It’s a complete training guide to help you get started with complex image processing workflows. It includes a checklist of tutorials, videos and lessons along with links to additional help topics.
This guide is useful to anyone interested in learning how to work with the powerful image processing and visualization capabilities available with the ArcGIS Image Analyst. Complete the checklist provided in the guide and you’ll get hands on experience with:
Setting up ArcGIS Image Analyst in ArcGIS Pro
Extracting features from imagery using machine learning image classification and deep learning methods
Did you know there is a huge repository of powerful Python Raster Functions that you can use for raster analysis and visualization? On theEsri/raster-functions repository on GitHub, you can browse, download, and utilize customized raster functions for on-the-fly processing on your desktop or in the cloud.
What are Python raster functions, you ask?
A raster function is a sneaky way to perform complex raster analysis and visualization without taking up more space on your disk or more time in your day, with on-the-fly processing. Asingle raster functionperforms an analysis on an input raster, then displays the result on your screen. No new dataset is created, and pixels get processed as you pan and zoom around the image. You can connect multiple raster functions in araster function chain and you can turn it into araster function templateby setting parameters as variables.
APython raster functionis simply a custom raster function.A lot of raster functionscome with ArcGIS out-of-the-box, but if you don’t find what you’re looking for or you want to create something specific to your needs, you can script your own with Python.
The Topographic C Correction function, written by Gregory Brunner from the St. Louis Regional Services office, essentially removes the hillshade from orthophotos. As you can imagine, imagery over mountainous areas or regions with rugged terrain can be difficult to classify accurately because pixels may belong to the same land cover class but some fall into shadow due to varying slopes and aspects. With the topographic correction function, you can get a better estimate of pixel values that would otherwise be impacted by hillshade. The result is a sort of flattening of the image, and it involves some fairly complex math.
Why should you care?
Okay, so now you know there’s a repository of Python raster functions. What’s next?
Explore the functions you may need. Some of the functions on the repository were written for specialized purposes and aren’t included with the ArcGIS installation, such as the Topographic C Correctionfunction (above) or the Linear Spectral Unmixing function [contributed by Jacob Wasilkowski, also from the St. Louis Esri Regional office].
Try writing your own Python raster function. A lot of what’s on the GitHub repository is already in the list of out-of-the-box raster functions, but you can open the Python scripts associated with each one, customize them, and save them as new Python raster functions. This can be a great learning tool for those new to the process.
Watch the repo for more functions. There are currently over 40 functions listed, and we are continually adding more.
Contribute! Have you written something that you can share with the broader community? Do you have ideas for cool raster functions? Add to the conversation by commenting below!
To easily access all the Python Raster Functions in the GitHub repository, simply click the Clone or Download button on the repository code page, and choose to download the raster functions as a ZIP file.
Extract the zip folder to your disk, then use this helpful Wiki to read about using the Python Raster Functions in ArcGIS Pro.
ArcGIS Enterprise configured for Raster Analytics enables large and small organizations to distribute and scale raster processing, storage and sharing to meet requirements for unique projects. This flexibility and elasticity also allows you to pursue projects that were previously out of reach due to hardware, software, personnel, or cost constraints. An overview of Raster Analytics concepts and advantages is described in the article Imagery Superpowers – Raster analytics expands imagery use in GIS.
To help you become familiar with the benefits of Raster Analytics, Esri is offering a new Learn Lesson for ArcGIS Enterprise users. The lesson guides you through the process of configuring your Enterprise system for Raster Analytics, shows you how to use raster processing tools and functions to assess potential landslide risk associated with wildfire. The analysis is run on your distributed processing system, and the results are published to your Enterprise portal for ease of sharing across your organization. The lesson is a practical guide for implementing a Raster Analytics deployment, and demonstrating how standard ArcGIS Pro tools and functionality can be used to run distributed processes behind your firewall and in the cloud, and shared with stakeholders across your enterprise. Check out this story map, which gives you a more detailed overview of what the lesson involves.
Ready to try it out? If you want to extend your capabilities with Raster Analytics for increased productivity, test out the lesson and see why users are excited about the opportunity to address demanding projects in a more effective and efficient manner.
Imagery can add valuable information and context to a wide array of GIS projects. For example, you can detect impervious surfaces for storm water management, map and manage riparian corridors, or track what’s changing in your county. Sometimes, though, incorporating imagery into your GIS can feel overwhelming—how can your system handle that much data?
Enter raster analytics, a distributed processing, storage, and sharing system designed to quickly process large collections of aerial, drone, or satellite imagery, then extract and share meaningful information for critical decision support. Raster analytics can be run locally, but you can also pair it with distributed cloud computing to maximize efficiency. Image processing and analysis jobs that used to take days or weeks can be completed in minutes or hours, bringing imagery projects that were impossibly large or daunting within reach.
Raster analytics leverages ArcGIS Enterprise, expanded with ArcGIS Image Server configured for distributed raster analysis, to integrate the components of the raster analytics system to support scalable, real-world workflows
What can raster analytics do?
By leveraging ArcGIS Enterprise with ArcGIS Image Server, raster analytics enables you to:
Quickly process massive imagery or raster datasets in a scalable environment
Execute advanced, customized raster analysis
Share results with individuals, departments, and organizations within or outside your enterprise
The scalable environment of raster analytics empowers you to perform computationally intensive image processing that would otherwise be out of reach or cost-prohibitive. When implemented on-site, raster analytics uses distributed processing to improve efficiency. You can also maximize efficiency by exploiting cloud platforms such as Amazon Web Services or Microsoft Azure, which allow you to dynamically increase or reduce your capacity based on the size and urgency of your projects. Either implementation can save you time, money, and resources.
Raster analytics uses all the advanced image processing and analysis capabilities of ArcGIS Pro to maximum advantage. Built-in raster functions cover preprocessing, orthorectification and mosaicking, remote sensing analysis, and an extensive range of math and trigonometry operators, while your custom functions can extend the platform’s analytical capabilities even further.
Raster analytics is also designed to streamline collaboration and sharing. Users across your enterprise can contribute data, processing models, and expertise to your imagery project, then share results with individuals, departments, and organizations in your enterprise.
The Chesapeake Conservancy, working with the University of Vermont and WorldView Solutions, was tasked by the Chesapeake Bay Program to produce one-meter-resolution land cover maps covering 100,000 square miles of the Chesapeake Bay watershed. These high-resolution land cover maps, which classify natural and man-made landscape features, are crucial for supporting watershed and storm water management, conservation, and for reducing pollution into the bay.
To produce this essential dataset, the Chesapeake Conservancy needed to process over 20 terabytes of raster data and categorize it into twelve land cover types. This project took a daunting 18 months to complete using their local machine resources. As a result, Chesapeake Conservancy is now working with raster analytics in the cloud to make this timeline more efficient and cost-effective going forward.
As a proof of concept, they used raster analytics to produce a persistent one-meter land cover dataset of Kent County, Delaware (798 square miles). The Kent County project—comprised of more than 30GB and 3.8 billion pixels of raster data—ran on a ten-machine cluster, each with twenty cores, and completed in less than 5 minutes. This same job took days to to process on their local machines.
The Chesapeake Conservancy is now engaged in reprocessing the entire Chesapeake watershed to benchmark time and cost savings using raster analytics for the project. Using raster analytics for projects in the future will mean that the Chesapeake Conservancy can accomplish ambitious projects in a timely and cost-effective manner, without having to spend resources to acquire, configure, and maintain a large computing and storage infrastructure.