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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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The merits of an example main program

Anonym

When I write a new routine in IDL—a procedure or a function or a class—I like to include a main program at the bottom of the file. I use the main program to

  • demonstrate the calling syntax of the routine
  • give an example of how the routine is used
  • define a simple unit test (or tests)

I first saw this idea used in Python and I’ve copied it for my work in IDL. For example, here’s the full code listing for a simple function, FLATTEN (which converts a multidimensional array into a vector) along with an example main. The programs are saved in the file flatten.pro, in a directory in IDL’s path.

; docformat = 'rst' 
;+ 
; A convenience routine that flattens/linearizes a 
; multidimensional array. 
; 
; :params: 
;  x : in, required, type=any array 
;   An array of any type to be converted to a vector. 
; 
; :author: 
;  Mark Piper, VIS, 2011 
;-
function flatten, x
   compile_opt idl2

   nx = n_elements(x)
   return, nx gt 0 ? reform(x, nx) : 0
end

; Example
a = indgen(5, 7)
b = flatten(a)
c = reform(a, n_elements(a))
help, a, b, c
print, 'Equivalent results? ', array_equal(b, c) ? 'Y' : 'N'
end 

By examining the main program, you can see how FLATTEN works; here, it’s used to convert a 5 x 7 array into a 35-element vector. To use the main program as an example, I execute it from the command line with the .run executive command:

IDL> .r flatten
% Compiled module: FLATTEN.
% Compiled module: $MAIN$.
A               INT       = Array[5, 7]
B               INT       = Array[35]
C               INT       = Array[35]
Equivalent results? Y

The .run command compiles both routines and executes the main program. We’d get the same behavior from the Run button (or the F8 keyboard shortcut) in the IDL Workbench. Note that—and this is important—the calling mechanism still resolves the FLATTEN function (by itself) correctly:

IDL> .reset
IDL> x = indgen(2,3)
IDL> print, x
      0       1
      2       3
      4       5
IDL> y = flatten(x)
% Compiled module: FLATTEN.
IDL> print, y
      0       1       2       3       4       5

This means that (as intended) FLATTEN can be used as a library routine independent of its example main program. I find this technique of including an example main to be especially useful with functions, which won’t execute with the Run button on the Workbench. (This may be a topic for another post, where I'd like to argue for an implicit redirect to !null for functions; e.g., FLATTEN could be called like this:

IDL> flatten(x)

without throwing a syntax error.) Note: ENVI 5 was released this week. It has a new UI and a new API. The API still uses IDL, but with an object-oriented interface. Though I'm not a heavy ENVI user, I'd like to show some examples of using the new API over the next few weeks and months.

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