Users of the Oriented Imagery Catalog Management Tools in ArcGIS Pro 2.5 may have encountered a crash when browsing for an Oriented Imagery Catalog (OIC) as input in any of the tools in the Oriented Imagery Catalog toolbox. 

This bug will be fixed in the next release of ArcGIS Pro, but there is a workaround in the meantime. To avoid the crash, don't click the Browse folder icon to navigate to your OIC. Instead of browsing to the file, you should copy the path to the OIC file and paste it into the input field of the GP tool.

To do this in Windows:

1. Open Windows File Explorer.
2. Browse to the OIC file. (If you’ve created this in your project’s geodatabase, the OIC file will be located by default at C:\Users\[username]\Documents\ArcGIS\Projects\[Project Name]\[OIC name].)
3. Select the OIC file, then click Copy Path. (You may have to remove any quotation marks around the file path.)

4. In ArcGIS Pro, paste the path into the Input Oriented Imagery field of the GP tool.

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

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

• 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:

• The Aggregate Multidimensional Raster tool supports more aggregation keywords.
• The Generate Multidimensional Anomaly tool has four new options for the Anomaly Calculation Method

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

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.

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

• Learn more about the ArcGIS Image Analyst extension for ArcGIS Pro and how to get it
• See what’s new in ArcGIS Pro 2.5
• Learn how to use ArcGIS Image Analyst, including hands-on tutorials
• Check out help documentation for ArcGIS Image Analyst

Using your knowledge of geography, geospatial and remote sensing science, and using the image classification tools in ArcGIS, you have produced a pretty good classified raster for your project area. Now it’s time to clean up some of those pesky pixels that were misclassified – like that one pixel labelled “shrub” in the middle of your baseball diamond. The fun part is using the Pixel Editor to interactively edit your classified raster data to be useful and accurate. The resulting map can be used to drive operational applications such as land use inventory and management.

For operational management of land use units, a useful classified map may not necessarily be the most accurate in terms of identified features. For example, a small clearing in a forest, cars in a parking lot, or a shed in a backyard are not managed differently than the larger surrounding land use. The Pixel Editor merges and reclassifies groups of pixels, objects and regions quickly and easily into units that can be managed similarly, and result in presentable and easy-to-understand maps for your decision support and management.

What is the Pixel Editor?

The Pixel Editor is an interactive group of tools that enables editing of raster data and imagery , and it is included with the ArcGIS Pro Image Analyst. It is a suite of image processing capability, driven by an effective user interface, that allows you to interactively manipulate pixel values. Try different operations using different parameter settings to achieve optimum editing results, then save, publish and share them.

The Pixel Editor is contextual to the raster source type of the layer being edited, which means that suites of capability are turned on or off depending on the data type of the layer you are working with. For thematic data, you can reassign pixels, objects and regions to different classes, perform operations such as filtering, shrinking or expanding classes, masking, or even create and populate new classes. Edits can be saved, discarded, and reviewed in the Edits Log.

Pixel Editor in action

Because the Pixel Editor is contextual, you need to first load the layer you want to edit. Two datasets are loaded into ArcGIS Pro, the infrared source satellite image and the classified result. The source data is infrared satellite imagery where vegetation is depicted in shades of red depending on coverage and relative vigor. This layer has been classified using the Random Trees classifier in ArcGIS Pro. The class map needs editing to account for classification discrepancies and to support operational land use management.

Launch the Pixel Editor

To launch the Pixel Editor, select the classified raster layer in the Contents pane, go to the Imagery tab and click the Pixel Editor button from the Tools group.

The Pixel Editor tab will open. In this example, we’ll be editing a land use map, so the editor will present you with editing tools relevant for thematic data.

The Region group provides tools for delineating and managing a region of interest. The Edit group provides tools to perform specific operations to reclassify pixels, objects or regions of interest. The Edit group also provides the Operations gallery, which only works on Regions.

Reclassify

Reclassify is a great tool to reassign a group of pixels to a different class. In the example below, you can see from the multispectral image that either end of the track infield is in poor condition with very little vegetation, which resulted in that portion of the field being incorrectly classified. We want to reclassify these areas as turf, which is colored bright green in the classified dataset.

