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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!



New ENVI Agent, IDL Agent, and GeoAgent Quick Guides

New ENVI Agent, IDL Agent, and GeoAgent Quick Guides

6/9/2026

The recent release of ENVI® Agent, IDL® Agent, and GeoAgent™ revolutionize how users interact with geospatial software. These agentic AI applications act as partners to plan, simplify, and execute complex workflows. Knowing where to start can be challenging for new users. To this end, we developed three new quick guides to... Read More >

Introducing NISAR Data Support

Introducing NISAR Data Support

6/5/2026

The release of ENVI® SARscape 6.3 in April 2026 includes preliminary support for NASA-ISRO SAR (NISAR) data. The NISAR mission is a joint Earth-observing satellite project between NASA and the Indian Space Research Organization designed to monitor changes in the planet’s land and ice surfaces using advanced radar imaging. It... Read More >

Monitoring Illegal Mining in the Amazon: Turning Persistent Data Into Actionable Insight

Monitoring Illegal Mining in the Amazon: Turning Persistent Data Into Actionable Insight

5/28/2026

Illegal mining over decades has constituted one of the most persistent and complex socio-environmental problems in the Brazilian Amazon. In recent years, with the increasingly intensive use of mechanized extraction, the associated environmental impacts—such as deforestation, intense soil disturbance, river siltation, and mercury... Read More >

From Answers to Action: Why ENVI and IDL Agents Go Beyond General AI

From Answers to Action: Why ENVI and IDL Agents Go Beyond General AI

4/20/2026

As generative AI tools like Claude and Gemini continue to gain traction, many organizations are asking the same question: Can general purpose AI actually support real geospatial workflows, or does it stop at surface-level answers? That question was front and center in our recent webinar, Meet Your New Partners in Science: ENVI... Read More >

Mapping Earthquake Deformation in Taiwan With ENVI

Mapping Earthquake Deformation in Taiwan With ENVI

12/15/2025

Unlocking Critical Insights With ENVI® Tools Taiwan sits at the junction of major tectonic plates and regularly experiences powerful earthquakes. Understanding how the ground moves during these events is essential for disaster preparedness, public safety, and building community resilience. But traditional approaches like field... 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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