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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!



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 >

NV5 at ESA’s Living Planet Symposium 2025

NV5 at ESA’s Living Planet Symposium 2025

9/16/2025

We recently presented three cutting-edge research posters at the ESA Living Planet Symposium 2025 in Vienna, showcasing how NV5 technology and the ENVI® Ecosystem support innovation across ocean monitoring, mineral exploration, and disaster management. Explore each topic below and access the full posters to learn... Read More >

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

9/8/2025

Geohazards such as slope instability, erosion, settlement, or seepage pose ongoing risks to critical infrastructure. Roads, railways, pipelines, and utility corridors are especially vulnerable to these natural and human-influenced processes, which can evolve silently until sudden failure occurs. Traditional ground surveys provide only periodic... Read More >

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Order of operations in an expression

Anonym
Given a floating-point array x, which of these two statements:
IDL> y1 = 2.0 * x / 3.0
IDL> y2 = x * (2.0 / 3.0)
executes faster, the first or the second? Note that both statements produce the same result to within floating-point precision. We can test the execution time of IDL code with the built-in SYSTIME function. Here’s an example program that demonstrates a technique for doing so:
pro test_orderofoperations
   compile_opt idl2

   n_iter = 1e2
   x = findgen(1e6)

   t0 = systime(/seconds)
   for i=1, n_iter do y1 = 2.0 * x / 3.0
   t1 = systime(/seconds) - t0
   print, t1, format='("Execution time for [2.0 * x / 3.0]:   ",f5.2," s")'

   t0 = systime(/seconds)
   for i=1, n_iter do y2 = x * (2.0 / 3.0)
   t2 = systime(/seconds) - t0
   print, t2, format='("Execution time for [x * (2.0 / 3.0)]: ",f5.2," s")'

   print, t1/t2, format='("Ratio : ",f5.2)'
end 
Note that I chose a smallish array size to avoid memory issues (this is another performance topic) and I looped over the statements many times to attempt to average out any transient effects. Here’s the result from running this program on my laptop (YMMV):
IDL> test_orderofoperations
Execution time for [2.0 * x / 3.0]:    0.77 s
Execution time for [x * (2.0 / 3.0)]:  0.30 s
Ratio :  2.58
Why does the execution time of these statements differ? In the first statement, there’s a multiplication and a division. Since both are at the same level in the operator hierarchy, IDL works from left to right, calculating (2.0 * x), then dividing the result by the value 3.0. The key here is that the expression (2.0 * x) is an array operation, so under the hood, at the C level of IDL, every element of the array x is multiplied by the value 2.0. The result is a new array, held temporarily in memory. Every element of this array is then divided by the scalar value 3.0. Compare this order of operation with that in the second statement. Here, because of the parentheses, the scalar operation (2.0 / 3.0) is performed first, with another scalar as a result. This scalar is then multiplied, element-by-element, with the array x. So, the difference between these two statements is that the first uses two array operations, whereas the second uses only one. The lesson is then: group scalar operations in an expression. This is a simple performance tweak that will help your IDL code run faster. For more information on code performance, including demonstrations of techniques similar to this, see Mike Galloy's book, Modern IDL. We also experiment with several performance techniques in our Scientific Programming with IDL course.
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