How artificial intelligence is transforming GEOINT
Connecting state and local government leaders
AI-based geospatial intelligence performs the time-consuming classification of location data, helping analysts quickly uncover relationships and predict outcomes.
Artificial intelligence has improved by leaps and bounds since IBM's chess-playing Deep Blue defeated reigning world champion Garry Kasparov in 1997. But that early face-off illustrated machine learning in its nascence: A computer makes sense of data it is given, finding patterns and crafting solutions based on the presented scenarios. One major difference today is that modern ML systems have access to infinitely more data from which they can uncover relationships and predict outcomes without pre-existing empirical models.
The new frontier in ML is turning geographic data into deep location intelligence. Enabling applications to understand relationships in geographic data is the key to addressing some of the most pressing threats facing the geopolitical world today. And the people at the forefront of these challenges are in the intelligence community.
Geoenabling intelligence data
Infusing location intelligence with AI technology has the potential to transform the role of geospatial intelligence staff, allowing them to offer more timely and complete analysis. In recent years, advances in AI algorithms for object recognition and broad-area searches have removed much of the inherent uncertainty from GEOINT decisions made in the field. Combining AI and intelligence information can allow us to move beyond simple recognition and into intelligent models for alerting and notification.
The scale, complexity, nature and pace of modern conflicts demands fresh approaches to delivering intelligence capabilities. Location intelligence provides the key to understanding situations better with the kind of deeper insights that real-time spatial analytics enables. For instance, if an intelligence analyst tracking smugglers who are crossing borders has a mass of point locations received through observations, sensors and reports, a spatial query could filter out the relevant data and identify smugglers' possible routes. The analyst can then visualize the relationships between the people, groups and objects and determine the threat’s likely course of action.
Recognizing images and patterns
However, GEOINT analysts still challenged by the necessity of collecting much of their intelligence from unconventional sources. While this data has a location component, it often must be extracted from its source and scrubbed before it can be analyzed. Humans can't examine every single pixel of an image or watch every second of a video to unlock valuable data. AI, however, can extract that information from imagery and locate it in space and time, so that it can then be connected with other relevant data. AI can also do these types of tasks faster and more effectively than humans.
Intelligent tools for better decision-making
Algorithms can also analyze and learn from imagery and location data based on known patterns to help organize and categorize the information analysts need and care about most. For instance, if specific types of structures are commonly used for storage or manufacturing, these types of structures can be easily codified, understood and ultimately identified by pattern recognition filters in ML algorithms.
Perhaps one of the most groundbreaking innovations that AI can enable in the GEOINT analyst’s day-to-day activities is giving the detection and reporting of foreign threats a real-time edge. Algorithms are allowing the GEOINT community to model indications and warning scenarios. That allows information regarding a threat -- whether to the U.S. military, political or economic interests or to citizens abroad -- can be assessed the instant that data in processed. In scenarios where time is of the essence, the ability to perform real-time analytics on intelligence about a foreign threat, determining whether it requires warning or more decisive action, is crucial.
Intelligence analysts have a specific set of needs, and each of those needs contain a component of location. The integration of location intelligence into the GEOINT analysts’ toolkit allows for a more holistic and contextual understanding of the data they see every day. Applying the cutting-edge AI technology to spatial analytics creates a smarter GEOINT capability, a definite edge.
The intelligence community must make sense of mountains of geospatial information quickly and turn that into actionable intelligence for decision-makers. A technology that performs the time-consuming and detailed function of classifying this data frees up GEOINT analysts to make the cognitive connections that only trained human intelligence personnel are able to understand.
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