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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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Queuing ESE Tasks in a Loop

Anonym

When using desktop ENVI and IDL, it is useful to setup processing that you have to do many times in a batch script. The easiest way to do this is with an IDL for loop that does processing on one file at a time. However, for an instance running ENVI Services Engine (ESE), it is best to call each task on an individual file. This makes the processing more robust, as an error in processing on a single file will not halt processing for every file. This leaves the question though - how would one loop over every file that needs processing?

One solution is to use IDL to create a loop that goes over each file, then launch every task that needs to be preformed. This builds up a list of tasks queued for execution. To do this, create a list of input files and output files much like you would in a batch process, then call the HTTP address required to submit the processing request to ESE one file at a time.

This can be done using IDL's built in HTTP client, IDLNetURL. As an example, if the ESE process to be called is an asynchronous task named "apply_color_table", the full HTTP call to start the task will be:

http://(host):8181/ese/services/AsyncService/apply_color_table/submitJob?inFile=file&outputFile=file

where (host) is the name or IP address of the server, and the keywords "file" are the actual input and output file names. One way to set up this call so that it occurs on multiple files is as follows, where inFiles is a variable containing all of the files to be processed.

 oURL = Obj_New('IDLnetUrl')

 oUrl.SetProperty, URL_SCHEME='http'

 oUrl.SetProperty, URL_HOST = !SERVER.HOSTNAME

 oUrl.SetProperty, URL_PORT='8181'

 oUrl.SetProperty, $

   URL_PATH='ese/services/AsyncService/apply_color_table/submitJob'

 foreach inFile, inFiles do begin

   oUrl.SetProperty, URL_QUERY='inputFile=' + inFile + $

     '&outputFile=' + 'ct_' + inFile

   result = oURL.Get()

   json = JSON_Parse(result)

   print, 'status file: ' + json['jobStatusURL']

 endforeach

The task that is called will contain the processing, in this case the call that would be made using IDL would be:

apply_color_table, inputFile=inputFile, outputFile=outputFile

This procedure will take in the input file and output file names as arguments, which are passed in through the queuing script.

Once the queuing script completes, ESE will begin running through the tasks one at a time, distributing the workload across CPUs and across any workers that are set up.

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