We used the multispectral image as the backdrop to more easily digitize the field, then simply reassigned the incorrect class within the region of interest to the Turf class.

Majority Filter and Expand
Check out the parking lots south of the track field containing cars, which are undesirable in terms of classified land use. We removed the cars and make the entire parking lot Asphalt with a two-step process:

(1) We digitized the parking lot and removed the cars with a Majority Filter operation with a filter size of 20 pixels – the size of the biggest cars in the lot.

(2) Then we used Expand to reclassify any remaining pixels within the lot to Asphalt.

Add a new class

Another great feature of the Pixel Editor is the ability to add a new class to your classified raster. Here, we added a Water class to account for water features that we missed in the first classification.

In the New Class drop-down menu, you can add a new class, provide its name, class codes, and define a color for the new class display.

After adding the new class to the class schema, we used the Reclass Object tool to reassign the incorrect Shadow class to the correct Water class. Simply click the object you want to reclassify and encompass it within the circle - and voila! – the object is reclassified to Water.

Feature to Region

Sometimes you may have an existing polygon layer with more accurate class polygon boundaries. These could be building footprints, roads, wetland polygons, water bodies and more. Using the Feature to Region option you can easily create a region of pixels to edit by clicking on the desired feature from your feature layers in the map. Then use the Reclass by Feature tool to assign the proper class.

We see the updated water body now matches the polygon feature from your feature class. The class was also changed from Shadow to its correct value, Water.

Summary

The Pixel Editor provides a fast, easy, interactive way to edit your classified rasters. You can edit groups of pixels and objects, and editing operations include reclassification using filtering, expanding and shrinking regions, or by simply selecting or digitizing the areas to reclassify. You can even add an entire new class. Try it out with your own data, and see how quickly you can transform a good classification data set into an effective management tool!

Acknowledgement

Thanks to the co-author, Eric Rice, for his contributions to this article.

In the aftermath of a natural disaster, response and recovery efforts can be drastically slowed down by manual data collection. Traditionally, insurance assessors and government officials have to rely on human interpretation of imagery and site visits to assess damage and loss. But depending on the scope of a disaster, this necessary process could delay relief to disaster victims.

Article Snapshot: At this year’s Esri User Conference plenary session, the United Services Automobile Association (USAA) demonstrated the use of deep learning capabilities in ArcGIS to perform automated damage assessment of homes after the devastating Woolsey fire. This work was a collaborative prototype between Esri and USAA to show the art of the possible in doing this type of damage assessment using the ArcGIS platform.

The Woolsey Fire burned for 15 days, burning almost 97,000 acres, and damaging or destroying thousands of structures. Deep learning within ArcGIS was used to quickly identify damaged structures within the fire perimeter, fast tracking the time for impacted residents and businesses to have their adjuster process the insurance claims.

The process included capturing training samples, training the deep learning model, running inferencing tools and detecting damaged homes – all done within the ArcGIS platform. In this blog, we’ll walk through each step in the process.

Step1: Managing the imagery

Before the fires were extinguished, DataWing flew drones in the fire perimeter and captured high resolution imagery of impacted areas. The imagery totaled 40 GB in size and was managed using a mosaic dataset. The mosaic dataset is the primary image management model for ArcGIS to manage large volumes of imagery.

Step2. Labelling and preparing training samples

Prior to training a deep learning model, training samples must be created to represent areas of interest – in this case, the USAA was interested in damaged and undamaged buildings. The building footprint data provided by LA County, was overlaid on the high resolution drone imagery in ArcGIS Pro, and several hundred homes were manually labelled as Damaged or Undamaged  (a new field called “ClassValue” in the building footprint feature class was attributed with this information). These training features were used to export training samples using the Export Training Data for Deep Learning tool in ArcGIS Pro, with the metadata output format set to ‘Labeled Tiles’.

                             

               Resultant image chips (Labeled Tiles used for training the Damage Classification model)

Step 3: Training the deep learning model

ArcGIS Notebooks was used for training purposes. ArcGIS Notebooks is pre-configured with the necessary deep learning libraries, so no extra setup was required. With a few lines of code, the training samples exported from ArcGIS Pro were augmented. Using the arcgis.learn module in the ArcGIS Python API, optimum training parameters for the damage assessment model were set, and the deep learning model was trained using a ResNet34 architecture to classify all buildings in the imagery as either damaged or undamaged.

