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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!



Using ENVI and IDL Agents with Your Own API Keys

Using ENVI and IDL Agents with Your Own API Keys

6/22/2026

Earlier this year, we introduced the ENVI® Agent and IDL® Agent to bring intelligent, AI-driven automation to your geospatial and data science workflows. If you missed the launch, you can catch up on the full breakdown by watching our release webinar. Both agents are built upon GitHub Copilot, a powerful AI orchestration... Read More >

What We're Looking Forward to at Esri UC 2026

What We're Looking Forward to at Esri UC 2026

6/16/2026

Every year, the Esri User Conference brings together thousands of geospatial professionals to explore new technologies, share ideas, and learn how organizations are solving complex challenges with GIS. For many members of the NV5 team, attending Esri UC is an annual tradition. Some have attended for more than 15 years. Others will be... Read More >

New ENVI Agent, IDL Agent, and GeoAgent Quick Guides

New ENVI Agent, IDL Agent, and GeoAgent Quick Guides

6/9/2026

The recent release of ENVI® Agent, IDL® Agent, and GeoAgent™ revolutionize how users interact with geospatial software. These agentic AI applications act as partners to plan, simplify, and execute complex workflows. Knowing where to start can be challenging for new users. To this end, we developed three new quick guides to... Read More >

Introducing NISAR Data Support

Introducing NISAR Data Support

6/5/2026

The release of ENVI® SARscape 6.3 in April 2026 includes preliminary support for NASA-ISRO SAR (NISAR) data. The NISAR mission is a joint Earth-observing satellite project between NASA and the Indian Space Research Organization designed to monitor changes in the planet’s land and ice surfaces using advanced radar imaging. It... Read More >

Monitoring Illegal Mining in the Amazon: Turning Persistent Data Into Actionable Insight

Monitoring Illegal Mining in the Amazon: Turning Persistent Data Into Actionable Insight

5/28/2026

Illegal mining over decades has constituted one of the most persistent and complex socio-environmental problems in the Brazilian Amazon. In recent years, with the increasingly intensive use of mechanized extraction, the associated environmental impacts—such as deforestation, intense soil disturbance, river siltation, and mercury... Read More >

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New in IDL 8.6: IDLTasks

Anonym

ENVITasks have been around for a few years now, but they didn’t help those of you who only have IDL and not ENVI as well.  In IDL 8.6 we are happy to announce that the ENVITask concept has been carried over to work in pure IDL without ENVI.  Many of the features are the same, but there are slight differences that I want to point out in this post.

The first and most obvious is how you construct an IDLTask.  Rather than launch ENVI and then call the ENVITask()function, you can call the IDLTask()function at any point in time.  As the help points out, you can pass in either a scalar string that is the name of the task you want or its filename, or you can pass in a Hash object that is the task definition (what you would get if you called JSON_Parse() on a task file).  If you pass in a string that isn’t a filename (relative or absolute), then it is treated as a task name.  The function will then look at !PATH and search for all .task files in each folder listed there.  It catalogs all the .task files it finds, but if there are multiple folders with the same base name only the first is recognized(just like how IDL handles multiple .pro or .sav files with the same base name found in !PATH).  The list of .task files is filtered to those with the correct IDLTask schema, which currently is only “idltask_1.0”.  This way we don’t accidentally pick up an ENVITask files and cause confusion.  If a.task file with the same base name as the requested task name is found, it is used as the task definition.  If no exact match is found, but partial matches exist, then helpful error messages are returned telling you about the name(s) that partially match, so you can correct your code.  I should point out that the current working directory (which can be retrieved by calling CD with the CURRENT keyword) is searched before any of the folders in !PATH, so that can affect the behavior of IDLTask().

The “idltask_1.0” task schema used for IDLTasks in IDL 8.6 is very similar to the “envitask_3.0” schema used by ENVITasks in ENVI 5.4.  The notable exception is that the TYPE property of your parameters won’t understand ENVI class types like ENVIRaster.  But all the basic datatypes available in IDL are supported by IDLTasks – strings, Booleans, and numbers, as well as List, Hash, OrderedHash, and Dictionary.

Another difference is how you interact with IDLTasks on GSF as opposed to ENVITasks.  The service endpoint for IDLTasks will be http://hostname:port/ese/services/IDL,while the ENVITasks use http://hostname:port/ese/services/ENVI.  The different endpoints are used to discriminate between the requests that should use the IDLTask() function vs the ENVITask() function to load the requested task.

Easy GSF deployment is one of the primary reasons you would want to build IDLTasks in the first place. If you have IDL functions or procedures that you are used to calling directly, then you are probably wondering why you would want to wrap them in an IDLTask.  As a C++ developer in a previous life, I appreciated the type safety that C++ requires, so I also appreciate the parameter validation that IDLTasks provide.  When developing your custom IDLTask, you will have to spend some time thinking about what the inputs and outputs are for your code, but once you do that you won’t need to worry about writing lots of input validation code, the IDLTask framework will take care of that for you.  The IDLTasks are also self-documenting like ENVITasks, so if someone else hands you a .task file and .sav file, you can load the task and then learn all about the parameter names, their types, cardinalities, and hopefully even descriptions. All of this information makes it possible to deploy your algorithms on GSF for running in the cloud, with all the same introspection capabilities over the REST endpoint.  Alternatively, you can set up batch processing using some sort of folder watch capability to spawn IDLTaskEngine instances to automatically run your code on each file that appears on your system.

 

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