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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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Data Structure Analysis

Anonym

One of the main questions anybody using a programming language has to ask themselves is "what data structures should I be using?" This can be a complicated and difficult question as there are many trade-offs to consider. If it is desired to have a dynamic data type, IDL provides multiple options. In this analysis we will consider: dynamic arrays, lists, hashes, and ordered hashes and their ability to insert and delete elements. To start, let's review our data structures. Dynamic arrays are based on the ability for IDL arrays to resize themselves. For example:

array = [1,2,3,4]               ; Declaration

array = [array, 5]              ; Insert

array = [array[0:1],array[3:*]] ; Remove

 

Lists use the IDL object LIST:

list = LIST(1,2,3,4)    ; Declaration

list.add,5              ; Insert

list.remove, 2          ; Remove

 

Hashes use the IDL object HASH:

hash = HASH([1,2,3,4],[1,2,3,4]) ; Declaration

hash[5] = 5                      ; Insert

hash.remove, 2                   ; Remove

 

Ordered hashes use the IDL object ORDEREDHASH:

ohash = ORDEREDHASH([1,2,3,4],[1,2,3,4]) ; Declaration

ohash[5] = 5                             ; Insert

ohash.remove, 2                          ; Remove

 

For each data structure we will time how long it takes to insert and remove n elements (Note: the inserts/removals are done inside of a FOR loop, one insert/removal per iteration. This is done to simulate an application which expects a high degree of volatility in the use of their data structures. However, since IDL is a vectorized language, it is always best to try to group multiple operations into a single call). Please see the attached plots for the results of the runs. The results are what we would expect from a simple big-O analysis. Dynamic arrays are comparable for small input sizes, however, as soon as the size of the input grows, it becomes much faster to use a hash (either type) or a list. In terms of pure speed for any arbitrary input size, list  is the fastest. However, if you know your input bounded to a few elements, all of the proposed data structures can offer a similar performance.

Note: For this analysis, all the data structures had similar performance up to 10,000 elements. This is in no way a comprehensive test and the results may differ on your system. However, the rule of thumb I follow is, if your input is less than 10,000 elements choose the data structure which is the easiest for you to work with.

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