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NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

12/3/2025

Large commercial SAR satellite constellations have opened a new era for persistent Earth monitoring, giving analysts the ability to move beyond simple two-image comparisons into robust time series analysis. By acquiring SAR data with near-identical geometry every 24 hours, Ground Track Repeat (GTR) missions minimize geometric decorrelation,... Read More >

Empowering D&I Analysts to Maximize the Value of SAR

Empowering D&I Analysts to Maximize the Value of SAR

12/1/2025

Defense and intelligence (D&I) analysts rely on high-resolution imagery with frequent revisit times to effectively monitor operational areas. While optical imagery is valuable, it faces limitations from cloud cover, smoke, and in some cases, infrequent revisit times. These challenges can hinder timely and accurate data collection and... Read More >

Easily Share Workflows With the Analytics Repository

Easily Share Workflows With the Analytics Repository

10/27/2025

With the recent release of ENVI® 6.2 and the Analytics Repository, it’s now easier than ever to create and share image processing workflows across your organization. With that in mind, we wrote this blog to: Introduce the Analytics Repository Describe how you can use ENVI’s interactive workflows to... Read More >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... Read More >

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

10/13/2025

On July 24, 2025, a unique international partnership of SaraniaSat, NV5 Geospatial Software, BruhnBruhn Innovation (BBI), Netnod, and Hewlett Packard Enterprise (HPE) achieved something unprecedented: a true demonstration of cloud-native computing onboard the International Space Station (ISS) (Fig. 1). Figure 1. Hewlett... Read More >

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Using a HASH with FOREACH

Anonym

The FOREACH loop is a handy tool in IDL, allowing a user to iterate over each element in an array without having to determine the size. To start the loop, the syntax is:

foreach element, variable, index do

The first argument "element" does not have to be defined beforehand, and is the variable that holds the element of the loop for the current iteration. The second argument is the IDL variable that contains the array, list or, hash you want to loop over. The third argument "index" is optional, and contains the index for the current loop. When using a list or array, this third argument is a good way to keep track of the iteration you are currently on.

With a HASH however, the third optional argument will be the key for the current key/value pair, and the first argument, "element" will be the value. This is useful for things like titling your graphics, since the key can be a string. This example below illustrates how to do this.

x = findgen(21)/10 - 1.0

i = 1

data = orderedhash()

data['linear'] = x

data['squared'] = x^2

data['cubed'] = x^3

p = list()

foreach this_data, data, type do begin

  p.add, plot(x, this_data, LAYOUT=[3,1,i], TITLE=type, THICK=3, /current)

  i++

endforeach

(p[0]).COLOR= 'red'

(p[1]).COLOR= 'green'

(p[2]).COLOR= 'blue'

The resulting graphic looks like this:

There a couple things to note in this example:

First, since the third optional argument is a key for the HASH, if you need numeric index for the loop, it has to be taken care of manually by incrementing a value (I used "i") in the loop.

Second, the use of an ORDEREDHASH is important. Without this, the plots will not necessarily be in the order expected. When I ran this with a regular HASH, the graphic came out as "squared" on the left, "linear" in the middle, and "cubed" on the right.

Finally, I used a list to hold the object returned by plot,which enabled me to change the color of the plots after the graphic initialized. The LIST::ADD method adds the plots to the list one at a time.

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