               

                                       The model converged around 99% accuracy                      

Once complete, the ground truth labels were compared to the model classification results to get a quick qualitative idea on how well the model performed.

         

                                                                           Model Predictions

For complete details on the training process see our post on Medium

Finally, with the model.save() function, the model can be saved and used for inferencing purposes.

Step 4: Running the inferencing tools

Inferencing was performed using the ArcGIS API for Python. By running inferencing inside of ArcGIS Enterprise using the model.classify_features function in Notebooks, we can take the inferencing to scale.

The result is a feature service that can be viewed in ArcGIS Pro. (Here’s a link to the web map).

Over nine thousand buildings were automatically classified using deep learning capabilities within ArcGIS!

The map below shows the damaged buildings marked in red, and the undamaged buildings in green. With 99% accuracy, the model is approaching the performance of a trained adjuster – what used to take us days or weeks, now we can do in a matter of hours.

               

                                                Inference results

Step 5: Deriving valuable insights

Business Analyst: Now that we had a better understanding of the impacted area, we wanted to understand who were the members impacted by the fires. When deploying mobile response units to disaster areas, it’s important to know where the most at-risk populations are located, for example, the elderly or children. Using Infographics from ArcGIS Business Analyst, we extracted valuable characteristics and information about the impacted community and generated a report to help mobile units make decisions faster.

                                       Get location intelligence with ArcGIS Business Analyst

Operations Dashboard: Using operations dashboard containing enriched feature layers, we created easy dynamic access to the status of any structure, the value of the damaged structures, the affected population and much more.

            

Summary:

Using deep learning, imagery and data enrichment capabilities in the ArcGIS platform, we can quickly distinguish damaged from undamaged buildings, identify the most at-risk populations, and organizations can use this information for rapid response and recovery activities.

 More Resources:

Deep Learning in ArcGIS Pro

Distributed Processing using Raster Analytics

Image Analysis Workflows

Details on the model training of the damage assessment 

ArcGIS Notebooks

ABOUT THE AUTHORS

Vinay Viswambharan

Product manager on the Imagery team at Esri, with a zeal for remote sensing and everything imagery.

Rohit Singh

Development Lead - ArcGIS API for Python. Applying deep learning to the Science of Where @Esri. https://twitter.com/geonumist

Do you have imagery from an aerial photography camera (whether a modern digital camera or scanned film) and the orientation data either by direct georeferencing or the results of aerial triangulation? If yes, you’ll want to work with a mosaic dataset, and load the imagery with the proper raster type.

The mosaic dataset provides the foundation for many different use cases, including:

• On-the-fly orthorectification of images in a dynamic mosaic, for direct use in ArcGIS Pro or sharing through ArcGIS Image Server.
• Production of custom basemaps from source imagery.
• Managing and viewing aerial frame imagery in stereo. 
• Accessing images in their Image Coordinate System (ICS).  

There are different raster types that support the photogrammetric model for frame imagery.  If you have existing orientation data from ISAT or Match-AT, you can use the raster types with those names to directly load the data (see Help here). 

For a general frame camera, you’ll want to know how to use the Frame Camera raster type and we have recently updated some helpful resources:  

• We’ve published a ‘best practices’ document at http://esriurl.com/FrameCameraBestPractices that provides a comprehensive overview of many different scenarios.
• We’ve also updated resources on the Imagery Workflows site to provide working examples you can follow. These resources start at this link http://esriurl.com/FrameCameraSample and will lead you to 2 different examples: 
  • First is the Manual workflow sample http://esriurl.com/FrameCameraManualSample if you’d like to see the “deep dive” details for how this raster type works.
  • The second is the Automated sample script http://esriurl.com/FrameCameraAutomatedScript that provides support for ingestion of aerial triangulation project files from Bingo, Vexcel Ultramap, DVP PAR, Australis, and Sanborn.

