Hear different perspectives from students, professors, and researchers to understand how Hexagon software bridges the gap between industry and education.
BK: Today we are interviewing a student, a professor, and a researcher from around the world to understand how Hexagon software bridges the gap between industry and education. Welcome to HxGN Radio. My name is Brian and joining me is Chris Hanni, student at the University of South Florida; Steve Fleming, professor of the Practice of Spatial Sciences with the University of Southern California; and Joyce Maphanyane, professor at the University of Botswana. Thank you all for joining today. I appreciate it.
SF: It’s good to be here. Thanks
JM: Thank you.
BK: I’m excited. We’re going to have some fun today. We’re going to get some great information, so we’re going to start off with a little bit of information from each of you, and Chris, tell us about yourself, your university affiliation, your role, some background, all that good stuff.
CH: Well, my geospatial background started off when I joined the Army; 12 years of remote sensing experience with four combat deployments. And then I decided to go into academia, and now I’m working on my PhD in Geography and Environmental Science. My thesis research actually led me here, working on palm decline in Florida. There’s a new disease that’s wiping out the state tree.
BK: Oh, no.
CH: I built a spatial algorithm, using Spatial Modeler, that classifies the palm trees at a very high degree of accuracy and bridges a gap with TensorFlow. I’m not sure if you’re familiar with that application, but it’s an artificial intelligence application that Google came out with. I have built an algorithm with that artificial intelligence.
BK: Nice.
CH: So, we can track down the palm trees and figure out where the sick ones are at. Give some spatial metrics to the forestry managers.
BK: For the desire, of course, of reducing that, trying to save the ones that are healthy.
CH: If there’s a mitigation strategy, if there is going to be a cure that’s going to be implemented, we have to find out where the sick palm trees are at. My maps provide that information.
BK: Excellent. Okay. Very exciting. Well good, and good work too, so it’s helping out some people. All right. Thank you very much. Appreciate it. Joyce, how about you?
JM: I’m a senior lecturer at the University of Botswana in Southern Africa. Geographical information system (GIS) was sort of fairly much new when I came back from University of Melbourne with my degrees. So literally we started the GIS. Initially, it was mainly digital cartography, but we streamed it to GIS and remote sensing. At the moment, I teach undergraduate from year two, which is the introduction course, year three, which is the elementary course, and then year four, whereby is mostly them researching using the information they have learned. Recently, I managed to put a master’s degree in geospatial sciences together with universities in Southern Africa: University of Cape Peninsula in South Africa, the University of Zambia in Zambia, and five universities in Angola, and the Science and Technology University of Namibia. They were led by a university in Germany who sponsored the whole collaboration.
Hopefully, we will go into teaching the secondary school students the geospatial sciences, because that is where it is lacking. But also, I think it would be far better if also the technical staff, who do not have degrees, or to put them into a diploma and then a degree, because they have the interest in geospatial sciences. Of course, we use it in research. At the moment, I have PhD students and three master’s students. Thank you.
BK: Wonderful. Yes, thank you very much, Joyce. Appreciate it.
JM: Thank you very much.
BK: All right. Steve?
SF: So, Steve Fleming, similar to Chris, an old soldier, did 30 years of active service in the military where I was … the last part of my career was working with geospatial systems. I had the honor of helping develop cadets at West Point. But as part of that, was required to kind of work with the intelligence community. All the organizations that work with, in this case, spatial data in order to make decisions for war fighters and others in support of war fighters was what I was doing. Also was working in academia. I was developing academic programs for those that were going to go in the service one, or maybe work for some of the agencies that were going to use them.
Upon leaving active duty, I ended up at the University of Southern California, who was about building a number of programs, and one of them was in the intelligence world, so GEOINT programs for folks that would be interested in taking on academic programs, whether it be at the undergraduate level or the graduate level that was going to apply the techniques.
I was a practitioner of using the discipline to help solve problems, and then realized the next generation was likely going to be challenged more than I was in using this as it was going to become something that more and more of us, the better we are at it, the better we’re going to be able to solve the problems of the world.
