GEOINT: Team Sport

GEOINT: TEAM SPORT



Technology is Forcing Changes in How Geospatial
Intelligence Professionals Learn and Think About Their Field.

by Harrison Donnelly, MGT Editor

Changes in geospatial technology are driving GEOINT professionals to rethink not only training strategies, but also their overall approach to the discipline as a whole, according to a recent panel of intelligence experts.

The explosion of the amount of data available both to analysts and their customers, the diversity of distribution means and changes in analytical needs are reshaping the field, members of the U.S. Geospatial Intelligence Foundation (USGIF) said during a workshop at the USGIF’s GEOINT Tech Days conference this spring. The government, industry and academic representatives discussed the geospatial intelligence tradecraft and its relation to national security and homeland security missions, addressing the following question: “How well are we, as a community, meeting the technical, procedural and educational needs of the defense and intelligence communities?”

Following are edited excerpts from panelists’ remarks.

Carl Stuekerjuergen
Director, eGEOINT Management Office
NationalGeospatial-Intelligence Agency

Why is a service-oriented architecture [SOA] important? For me, the most important reason to move to Web technology and a SOA architecture is that it will give us new-found agility in the way we do the business of GEOINT, and perhaps even more importantly allow for collaboration. These are capabilities the DoD and IC cannot ignore. It allows us to capture and share tradecraft, right when we need it the most given our changing demographic. This community basically stopped hiring in the 1990s following the anticipated “peace dividend.” We now have a brain drain that we’re going to face as our subject matter experts in this tradecraft begin to walk out the door. For me, it’s all about how we make sure that we are capturing that corporate knowledge and expertise rather than watching it walk or revolve out the door. To be sure, some people come back as contractors, but the point here is that we have to make sure we’re capturing that tradecraft and making it more broadly available.

For a lot of reasons, mostly technical, GEOINT content now comes from every domain in the IC. What I call geo- SIGINT or geo-HUMINT and other areas are just exploding, in part because of the commercialization of GPS technology and how ubiquitous it has become in practically everything we use. The other reality is that the location-based services industry has been driving these technologies and associated commercial tools, which are adaptable to IC use.

On the open source side, what we see is one of the largest explosions of content and sources for GEOINT. If you look at www.wikimapia.com, for example, it now has some 68 million personal locations that have been put in by individuals to share. GPS is embedded in cameras today as well, so if you look at www.flickr.com, there are 2.5 million geo-located pictures. That is just the tip of the iceberg in terms of where open source is going.

GEOINT is a team sport. The tools on the SOA architecture will enable newfound collaboration, often in ways that we never had thought of before. It’s really the sharing of tradecraft in areas that traditionally have been stovepiped, discipline-based domains, particularly within the various “ints.” This is going to drive unexpected benefits, as GIS truly matures as a tool for analytical collaboration and scenario-based analysis. To me the real power of what GIS can do has always been to embed the subject matter expert who knows how you do geospatial analysis to do scenario-based modeling and “what if” kinds of analysis. Bringing this into a SOA domain will only enhance the transparency and peer review of those kinds of analytic methods.

The real challenges here are not technology. Industry has done its part, and the technology is already here. I argue that the community is only using a fraction of the functional capabilities that the tools that we have already provided. So the real challenge is culture and people. Particularly to the IT developers, I encourage you to help us figure out how this will fundamentally transform the way we do the system acquisition business. We need to understand that, and if we don’t understand it, we’ll just tend to wrap technology around the old ways of doing business.

Jack O’Connor
National Geospatial-Intelligence Agency

I’m a practitioner, in the business of managing analysis and defining geospatial analysis. I want to talk about the challenges of what we used to have to teach analysts, what changed in our world, and what we’re now finding that we have to teach analysts. We hire and bring in analysts from six sources. It’s a mix of what I would call the soft side of higher education—liberal arts—and the hard side—people who come in with very strong GIS and math background. Each has an area of new learning when they hit the intelligence community, because it is different from most of their backgrounds. Even the analysts who come from the military to the agency have a learning curve.

