is great information for honing in on a better-classified image–now we know that we should focus on getting better Bare Earth training samples to improve our results. You could keep going with this until you get a really high accuracy for all classes, if that’s what you need for your analysis. If you need just a general idea of the area, you could just take what you get in Round 1! Check out what we got:

Source image:

Landsat 8 imagery of Ventura, California

Classified Image:

Results of the Vector Support Matrix Classification

Make sure to check out the new Image Classification Wizard with the release of ArcGIS Pro 1.3!

Julia L. and Rebecca R. – Desktop Support

Original author: Rebecca

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Support Services at the Esri User Conference: June 28-30, 2016

Esri Support Services will be at the 2016 Esri International User Conference in San Diego (June 27 – July 1), where we will offer the GIS Technical Support help desk area to answer any of your questions. Specialists in all areas of Esri software will be available to assist you.

Users attending the conference can visit our reception desk and set up a meeting with a support analyst (for a specific technology area) to discuss their problems, issues, and questions. Appointments are not required, so feel free to stop by in between sessions or during lunch, but don’t forget to register for the User Conference to gain access to the GIS Technical Support Island, technical sessions, user stories, and demos of the latest Esri products.

Location:

Toward the back of Hall A in the Customer Care pavilion; if you’re facing away from the street, we’re on the eastern-most side of the building!

Hours of Operation:

Tuesday, June 28th, 9:00 AM – 6:00 PM Wednesday, June 29th, 9:00 AM – 6:00 PM Thursday, June 30th, 9:00 AM – 1:30 PM

We look forward to seeing you there!

Gregory L. – Online Support Resources

Original author: Greg Lehner

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