BK: Absolutely.
SF: In that regard, I just thought it was important to be around academia and helping the next generation learn how to use this stuff. That’s my background.
BK: Awesome. Thank you very much. Appreciate it. Well, I want to ask each of you, and we can just kind of go around the table here. We’ll start with you, Chris, have you, and of course, you know in this position it’s just going to be you, but any of your students as well as we go around the table, used Hexagon software for a specific project or for teaching a course to reinforce theoretic concepts, you know, et cetera.
CH: I mean, absolutely. A lot of my research had a lot of stepwise refinement and masking processes, and there’s a tremendous amount of intermediate files that are produced while you’re doing this process. [It was a] very, very large model. In order to keep track of all those files, like through very small stepwise refinement processes, it would have been a nightmare, almost impossible. The Spatial Modeler keeps everything contained and packed up into a nice bundle, where you don’t really have to keep track of all those intermediate files and then you can just tweak it how you need it and then get to your end results. So, it helped out a lot. I’m very thankful.
BK: Great to hear. Excellent. Joyce.
JM: Mainly, we use Hexagon ERDAS IMAGINE mainly for land cover change. Land cover change is a meter for natural resources. What is there, what is used, and what remains, and how much that is used, and where. So, where do you use the satellite imagery or aerial photographs? If Botswana is the worst country, we might have to monitor the wildlife, we might need to monitor the health of the wildlife. You know?
So, we can see how much forest and how much vegetation they can be sustained in. We can actually use land cover change from satellites using time series, and the ERDAS IMAGINE, it allows you to visualize the satellite images and then categorize it into classes before you can actually go there specifically and say this is this type of land cover and link it to the real world and see how much cities are growing and how much people’s fields are being taken by the cities and the villages, which means reduction of food security. Mainly, my students research mainly the pollution and the land cover changes, pollution in water, pollution in the soils.
BK: Very interesting. Without ERDAS IMAGINE, could you even do most of what you’re doing right now?
JM: You can’t, because the thing is that this remote sensing in geospatial sciences is highly technical. You need to know mathematics; you need to know physics. You also need to know statistics. On top of that, which is not fair, you know, the science students will not know social economic aspects. People from this different should work together. So now you can have somebody from the social, because this science, this mathematics, this statistics is in-built within ERDAS IMAGINE. You just need to teach them the procedures of what you do in classification. How can you do the NDVI, how do you infuse the removal of the atmospheric pollution from the satellite images, which a normal person cannot know the sciences plus the weather plus the socioeconomic fact, you see.
BK: Then you’ve got to pull in, you know, various people from those professions as well, which is more money and more coordination. So yeah.
JM: Yes. And then the ERDAS IMAGINE is like the science behind it. It actually simplifies the sciences. The programs are already there. You just have to teach the student what to do and for them to understand the theory behind it.
BK: Excellent. Good. Well that’s impressive and definitely the way to go. Thanks for sharing that! Steve?
SF: Previous life. I did work with and use personally the software suite back in the days of ERDAS IMAGINE. Used that and taught a number of courses at the military academy and then used it as a practitioner. So, I realized the power of it and why it was important to have that as a software application that was necessary to evaluate imagery. This is actually back when imagery, we understood it then, was often collected through national technical means collects. Obviously, there were some service providers that were giving it to us, and we were using commercial software and commercial imagery in order to make some decisions. But since then, there’s been a lot of things that have happened that require us to use different software suites in order to be able to handle the different variations of imagery that are now coming at us.
Whether we’re talking satellite collects, or airborne collects, or even terrestrial collects, the requirement today to be able to manage the different forms of data is changing rapidly, and we just have to make sure we understand the software applications that best do it. To that point, folks at USC realize that. And so in building academic programs, in order to support this, we’re moving in the direction of creating more opportunities for students to use these different forms of software, whether we’re talking, you know, the Hexagon suite or other suites that are available to include open source software that’s out there in order to be able to solve some of these very challenging problems. As part of that, we’re introducing new courseware and new academic programs. We’re in the business of launching new academic certificates, new academic undergraduate majors, new academic master’s degrees that support, in my world, the human security requirements of understanding this, and these technologies, better in order to be good practitioners or professionals.
