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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!



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 >

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

12/3/2025

Large commercial SAR satellite constellations have opened a new era for persistent Earth monitoring, giving analysts the ability to move beyond simple two-image comparisons into robust time series analysis. By acquiring SAR data with near-identical geometry every 24 hours, Ground Track Repeat (GTR) missions minimize geometric decorrelation,... Read More >

Empowering D&I Analysts to Maximize the Value of SAR

Empowering D&I Analysts to Maximize the Value of SAR

12/1/2025

Defense and intelligence (D&I) analysts rely on high-resolution imagery with frequent revisit times to effectively monitor operational areas. While optical imagery is valuable, it faces limitations from cloud cover, smoke, and in some cases, infrequent revisit times. These challenges can hinder timely and accurate data collection and... Read More >

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 >

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Accessing Features Only Available to 32-bit IDL from 64-bit IDL

Jim Pendleton

Not all functionality available to IDL and ENVI in 32-bit mode is available in 64-bit mode, and vice versa.

There are multiple tables in our online documentation that list support for various platforms.

If you're on a 64-bit platform, you have the option of launching IDL in either 32- or 64-bit mode. But that doesn't really solve the problem.

For example, let's say you have a main application that executes in 64-bit IDL, but you want to have access to data in DXF-format files. If you attempt to create an instance of an IDLffDXF object that parses this file format, you'll get an error:

IDL> heart = obj_new('idlffdxf', filepath('heart.dxf', subdir = ['data']))
% OBJ_NEW: Dynamically loadable module is unavailable on this platform: DXF.
% Execution halted at: $MAIN$          

We could fire up a second command line or Workbench session of IDL in 32-bit to parse the file, but a more convenient method, and the way we would want to implement a solution within a compiled routine, is through an IDL_IDLBridge object. There's a special keyword named OPS (technically, "out-of-process server") which allows us to set whether the bridge process should run in 32- or 64-bit mode.

Here, we'll start a 32-bit IDL process from our 64-bit IDL session.

IDL> b = idl_idlbridge(ops = 32)
% Loaded DLM: IDL_IDLBRIDGE.

Obviously, if you're on a 32-bit platform (still?!) you cannot simply create a 64-bit process via the magic of an IDL keyword.

We can construct a command to be executed in our 32-bit process to read the data.

IDL> command = "heart = obj_new('idlffdxf', filepath('heart.dxf', subdir = ['examples','data']))"
IDL> b->execute, command

Now we can proceed with an example from the documentation for the IDLffDXF::GetEntity method's documentation, transferring the data back to our main process for display.

IDL> b->execute,  "heartTypes = heart->getcontents()"
IDL> b->execute, "tissue = heart->getentity(heartTypes[1])"
IDL> b->execute, "connectivity = *tissue.connectivity"
IDL> b->execute, "vertices = *tissue.vertices"
IDL> vertices = b.getvar('vertices')
IDL> connectivity = b.getvar('connectivity')

Now that we have local copies in our 64-bit process of the vertices and connectivity list data from the 32-bit process, we can display the result.

IDL> poly = idlgrpolygon(vertices, poly = connectivity, style = 2, color = !color.red)
IDL> xobjview, poly
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