Further information:

• Note that if your imagery is oblique, the Frame Camera raster type supports multi-sensor oblique images. Refer to the http://esriurl.com/FrameCameraBestPractices for configuration advice.
• If you want to extract a digital terrain model (DTM) from the imagery, or improve the accuracy of the aerial triangulation, see the Ortho Mapping capabilities of ArcGIS Pro (advanced license). http://esriurl.com/OrthoMapping.
• If you are seeking additional detail on the photogrammetric model used within the Frame Camera raster type, see this supplemental document http://esriurl.com/FrameCameraDetailDoc

Given the growing number of people using commercial drones these days, a common question is: “What do I do with all this imagery?”

The simple answer is that it depends on what you’re trying to accomplish.

If you just want to share the imagery as-is, and aren’t worried about making sure it’s georeferenced to be an accurate depiction of the ground, Oriented Imagery is probably your answer. If you’re capturing video, Full Motion Video in the Image Analyst extension for ArcGIS Pro is your best bet. Ultimately, though, many users plan to turn the single frame images acquired by drones into authoritative mapping products—orthorectified mosaics, digital surface models (DSMs), digital terrain models (DTMs), 3D point clouds, or 3D textured meshes.

Esri has three possible solutions for producing authoritative mapping products from drone imagery, each targeted for different users— (1) Drone2Map for ArcGIS, (2) the ortho mapping capability of ArcGIS Pro Advanced, and (3) the Ortho Maker app included with ArcGIS Enterprise. Read on to get an overview of all three solutions, and to figure out which one is best for your application.

Drone2Map for ArcGIS

For individual GIS users, Drone2Map is an easy-to-use, standalone app that supports a complete drone-processing workflow.

Drone2Map includes guided templates for creating orthorectified mosaics and digital elevation models. It’s also the only ArcGIS product that creates 3D products from drone imagery, including RGB point clouds and 3D textured meshes. Once you’ve processed your imagery, it’s easy to share the final products—2D web maps and 3D web scenes can be easily published on ArcGIS Online with a single step. ArcGIS Desktop isn’t required to run Drone2Map, but products created with Drone2Map are Desktop-compatible. That’s important, because it gives you the option to use ArcGIS Pro as an image management solution, or to serve your imagery products as dynamic image services using ArcGIS Image Server.

Ortho mapping capability of ArcGIS Pro Advanced

For GIS professionals, the ortho mapping capability of ArcGIS Pro Advanced enables you to create orthomosaics and digital elevation models from drone images (as well as from modern aerial imagery, historical film, and satellite data) in the familiar ArcGIS Desktop environment.

There are added benefits to processing your drone imagery in ArcGIS Pro. For users with very large imagery collections, Pro’s image management capabilities are especially valuable. Managing drone imagery using mosaic datasets makes it easy to query images and metadata, mosaic your imagery, and build footprints. Image management and processing workflows in ArcGIS Pro can also be automated using Python or Model Builder. Finally, sharing your imagery is straightforward. While you can publish your products to ArcGIS Online, you can also use ArcGIS Pro in conjunction with ArcGIS Image Server to publish drone products as dynamic image services.  

Ortho Maker app in ArcGIS Enterprise 10.6.1+

For ArcGIS Enterprise users, the Ortho Maker app offers a solution for organizations with multiple users who want simple, web-based workflows to create orthomosaics and DEMs from drone imagery.

Ortho Maker provides an easy-to-use web interface for uploading drone imagery and managing the ortho mapping workflow, while behind the scenes it uses the distributed processing and storage capability of Enterprise and ArcGIS Image Server to quickly process even very large collections of drone imagery. (That also means it requires ArcGIS Image Server configured for raster analysis.) The ArcGIS API for Python can be used to automate the ortho mapping process. Sharing Ortho Maker products is virtually automatic—they become imagery layer items accessible in your Enterprise portal, easily shared with users throughout your organization.

What do typical users say?

Next steps

Now that you have a better idea which solution makes sense for your application, it’s time to take one for a test drive. Drone2Map offers a free 15-day trial, plus a hands-on Learn lesson to get started. You can try ArcGIS Pro Advanced free for 21 days, and read more about getting started with ortho mapping for drone imagery.  For users with Enterprise 10.6.1+ and raster analysis enabled, Ortho Maker is included—find out how to get started.  Other Enterprise users should contact their administrator to see about getting access. If you still have questions, contact Esri for more product information.