I look at that as the requirements that we have moving forward and whether we’re doing basic research or applied research and you know, some of the work probably that Chris is more interested in doing right now based upon where his glide path is. Certainly I think he could reflect back on when he was a user of this in answering today’s question about why it’s important to be able to take that imagery as we knew it and make it a good assessment, one, and then provide a good recommendation to a decision maker within, say, six hours. So how do we do that? How do we build the next bench, if you will? How do we build the next set of researchers and then maybe educators like Chris, or maybe how do we build the next analyst at fill-in-the-blank organization, whether it be one of our government agencies or maybe a not-for-profit, but how do we do that?
I think it’s important that education recognize the change, how dynamic it is, and then be able to integrate the right partners like Hexagon in order to be able to achieve that end result.
BK: Excellent. Any follow up thoughts on that question?
CH: Just like the direction of the military, kind of falling back. It’s been awhile since I’ve been in at that. And I know what you’re talking about. I worked several different kinds of missions. You know, airborne, satellites, as a collection manager, quality control, communications, as an NCO in the Army and so very demanding environments and I admire what you’re doing. It’s just I kind of see myself in the transition right now where I’m kind of focused more on environmental concerns.
SF: Yeah, I have to reflect on the military. We use the word “military” often, but when I talk to folks about what I did and in this case what Chris did, there’s the military answer and some folks appreciate that answer.
But really, at the end of the day, what military organizations around the world are doing extremely well is they manage chaos, they’re chaos managers. We can look around and find our chaos managers. Certainly, the military is one example of chaos management. Then we have a lot of folks that are in the business more at the local, state, and city level, regional level that are chaos managers. And we find these folks that are in the business of what we would routinely call providing safe and secure communities for us. Depending upon which end of the spectrum you want to talk about, there are folks out there that manage chaos very well. They’re designed to do it. It could be chaos as a response to a natural disaster when we sometimes can be predictive about that, and other times we can’t.
You know, where I live in California, the one that’s just really hard to predict, which you never hope happens, is an earthquake. It’s super hard to predict that. The others are pretty predictable and usually have some kind of run-up time in order to provide some form of safety. But nonetheless, they’re going to happen, and we have no control over these, but the military or organizations like it can go in afterwards and help manage the chaos which is created on the back end. On the other end of it is intentional manmade disaster, versus natural disaster, and warfare at the end of the day is intentional manmade disaster. So that’s chaos as well. And then everything in between.
CH: Just one more follow-up with your question. I think a concerted effort at the schoolhouse in Huachuca. It’s probably one of the best starting points in trying to bridge that gap for the speed of how far things are involving the geospatial community and filling in the soldiers with good instruction at the schoolhouse with good lectures.
SF: And so, what Chris just mentioned, the school house, this is the military intelligence schoolhouse at Fort Huachuca, Arizona. That’s what he’s talking about, so there’s organizations like it in the military that do the education/training, kind of push-button-ology, the how-to of that latest set of devices that we use that helps us understand the state of the art at that point in time. We have these across our services, so whether we’re talking, in the Army, at Fort Huachuca, there’s other services that use these types of technologies and certainly at the national level, there’s agencies that use these technologies. It’s routinely called GEOINT, geospatial intelligence, but more broadly it’s the science of where geospatial, where [it is]on the earth, in order to provide decision-makers with a good solution through just intelligence.
So, intelligence, better definition instead of like in the military context, it’s actionable information for decision-makers. That’s intelligence. When you’re beyond just a flip of a coin, you have better than a 50/50 chance that the recommendation that’s provided with you comes with enough academic or intellectual might to where the decision that you make has confidence other than just a coin toss. And so that to me is what intelligence is.