What we used to teach new analysts was the essence of feature recognition—understanding and being able to perceive distinctions, understanding the differences, then understanding the differences that make a difference, and then building context. We learned what equipment looked like, and then we learned what it looked like in garrison, and understood that certain collections of equipment can be translated into the idea of a regiment or squadron. But then you had to learn what these pieces of equipment looked like in the field, not neatly lined up, and how organizations in action differ from static organizations. Another part of basic training was sensor training—teaching people what sensors help answer which questions. Then finally, how to communicate what we found—to move from data to information, and from information to evidence, and then out of all the evidence, determine the best answers to the intelligence questions.

I had the privilege of leading NGA analysts in Operation Iraqi Freedom, which was a real turning point in the geospatial intelligence business, because it brought out things that we hadn’t dealt with before. These changes are what is now driving what we teach. The first difference is that a lot of what we used to investigate were past puzzles. Equipment that was already built—where would it deploy, and work under construction—what would it become? But we now deal a lot with future questions— what will different sectarian groups’ social, political and military influence over various countries in the Middle East look like? That’s a future event, and we’re spending as much time wrestling with future questions as we are with past puzzles.

Secondly, we used to fight over scarce sources. But in many cases now, sources are abundant. There’s been an explosion of commercial imagery, various sensor types and the great investments that the government has made in networking. That has made access to information generally much less of an issue. When imagery was scarce, we had an assured audience. We wrote a highly classified report, and people would always want to find it. Now, however, there is no guaranteed audience. There is so much information out there, classified and unclassified, that you have to work to get attention if you’re an analyst.

The next distinction is that we used to work at building the individual expert—like the medical general practitioner who deals with any illness that he or she sees. We’ve now moved to teams of specialists, where no one can know the quality of all the sensors, and how to use them effectively; no one can have both deep social and regional knowledge as well as all the GIS tradecraft and how to judge all the different data sources. Like the medical profession, what we’re doing now is bringing in teams—folks with different kinds of expertise to deal with different questions. Traditionally, we focused on “what, when and where” questions. We’re now being asked to contribute more on the “how and why” questions.

For analysts, EEIs are the essential elements of information—when you look at an image, what questions should you ask? We now are moving more toward social EEIs in places. Order of battle knowledge is really helpful in some countries, but not in others. Insurgent groups don’t have standard orders of battle, so you learn how to pick up different bits of social information and weave them into stories. We’ve broken out what we have to teach into six categories. First is capacity. When I went into the business as an editor, we would consider a large, complicated report, say on arms control, to be one with about 120 parts— pages, charts and images. Now we routinely, on a much more accelerated schedule, put out reports that have more than 500 files attached to them. That happens all the time. In addition, we used to work with fewer than handfuls of sensors. We’re now at dozens of sensors, and there are scores of data sets, from forward military operations, classified and other sources. All that information is combined into our work. So the volume of information and things that must be considered is much greater than it was, and we’re having to teach new analysts to get used to how hard this job is.

One issue for new analysts is the tendency to think that anything that is written down is true. When sources were scarce, we always had to consider how good the source was. But one of the habits of mind we’re having to teach analysts today is that having a lot of credulity and a tendency to believe things is not particularly helpful. So we’re having to get them to question the points of view of sources, and actually plot out data sets to see if they match reality, both on the mathematical side and the soft side. We’re spending more time on this than we had planned.

If I could figure out how to do a test for curiosity, I’d be rich. We have people from geospatial backgrounds, and those who are really expert with layered GIS. They are both great sources of answering the what, where and when questions. But there is something in some analysts that allows them to figure out the answers to how and why questions. It is some innate curiosity that allows them to grind through data and look at it from a different perspective. That’s the spark we look for, and it’s the distinguishing factor between the great analysts and those who are good visually but have to get into the more complex analytical issues. This is something we struggle with: How do we attract the curious?

It used to be that attention wasn’t a scarce resource and didn’t have to be managed. But today we are living in an attention-deficit world. If you don’t command people’s attention, it’s not given to you—it’s a scarce resource that you have to manage. We have to teach analysts this on behalf of the customers, who are all deluged with far more information and sources. What used to be scarce is now freely available. What is scarce now, and what our customers expect from us, requires us to go beyond visualization. Customers want us to help them focus and make distinctions. To do that, we have to communicate with them, and we’re spending a lot more time teaching new analysts this set of skills. If you post it, it doesn’t mean they will come. That’s our model for production. We teach new analysts to narrow-cast, target audiences, build communities of practice and network.

