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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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What the *bleep* is IDL doing? Creating variables

Anonym

The IDL language has many features which allow for quick and simple programming.  For example, say you have an INT array and you need to append a value to it:

intarray = [intarray,42]

 

Quick.  Simple.  However, just because you can do this doesn't mean you should.  Let's examine what is happening in the statement:

intarray = [intarray,42]

 

First, IDL has to create a temporary array to hold intarray and the new value.  Then, IDL copies intarray into the temporary and adds 42 to the end of it.  Finally, it has to free the old intarray variable.  Creating a temporary variable is an expensive operation.  As such, you should always be mindful when operations will create temporary variables.  Take for example creating an integer array of fixed length with some initial value using array appending.

print, 'Creating array by appending'

tic

length=100000l

a=[]

for i=0l,length-1 do a=[a,42]

toc

 

Which completes in:

% Time elapsed: 1.4720001 seconds.

While the append functionality gives the flexibility to add an element to an array, the time to create the temporary makes this approach extremely impractical.  However, if we need a dynamic array which elements and be added and removed as needed, consider using LIST. 

print, 'Creating array by list'

tic

length=100000l

l=list()

for i=0l, length-1 do l.add, 42

a=l.toarray()

toc

 

Which completes in:

% Time elapsed: 0.15200019 seconds.

LIST's avoid duplicating the entire array every time you add an element.  This is especially important when you are dealing with dynamic data.  However, if we know how long our array should be we can use IDL's array creation functions to drastically increase performance. 

print, 'Creating array by preallocate'

tic

length=100000l

a=intarr(length,/nozero)

for i=0l, length-1 do a[i]=42

toc

 

Which completes in:

% Time elapsed: 0.0060000420 seconds.

In this case we can speed up our processing even more.  Since we are initializing each element of our array to the same value, we can improve our performance even more by using the MAKE_ARRAY function.

print, 'Creating array by vectorized solution'

tic

length=100000l

a=make_array(length,value=42)

toc

 

Which completes in:

% Time elapsed: 0.00000000 seconds.

There are many way to accomplish any given task in IDL.  However, each method has its unique advantages and disadvantages.  By understanding what you are trying to accomplish and what IDL has to do under the hood to perform an operation, you can dramatically decrease the time it takes your code to run.

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