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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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IDL's Lambda Function Examples

Anonym

The Lamba function concept was introduced in IDL 8.4. I found that using a Lambda function can result in fewer lines of code and save coding time. However, IDL's Lambda function does not create the most optimal code. Although in my experience, the same is also true for other languages. While the syntax can look very short and concise, the execution time and memory use is not optimal.

Here are some examples of using a Lambda function in conjunction with Map, Filter, Reduce, as well as the FOR loop versions for comparison.

First create some data to use for the remainder of the examples.

 ;Create some test data in a string array of 1400 lines

 ;containing various comma separated numbers

 str = strjoin(strtrim(fix(bindgen(8,1400)),2),',')

This is an example of splitting all the strings on the commas using Map.

 ;split on comma and place the result in another array

 a = str.map(lambda(x:x.split(',')))

 

 ;exactly the same result using a loop

 b = strarr(8,1400)

 for j=0,1399 do b[0,j] = str[j].split(',')

 

The next example is using Filter to return only strings that have a '10' in the 3rd column:

 ;Filter to only keep lines with '10' in 3rd column

 c = str.filter(lambda(x:(x.split(','))[2] eq '10'))

 

 ;Use For and list

 d = list()

 for j=0,1399 do if (str[j].split(','))[2] eq '10' then d.Add, str[j]

 d = d.ToArray()

The final example uses Reduce to return the maximum of the individual totals for each string:

 ;Use Reduce to find the maximum total

 m = str.reduce(lambda(x,y:isa(x,/string)?total(long(x.split(',')))>total(long(y.split(','))):x>total(long(y.split(',')))))

 

 ;Using for loop

 maxtot = -999

 for j=0,999 do begin

   tot = total(long(str[j].split(',')))

   if tot gt maxtot then maxtot = tot

  endfor

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