One thing that’s really helped NGA is our ability to deploy analysts forward, so we can communicate with our peers who are in the midst of operations, and build communities that don’t lose from hierarchical translation or rotational replacement. If new analysts come in expecting that the system will tell them what to work on, they have some rude shocks.

James P. Dolan
Executive Vice President and Chief Operating Officer
Overwatch Geospatial Systems

UAVs and the sensors they can fly have brought a revolutionary change to our community, with much greater volume, different types of data in different file sizes and formats. It’s coming toward imagery analysts at high speed. This data in real time is used to support operations, but we also have analysts who are trying to do what we in the past have referred to as first-, second- and third-phase analysis. There’s a lot of things happening, with a lot more data and a lot more variety. In addition, the expectations are much greater. In the old days, when we used light tables and some of the traditional sources and methods, if you had a turnaround for an imagery project that had to be done in two or three hours, and could do that, you were a hero. In the environment of today, however, you’re talking minutes or maybe hours, but the drive is to reduce that timeline to pull actionable intelligence out of that full-motion video stream as quickly as you can.

In the environment of today, we’re dealing with mobile targets that don’t fit the types of order of battle descriptions that we learned in traditional training. The tactics are asymmetric—it could be a pleasure craft driving itself into the side of a naval combatant, or an SUV loaded with explosives. These asymmetric tactics don’t fit with what we’ve seen in the past. We’ve also seen more and more operations in urban environments, which really complicates our job. In addition, a lot of times the predictive triggers that we used to have don’t come to as fast or readily as they should.

With traditional overhead sources, there is a lot more context in the data as the analyst looks at it. But if you look at a video sensor that’s flying a low altitude and is really zoomed in on a particular object or individual, there’s a greater need for context. The back-up data for a lot of UAVs is very immature and unstable, which makes a lot of the science behind imagery analysis more difficult. The concept of operations of full-motion video exploitation is something that is continually evolving, and it’s difficult for us to train an analytic workforce for these jobs. With full-motion video, in areas like Iraq and Afghanistan, there are a lot of cultural things that analysts need to know. They may be watching a fullmotion video stream of three or four people walking down the road, or someone digging near a road. The question is whether the person digging near the road is digging a well or filling a pothole, or burying an IED? There are lots of different things that analysts could benefit from if they really understand the people and the culture.

Carol Robert
Director, Geospatial Analysis
BAE Systems

Commercial imagery can provide the basis for a wealth of mapping and charting, analysis and so on. It also gives us a unique ability to disseminate product to members of the homeland security, law enforcement and disaster relief communities, all of which can benefit without having to jump through a lot of dissemination rules. It does make some types of analysis and exploitation easier, and we have staggering quantities of high resolution imagery available. On the downside, a lot of analysts in the intelligence community will use what they are most familiar with. They have easy access to classified imagery, so that’s where they do first, even though there is a wealth of data available to them on the commercial side, which they may not even think to use. So part of the problem is in making sure they understand what is available to them. In addition, most analysts are only trained on part of the tasking, collection, processing, exploitation and dissemination process.

The intelligence community is not the only customer of commercial imagery, so there is competition for that resource. One analyst recently gave me the example that is fairly typical for the process. He needed a tasking for an upcoming event that we were supposed to monitor. He was given two collection windows, of which the first was cloudy, and the second didn’t shoot for some reason. Two days after the event, the image was shot, and it showed up a week later on his desk with nothing to relate it back.

Anthony Stefanidis
Director
Geospatial Intelligence Graduate Certification Program
George Mason University

The major issue is that we’re talking about things that are increasingly dynamic. We’re not talking only about people who are moving, but also about the area they’re in. This is much more challenging to capture and describe. The other challenge is information has become more diverse. We are always receiving more and more information that we have to make sense of. The dynamic nature of the information and the diversity of sources have changed the landscape for us.

The main success story that we have is using new platforms to collect data. That’s been working well for us, and we can collect lots of data. We can probably collect in one week in Iraq more data than we collected in more than five years of World War II. We are doing that very well. We have automated analysis tasks and mapping processes so that we can faster and more reliably than before, and have better delivery of more information to more users. Where do we fail? The main problem is that we are very successful. We can collect all this information, but we now face the problem of how to process it in a timely manner. We have to catch up with our rate of success. The other thing that is keeping us back is that we do not have a solid theory or spatial-temporal algebra. ♦