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2020

Drone2Map version 2.1 is now available.  Current users can view “About” in the main menu on the left side of the screen to verify your version, and download a new version if necessary.  You can also download from My Esri.

 

 

What’s new in Drone2Map for ArcGIS version 2.1? 

In this release we continue to improve the user experience in many areas of the workflow.

 

Camera Model Editor

  • Esri maintains an internal camera database which is updated along with Drone2Map several times per year. In addition to the internal camera database, Drone2Map also has a user camera database. With the Camera Model Editor, users are now able to edit existing cameras from the internal camera database and store the modified camera models in the user camera database.

  • An important use case supported by this capability is to provide support for high quality metric cameras, where the photogrammetric lens parameters such as focal length, principal point and distortion are stable and known. Since Drone2Map supports consumer cameras, these parameters may (by default) be adjusted during processing. For metric cameras, the Camera Model Editor allows users to input known, high accuracy parameters when applicable and maintain those values throughout processing.

  • Additionally, when a successful project has been processed and you are happy with the results, the .d2mx file from that project may be imported into the camera model editor of a new project and those optimized camera parameters from the imported project will be stored in the user camera database and allow those parameters to be used in future processing jobs. This helps to standardize results and reduce processing times.

 

Control Updates

  • In this release there is an improved user experience for managing control using the Control Manager.  Users can view properties of each control point, filter based on the type of control, and launch the links editor, all with a few button clicks.
  • Some geographic features, such as water, can be difficult to generate sufficient tie points and successfully match those tie points using automated algorithms. Now users can create and link manual tie points to images to successfully process imagery in geographic areas that previously caused problems.

  • Linking control to your images can be a time-consuming process. At Drone2Map 2.1, we have introduced assisted image links. This workflow requires initial processing to be run, and after you enter one link, the software is able to automatically find your control markers in subsequent images and provide visual feedback as to the accuracy of that link. Once satisfied with the positioning of the control to the images, simply click Auto Link and Drone2Map will link the verified control for you.

 

 

Share DEM as Elevation Layer

  • Drone2Map users are now able to publish their own custom surfaces on ArcGIS Online or ArcGIS Portal for either an ortho reference DTM or top surface DSM. These surfaces can be used in 3D web scenes to ensure accurate height values for point clouds and meshes generated by Drone2Map.

 

 

Add custom DEM into the Drone2Map project

  • Users may add their own elevation surface into the project (on top of the default World Terrain surface), to ensure that any 3D views incorporate the authoritative elevation surface.  This can be very useful in project areas that are captured on multiple dates (e.g. agriculture) and/or where an accurate input terrain is important (e.g. an airport, construction site, or a site with material stockpiles).

  • In addition, if ground control points are subsequently extracted from the map, the Z values are provided by the custom elevation surface. This is important to ensure date-to-date consistency for sites that are captured repeatedly and analyzed over time.

 

Elevation Profile and Spectral Profile for additional analytical capabilities

  • Users are now able to generate cross-sectional elevation profiles in any Drone2Map projects that are processed to create output surfaces (DSM and/or DTM).

                                Imagery provided by GeoCue Group, Inc.                                                                

 

  • For users with multispectral cameras, Drone2Map also allows extraction of spectral profiles (defined by point samples, linear transects, or 2D areas of interest) to support detailed analysis of vegetation or other landcover surface types.

 

 

Colorized Indices

  • Indices created from multispectral imagery products are now colorized by default.

 

New Inspection Template

  • The inspection template has been added to all users who wish to create projects that are focused on inspecting, annotating, and sharing raw drone images.

 

Browse performance improvements

  • Performance has been improved when browsing folders and files on disk.

Exif reader improvements

  • The performance of reading and extracting Exif data from drone images has improved to significantly reduce the amount of time required to create a project.

Licensing Changes

  • Drone2Map for ArcGIS 2.1 is a “premium app” which is a for-fee add-on to ArcGIS Online or ArcGIS Enterprise.

 

Full release notes for Drone2Map 2.1 are available here

The ArcGIS Image Analyst extension for ArcGIS Pro 2.5 now features expanded deep learning capabilities, enhanced support for multidimensional data, enhanced motion imagery capabilities, and more.

Learn about  new imagery and remote sensing-related features added in this release to improve your image visualization, exploitation, and analysis workflows.

