Attend a live webinar or watch a previously recorded webinar to learn about specific solutions that our products provide geospatial data users.


A Deeper Dive into SAR: Agriculture and Land Surface Deformation

In this third and last part of our SAR webinar series, we are focusing on the background, processing, and... more »

Quality is Everything. Ensure...

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Custom LiDAR Analysis: Using the...

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Extending ENVI LiDAR to Create...

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Using GPU's to Accelerate Orthorectification, Atmospheric Correction, and Transformations for Big Data (Recorded)


Join Amanda O'Connor as she discusses her presentation, "Using GPU's to Accelerate Orthorectification, Atmospheric Correction, and Transformations for Big Data," originally presented at the 2013 AGU Conference. Read More >

Custom LiDAR Analysis: Using the ENVI LiDAR API for IDL (Recorded)


In this webinar, we’ll take a look at the ENVI LiDAR user interface to become familiar with out-of-the box functionality. We will also review custom extensions for ENVI LiDAR, developed in IDL. A big advantage of ENVI LiDAR is its extensibility with IDL, opening a world of potential for... Read More >

Image Analysis for Flood Disaster Response (Recorded)


In the aftermath of the recent flooding in Colorado, Exelis Visual Information Solutions held two workshops for state, county, and city GIS analysts who were responding to the flood. Due to interest from outside the Colorado region, we will be holding a one-time webinar that covers the... Read More >

ENVI for Environmental Applications: Coastal Monitoring in the Arctic (Recorded)


Learn how ENVI provides to accurately map coastal erosion using Landsat archive imagery with ENVI and ArcMap ™ . Read More >

What's New in IDL 8.2 (Recorded)


This webinar will overview the new features in IDL 8.2. Read More >

Introducing ENVI 5 (Recorded)


ENVI 5 is the next generation of image analysis, bringing you a streamlined, high performance interface, a suite of new image processing workflows, and a display built to excel at handling increasingly large, multi-modal data sets. Read More >