BK: Excellent. All right, great. Steve, Chris, thank you very much for that. That was a great explanation and some great information. Well, Joyce, tell me a little bit about the benefits of using Hexagon technology in academia.
JM: Well, the first thing is that it helps you to teach, in geospatial science, for instance, you, first of all, you have to visualize the satellite images. You have to look at them and see whether they are usable or not. That way you can still view them in ERDAS IMAGINE. Then you have to process the images. You have to take individual satellite bands or channels and stackthem together so that they can make sense. A composite map, depending on whether you want to look at the vegetation, you can actually take an infrared and make some mathematical divisions and additions and multiplications, like NDVIs, you know, within the set of processing of ERDAS IMAGINE. The second thing is that you need to do the analysis within this data sets. The ERDAS IMAGINE has simplified digital spatial statistics so that you can actually do the classification, including going out to do the ground truthing, because you know exactly which area or which band classes you are going to be looking for.
And then, in Africa where I come from, we are so much short of where I can say highly-trained computer scientists. The few we have, they will just evaporate out to go and look for more money. That’s why we are not more on the open source, because at some point with the open source you have to find somebody, you know, to program something to go from one piece to another. With the ERDAS IMAGINE software, because also we allow for a contact whereby, we can call to say we have this problem, and then we call for somebody, or we send them email and they can … you are not alone. You have somebody. And apart from teaching the students, also we do research for mineral prospective.
We do research where we need to find which area may be heated, because it might be prone to seismic movement. We also need to find way really where there is water because Botswana is a desert. You can also, if we have to do the border with South Africa or with Namibia or with Zambia, you can have the satellites and just work on them without having to go out to another country to say, “Can I actually use your data?”
BK: It’s really simplifying things.
JM: Yes, yes. And mainly because it’s programmed. It’s also tailored within the theory, you know, when you teach something within the theory, maybe the topographical error, which might make the satellite image not readable. You know, you have that for corrections in the ERDAS IMAGINE. Step-by-step, you are actually looking at the science, which is embedded into the software. Mind you, the software is part of the six components for geospatial sciences, which is the computer hardware, the computer software, the people themselves, the organizations, the data, and also the dynamism of data, how to communicate with it. Within that is embedded in the ERDAS IMAGINE software.
BK: Excellent. Glad it’s so helpful.
JM: Thank you. Thank you.
BK: Well, Steve, there’s been an announcement of Luciad M.App Enterprise M.App X education programs. What do you think all about this?
SF: Yes, great announcement, and the words I would pick around the word announcement. What do I think about it? I would say it’s an insightful announcement, a thoughtful announcement, and purposeful announcement.
So, insightful. Recognizing that working with academic programs and starting there as a place of entry for this type of set of applications is the future. And you’ve got to start there. This is not one of those things that you can often pick up late in the game. So, working with academic institutions, and they’re not the only game in town that has done this. There’s many organizations that have recognized that working with students when they’re working on developing themselves as purposeful professionals. So usually, in the college years or sometimes late high school years is where we see folks recognizing who they want to be and why they want to be it. Delivering that as a tool to them is as an insightful decision.
The next thing, thoughtful, they’ve figured out how to do it. They’ve put some energy into the way to deliver this type of opportunity to students by providing the protocols in order to be able to be ingested by academic institutions. And then lastly, purposeful, which at the end of the day when you listen to people talk about why we do these things. What’s the purpose of bringing these types of technologies and using them by folks normally is to leave the world a better place than you found it.
Big Idea, idealistic idea, certainly. But I think it’s recognized by, in this case, Hexagon, that that’s something that they should be part of. It’s something bigger than the company. So, insightful, thoughtful, purposeful, and education to me, big difference than training. We’ll use that word as an operative word in the discussion. One thing about the data that we’re talking about, which is certainly a topic that a lot of folks talk about today when we think about how data is used around the world. You know, the unique thing about this data, it’s spatiotemporal. It answers a couple of questions just embedded in data, and that’s where and when, and that’s what often we get as a function primarily of GPS data, which is now being connected to other forms of data. But that allows you to understand the where and when that becomes inherent and what we do. The education stuff tries to answer the why, the what, and the who, whereas how you use the application is often just the how. So that’s maybe the training that comes with it and how to do it and push-button-ology and maybe some of those other words that kind of get linked to training. There’s a how question that’s answered by the education community; it answers some of those other bigger questions about the why things happen, what’s going on, and who’s doing it. So contextually, in an academic environment, you can teach and learn those things. And I think that’s where academic institutions are leveraging this best.
