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Unlocking Information from Imagery in ArcGIS

From data and detection to action in the field: using Imagery and Deep Learning to get the job done

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Why Imagery?

Remotely sensed imagery contains a myriad of valuable information, if you know how to find it. Satellite data, aerial photography, and drone imagery are increasingly used in commercial industries, scientific research, emergency management, resource management, reconnaissance and entertainment. You commonly see examples of image analysis in newspaper or scientific journal articles, and find a wide array of uses and applications that show why remotely sensed imagery is so important. 

At first glance, imagery is critical because it allows us to inspect areas of interest that are hard to get to because they are too far away or difficult to access. For example, the spreading boundaries of wildfires are difficult and dangerous to assess from the ground, and aerial or high-resolution satellite imagery can help. 


 A Sentinel-2 satellite captured a blazing wildfire as it burned through the High Plains in Oklahoma on April 13, 2018


But visual inspection of imagery is just the tip of the iceberg. Prompt location-based insights from imagery are important for many industries. To make it possible to extract information from imagery, Esri has blended remote sensing and GIS technologies to bridge the gap between big data, machine learning, and getting answers. 

 Visual inspection of imagery is just the tip of the iceberg.

With image classification tools, we can identify features from imagery with software, removing the impacts of human error and increasing the chances of finding objects that are hard to see with the human eye. We can use the cloud to share information for even more effective data collection. With the ArcGIS Platform, we can go from remotely sensed imagery to an efficient task-oriented workflow managed in the cloud.  

Doing it all with ArcGIS

Esri offers an end-to-end imagery platform that incorporates image management, data preparation and training, deep learning model integration, and operational workflows that can be shared within your organization or to the public. You can even leverage distributed processing power with Raster Analytics in ArcGIS Enterprise to manage and analyze large amounts of imagery quickly and share it in the cloud.

Esri's End-to-End Geospatial Artificial Intelligence (GeoAI) Workflow

We'll walk you through two "Platform Stories" to show you how we incorporated Esri's imagery workflows with the rest of the ArcGIS Platform for efficient, action-oriented problem solving. 

Platform Stories

Pipeline Encroachment in Egypt


Click on the "Pipeline Encroachment" tab at the top of the story map to find out how we did it. 

Coconut Palm Health in Tonga

Click on the "Coconut Palm Health" tab at the top of the story map to see how we did it. 

Interested in deep learning? Read the Deep Learning in ArcGIS section of this Story Map to see what's coming in ArcGIS Pro 2.3. 

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