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Use deep learning for feature extraction and classification

For a human, it's relatively easy to understand what's in an image—it's simple to find an object, such as a car or a face; to classify a structure as damaged or undamaged; or to visually identify different land-cover types. However, it can become tedious to do this at scale. This is where machine learning comes in. With machine learning, you can use and automate this task to solve real-world problems. To accomplish this, ArcGIS implements deep learning technology to extract features in imagery to understand patterns—such as detecting objects, classifying pixels, or detecting change—in different data types and modalities.

Deep learning is a type of machine learning that can be used to detect features in imagery. It uses a neural network—a computer system designed to work like a human brain—with multiple layers. Each layer can extract one or more unique features in an image. Processing is often distributed to perform analysis in a timely manner.

To implement deep learning in your workflow, you can use or fine-tune pretrained deep learning packages or train a deep learning model using various ArcGIS deployments. ArcGIS also integrates with third-party deep learning frameworks to extract features from single images, imagery collections, point clouds, or videos.

Deep learning workflows in ArcGIS follow these steps:

  1. Generate training samples of features using editing tools in ArcGIS. These tools use GPU processing to perform analysis promptly.
  2. Use those training samples to train a deep learning model using ArcGIS Pro, ArcGIS Image Server for ArcGIS Enterprise, or ArcGIS API for Python.
  3. Using the resulting deep learning model, run the inferencing tools in ArcGIS Pro (in a desktop environment), ArcGIS Image Server for ArcGIS Enterprise (to scale the processing), ArcGIS Online (in a SaaS environment), or ArcGIS API for Python (in a developer environment) to extract specific features in the imagery.

Explore the following resources to learn more about feature extraction using deep learning in ArcGIS. (Not sure where to start? Look for the star by Esri's most helpful resources.)

To perform deep learning using feature extraction, you need the ArcGIS Image Analyst extension for ArcGIS Pro. To use the pretrained deep learning models online, you need ArcGIS Image for ArcGIS Online. To perform deep learning using distributed processing, you need ArcGIS Enterprise with ArcGIS Image Server configured for raster analytics.

Imagery Workflows resources

Review the community-supported tools and best practices for working with and automating imagery and remote sensing workflows:

ArcGIS Help

Review the following links on reference materials for ArcGIS products:

ArcGIS blogs, articles, story maps, and technical papers

Review the following supplemental guidance about concepts, software functionality, and workflows:


Review the following Esri-produced videos that clarify and demonstrate concepts, software functionality, and workflows:

Training and tutorials

Review the following guided lessons and tutorials based on real-world problems and key ArcGIS skills:

ArcGIS Solutions

Review the following industry-specific configurations for ArcGIS:

  • The 3D Basemaps solution, which streamlines the creation and maintenance of a 3D basemap, now includes a deep learning model for tree point classification from lidar.

Developer resources

Review the following resources and support for automating and customizing workflows:

Esri Community

Use the online imagery community to connect, collaborate, and share experiences:

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