Deep Learning

We’ve introduced several key deep learning features that offer a more comprehensive and user-friendly workflow:

  • The Train Deep Learning Model geoprocessing tool trains deep learning models natively in ArcGIS Pro. Once you’ve installed relevant deep learning libraries (PyTorch, Fast.ai and Torchvision), this enables seamless, end-to-end workflows.
  • The Classify Objects Using Deep Learning geoprocessing tool is an inferencing tool that assigns a class value to objects or features in an image. For instance, after a natural disaster, you can classify structures as damaged or undamaged.
  • The new Label Objects For Deep Learning pane provides an efficient experience  for managing and  labelling training data. The pane also provides the option to export your deep learning data.
  • A new user experience lets you interactively review deep learning results and edit classes as required.
New deep learning tools in ArcGIS Pro 2.5

New deep learning tools in ArcGIS Pro 2.5

Multidimensional Raster Management, Processing and Analysis

New tools and capabilities for multidimensional analysis allow you to extract and manage subsets of a multidimensional raster, calculate trends in your data, and perform predictive analysis.

New user experience

A new contextual tab in ArcGIS Pro makes it easier to work with multidimensional raster layers or multidimensional mosaic dataset layers in your map.

Intuitive user experience to work with multidimensional data

Intuitive user experience to work with multidimensional data

  • You can Intuitively work with multiple variables and step through time and depth.
  • You have direct access to the new functions and tools that are used to manage, analyze and visualize multidimensional data.
  • You can chart multidimensional data using the temporal profile, which has been enhanced with spatial aggregation and charting trends.

New tools for management and analysis

The new multidimensional functions and geoprocessing tools are listed below.

New geoprocessing tools for management

We’ve added two new tools to help you extract data along specific variables, depths, time frames, and other dimensions:

  • Subset Multidimensional Raster
  • Make Multidimensional Raster layer

New geoprocessing tools for analysis

  • Find Argument Statistics allows you to determine when or where a given statistic was reached in multidimensional raster dataset. For instance, you can identify when maximum precipitation occurred over a specific time period.
  • Generate Trend Raster estimates the trend for each pixel along a dimension for one or more variables in a multidimensional raster. For example, you might use this to understanding how sea surface temperature has changed over time.
  • Predict Using Trend Raster computes a forecasted multidimensional raster using the output trend raster from the Generate Trend Raster tool. This could help you predict the probability of a future El Nino event based on trends in historical sea surface temperature data.

Additionally, the following tools have improvements that support new analytical capabilities:

New raster functions for analysis

  • Generate Trend
  • Predict Using Trend
  • Find Argument Statistics
  • Linear Spectral Unmixing
  • Process Raster Collection

New Python raster objects

Developers can take advantage of new classes and functions added to the Python raster object that allow you to work with multidimensional rasters

New classes include:

  • ia.RasterCollection – The RasterCollection object allows a group of rasters to be sorted and filtered easily and prepares a collection for additional processing and analysis.
  • ia.PixelBlock – The PixelBlock object defines a block of pixels within a raster to use for processing. It is used in conjunction with the PixelBlockCollection object to iterate through one or more large rasters for processing.
  • ia.PixelBlockCollection – The PixelBlockCollection object is an iterator of all PixelBlock objects in a raster or a list of rasters. It can be used to perform customized raster processing on a block-by-block basis, when otherwise the processed rasters would be too large to load into memory.

New functions include:

  • ia.Merge() – Creates a raster object by merging a list of rasters spatially or across dimensions.
  • ia.Render (inRaster, rendering_rule={…}) – Creates a rendered raster object by applying symbology to the referenced raster dataset. This function is useful when displaying data in a Jupyter notebook.
  • Raster functions for arcpy.ia – You can now use almost all of the raster functions to manage and analyze raster data using the arcpy API
New tools to analyse multidimensional data

New tools to analyse multidimensional data

Motion Imagery

This release includes enhancements to our motion imagery support, so you can better manage and interactively use video with embedded geospatial metadata:

  • You can now enhance videos in the video player using contrast, brightness, saturation, and gamma adjustments. You can also invert the color to help identify objects in the video.
  • Video data in multiple video players can be synchronized for comparison and analysis.
  • You can now measure objects in the video player, including length, area, and height.
  • You can list and manage videos added to your project with the Video Feed Manager.
Motion imagery in ArcGIS Pro

Pixel Editor

The Pixel Editor provides a suite of tools to interactively manipulate pixel values of raster and imagery data. Use the toolset for redaction, cloud and noise removal, or to reclassify categorical data. You can edit an individual pixel or a group of pixels at once. Apply editing operations to pixels in elevation datasets and multispectral imagery. Key enhancements in this release include the following:

  • Apply a custom raster function template to regions within the image
  • Interpolate elevation surfaces using values from the edges of a selected region

Additional resources