BK: All right, well you mentioned the future, that this is the future, and I’m curious to know: where do you see the future of geospatial going in academia? Chris, I’d love to hear your thoughts.
CH: Well just kind of coming back just a little bit to M.App application. That’s a cloud-based remote sensing platform, which I think is beneficial to students, because a lot of students don’t have a lot of resources to buy the hardware components, like the very fast GPUs and CPUs. If you can have, like, a centralized processing area, I think that takes a lot of the load off of the individuals when they’re manipulating these very large data sets. I think they expand those applications for a full geoprocessing suite. I think there’s a lot of promise with them.
BK: Good. Excellent. Joyce?
JM: Geospatial science is big. Yes. You need to answer many questions. That’s why really in academia, there’s a lot of work. We have to do the groundwork so that we can teach people how to source the data, how to use it, how to analyze it and read it. Where I come from in Botswana, they say there’s government, there’s the academia, there’s the NGOs, and there’s the majority. And the community who are the majority, normally things are done for them, and it’s not like here in the USA where technology is possible for everybody. There, you’ll find that the technology is only possible especially for the government and for the universities, and for us, we have to use it so that we can assist the way the country is running. You know, how to protect our wildlife, how to find water for people, you know, how to do the environmental protection, but also with the indigenous knowledge, the type of resource in the forest, which are useful in certain ways, and use that data and sort of save it so that it’s not only knowledge to them, because if they pass on, it means that that knowledge is gone. We have to take that knowledge, infuse it in geospatial sciences, so that it can be useful for the future.
BK: Good. Excellent. All right. Steve, any further thoughts?
SF: Yeah, I think the … you know, what we’re seeing is there’s a whole bunch of academic disciplines, which translates into professions upon receiving your education. But I think there’s a whole bunch of academic disciplines right now that are very interested in the geospatial world. The idea of collaboration with them and figuring out how to work and interface with these organizations that we find around campuses is going to be a huge step in the right direction, the better we become at it. Instead of becoming what we call the silos of excellence that you routinely see on campuses, it’s this department versus that department versus that department, which for those that are around academia sometimes understand how it happens. Although it’s in some ways not justifiable, but it’s the idea of some research dollars get just put against one certain person’s interest and that then becomes a very protective space.
That kind of goes against the grain of being collaborative with someone else working, say, someone else on a campus. That said, the idea that no one’s as smart as everyone and how academic institutions provide clear opportunity to do collaborations, to try to work at solving some wicked problems. I think we’re going to see geospatial have better reach across academic institutions moving forward. And the better Hexagon can figure out how to fit into that in order, to provide multidisciplinary approaches to create some solutions that are what I would call wicked problems, that require attacking a problem from multiple angles, is going to be huge, as we kind of move forward. There’s a quote out there that talks about how good teams work. Good teams communicate well, cooperate well and care about each other. The first part of it is how well do you communicate? Ensuring that we have good communication amongst academic institutions, you know, internally is going to be, for me anyways, a big part of the solution set moving forward in order to use this stuff well and effectively.
BK: Okay, great. Well, this has been fantastic information. Thank you all for sharing. Really appreciate it, Chris, Joyce, Steve. Thank you. Thanks for your time. Appreciate it.
JM: Thank you very much.CH: Thank you.
SF: Thank you for having me.
BK: Absolutely. For more information, please head on over to hexagongeospatial.com. And to learn more and listen to additional episodes, go to hxgnspotlight.com, and thank you very much for joining us here on HxGN Radio.
