With the first day of Summer 2016 in the books, my thoughts are turning towards the ENVI Analytics Symposium (EAS) and our shared community goals regarding the geospatial analytics marketplace.  As a remote sensing scientist, I know that the 2016 EAS will deliver a vigorous and lively debate on new sensors, collection platforms, cloud technology, data science and algorithms.  We will have that dialogue with world-class research scientists and industry thought leaders discussing how geospatial technology (such as spatial-temporal analytics and Small Sat platforms), is meeting the growing challenges of National Security, Global Climate Change, Health Analytics and Precision Agriculture.

Of particular interest will be an in-depth discussion, led by Dr. Michael Steinmayer of SulSoft, on de-forestation monitoring in the Amazon rain forest using airborne SAR and spectral data.  I am also honored to announce that former NGA Director Robert B. Murrett, a professor on the faculty of the Maxwell School of Citizenship and Public Affairs at Syracuse University, and Deputy Director of the Institute for National Security and Counterterrorism (INSCT), will once again join me on the EAS stage as a moderator for our panel discussions.

This year’s EAS will also explore the business drivers behind the growth in geo-solutions across a wide spectrum of consumer applications.  Our track on Cloud Platforms and Geospatial Markets will feature a diverse group of companies that are investing in online marketplaces and leveraging cloud technology.  Industry leaders such as DigitalGlobe, Airbus Defence and Space, Cloud EO, Harris Geospatial and Amazon will discuss their approaches, success and challenges in getting to the next big growth ring – commercial customers that want answers to business problems. We are adding emphasis to this commercial business theme by including an Industry Solutions track featuring innovative companies, such as exactEarth, FLoT Systems and Highland Agriculture, who will discuss their analytical approaches to maritime, utility and agricultural markets. 

Our line-up of workshops has something for everyone including Deep Learning approaches for geospatial data, an introduction to Geiger-mode LIDAR, SAR Lunch and Learn, Small UAS data processing and many other topics of interest.  Be sure to take a look at the workshops and agenda topics for the event.  I hope you will join us this August 23rd and 24th in Boulder, Colorado, for the next chapter of the ENVI Analytics Symposium!

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 on-line 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

As part of the Commercial Services Group here at Harris, I have a unique opportunity of using our latest and greatest tech, often before it even goes public. (Making me a bit of a beta tester sometimes.) Over the past couple months, I’ve had the pleasure of getting to work with some of our newest tech, and it’s very exciting. The two pieces of technology I’ve had my hands on are our Geospatial Framework (GSF), and our machine learning tool known as MEGA.

The Geospatial Framework has given me a lot of power in setting up large amounts of processing either locally, or on a server. It allows for clustering, many different processing engines, and even lets you hook it into custom websites. From what I’ve seen, it’s very flexible and powerful tech.

MEGA, which I’m also extremely excited about, is a new way to tackle feature extraction problems. Though I don’t understand the underlying development of artificial intelligence, I have had hands on experience training this deep learning system. While the code is complex, the idea is not – first, feed the system examples of what you are looking for in imagery. Once the system is trained, the software will tell you locations and confidences for where those objects of interest are.

Image: http://stats.stackexchange.com/questions/tagged/deep-learning

Using these two technologies together, I’ve been classifying large amounts of image data quickly through a deep learning system, and the sky really is the limit. With GSF, the ability to scale up just depends on how much hardware you can allocate to a task.

The more I use and learn about these technologies, the more excited I become about them. The best part is that they are very new, so they have room to become more and more powerful and robust. Can’t wait!!