Geospatial Analytics When, Where, and How You Need Them

Geospatial Analytics When, Where, and How You Need Them

1/5/2017

Let’s say you need to generate a large-scale information product. Maybe an impervious surface map or land use/land cover map. You’ll need high-powered analytics, access to data, and the computing power to chew through it all. But it’s a short term project that will wrap up in a month, maybe two.

CloudEO and Harris have partnered to offer ENVI in the Cloud; a cloud-deployed version of desktop ENVI. Just log-in and start working – it’s that easy!


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Machine Learning: Automate Remote Sensing Analytics to Gain a Competitive Advantage

Machine Learning: Automate Remote Sensing Analytics to Gain a Competitive Advantage

1/4/2017

Wondering how you can use machine learning, and more specifically deep learning technologies, to get a jump on the competition? This webinar will provide a brief, high-level overview of machine learning and its applications before delving into Harris’ five year head start developing deep learning technologies that are being deployed today. Harris has applied…


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FEATURED WEBINARS

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Geospatial Analytics When, Where, and How You Need Them

1/5/2017

Let’s say you need to generate a large-scale information product. Maybe an impervious surface map or... more »

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Machine Learning: Automate Remote Sensing Analytics to Gain a Competitive Advantage

1/4/2017

Wondering how you can use machine learning, and more specifically deep learning technologies, to get a... more »

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

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

Author: Abby Lehman/Wednesday, May 28, 2014/Categories: Webinars, IDL Webinar, ENVI Webinar

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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 custom LiDAR analytics. We will cover the following topics:

  • Build an ENVI LiDAR project, prompting a user for LiDAR input files in either .las or .laz formats
  • Automatically process the LiDAR input data to generate a DEM, DSM, 3D buildings, trees, and power lines
  • Process the data to classify the point cloud
  • Review a sample extension to annotate the highest point in a point cloud
  • Review a custom extension that can be implemented to extract building footprints from low resolution LiDAR data
  • Run an IDL extension in batch mode to associate NDVI values from a multispectral image to point-cloud LiDAR data

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