Latest Contributions by VinayViswambharan

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 ... View more

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 ... View more

ArcGIS Pro 2.3 is the largest release of ArcGIS Pro to date, featuring more new and updated features than any previous release. Take advantage of the new imagery and remote sensing capabilities in ArcGIS Pro 2.3 to improve your workflows for image visualization and exploitation, analysis, map production, and management. Here are just some of the imagery and remote sensing-related features you’ll find in ArcGIS Pro 2.3:   Image Management ArcGIS Cloud Storage Connection files ArcGIS is powered by a scalable and optimal information model for managing large volumes of imagery: the mosaic dataset. ArcGIS Pro 2.3 now enables you to make direct connections to cloud stores and to work with imagery natively as image layers in your project. The new ArcGIS Cloud Store connection (ACS) file allows you connect to cloud stores directly, browse buckets/folders and access imagery in your project. AWS, Azure, and Alibaba are the currently supported cloud stores.                                                                                Direct connection to your cloud store     Map Production Ortho mapping – processing aerial images User experience enhancements improve the way you prepare aerial and scanned imagery for analysis. Frame and Camera table generation is now a guided step in the Ortho Mapping Workspace Wizard, as opposed to a manual process. Interior orientation for aerial scanned imagery can now be performed in ArcGIS Pro 2.3, and the UX supports multiple fiducial templates, which come in handy when you have varying quality of fiducials across the image.                                                                Enhanced tools to prepare aerial and scanned imagery for analysis   Analysis Deep Learning and Image Classification For years, ArcGIS has enabled you to classify remote sensing imagery using statistical and machine learning classification methods. Enhancing these capabilities at 2.3, we introduce the Deep Learning toolset, which enables you to incorporate deep learning models directly into your GIS workflows. -    Object detection and pixel classification tasks are now supported with three new geoprocessing tools that use deep learning models generated by popular deep learning frameworks. -    ArcGIS Pro now supports popular deep learning frameworks including TensorFlow, CNTK and Keras. -    Updates to the Export Training Data For Deep Learning geoprocessing tool simplify the creation of training samples and metadata for input to deep learning frameworks.                                                 Raster Functions ArcGIS Pro 2.3 expands the list of raster functions with additional statistical operators and distance functions, enabling you to design and run complex custom image processing algorithms as raster function chains. Seven new functions are available in this release:   ArcGIS Pro – Basic ArcGIS Spatial Analyst Extension ArcGIS Image Analyst Extension Lookup Corridor – Wildlife corridor assessments Focal statistics – modeling such as fire growth, filtering data errors, anomaly detection, edge detection, land surface ruggedness calculation Region Group – forestry applications Path Distance Allocation – Determining first responder management areas     Path Distance – Cross country mobility applications by the military     Path Distance Backlink – determine routes     Visualization Motion Imagery (Full Motion Video) Motion Imagery, added in ArcGIS Pro 2.3, is the evolution of the Full Motion Video (FMV) add-In for ArcMap. Motion Imagery enables you to manage and interact with video that has embedded geospatial metadata. At 2.3, you can create and edit features in the video player and update existing feature classes, display GIS layers (e.g. building polygons, hydrology features, etc.) in the video player and the map view, and extract metadata from both live video feeds and archived videos.                                                                                                  Motion imagery (Full Motion Video) in ArcGIS   Most of the functionality available in the FMV add-in for ArcMap is now fully integrated into Pro 2.3.  FMV now takes advantage of ArcGIS Pro’s powerful architecture, 3D scenes, asynchronous processing, smoother video playback, and better performance.   Additional resources Get an overview of what’s new in ArcGIS Pro 2.3 in general. Learn more about the Image Analyst extension and how to get it. Learn how to get started with the Image Analyst extension. Try out new features with sample data: - Sample data for a stereo tutorial - Sample data for trying Motion Video ... View more