What is GeoAI? Definition, examples, and real-world applications
What is GeoAI? Understanding geospatial artificial intelligence GeoAI, short for geospatial artificial intelligence, involves using AI techniques like machine learning...
Note: This content reflects Picterra’s perspectives and product features at the time of writing, which may have since changed.
The whole industry is guilty of throwing around the expression “geospatial intelligence”. But what does it really mean? How does it differ from Geographic Information System (GIS)? We’ve put together a quick guide to shed some light on this emerging concept within the geospatial world.
It’s a common misconception that geospatial intelligence is just about maps and geographic information. Geospatial intelligence, or GEOINT for short, is actually the fusion of imagery and geography, which can be used to help solve a wide range of problems. In fact, it has a very long history. In its most basic form, geospatial intelligence has been around since humans first learned to tell stories about their surroundings. The storyteller drew an image on a cave wall using charcoal or other natural pigments, and that image became part of the story. Geospatial intelligence was born with this human tendency to communicate about our surroundings through drawings and paintings.
With the advent of photography and film technology, it became possible for images to be captured on stable media and preserved for later use. For example, the Americans used “Keyhole” spy satellites to capture photographic surveillance of foreign powers in the 60s and 70s. (Fun fact: they dropped film negatives using a parachute and captured them in the air by a plane to avoid losing them on foreign lands.)
It wasn’t until the creation of digital technology that geospatial intelligence really came into its own. Digital cameras on satellites, vehicles, and drones are capable of collecting enormous amounts of data in seconds, which can be stored in databases and harnessed for future queries or analyses. Imagery processing software can turn raw pixels into meaningful information: crop patterns on farmland; vehicle traffic on a highway; changes in vegetation growth over time; shipping activity at a port; or even street-level views of popular tourist destinations.
But the term itself actually has its roots in the US military and intelligence communities. The term gained popularity in 2004 when the National Imagery and Mapping Agency of the US Department of Defense was renamed the National Geospatial-Intelligence Agency (NGA).
A September 2006 memo from NGA defines geospatial intelligence as:
Today, the expression is used beyond the military sectors and is widely adopted by the research community and commercial organizations (like Picterra).
GIS refers to geographic information stored in layers and integrated with geographic software programs so that spatial information can be created, stored, manipulated, analyzed, and visualized. Such information includes any data source that has a location attached to it (e.g. zip codes, addresses, coordinates, etc.). It can be derived from many sources including GPS sensors, satellite and drone imagery, geotagging, and so on.
GEOINT should be considered a subcategory of GIS. It is a field for using geospatial data in combination with other analyses to improve and reduce uncertainty in critical decision-making. As the European Union’s Satellite Centers (“SatCen”) neatly puts it:
Geospatial intelligence reveals patterns and relationships that would have been impossible to spot if the data remained as a raw set of locations.
With the growing demand for accurate geographically-based information, geospatial intelligence captures two emerging trends:
In fact, geospatial data is just like any other data source. It needs to be collected responsibly (with minimal impact on humans, animals, and the surrounding environment) and presented in an understandable way. By using geospatial intelligence, organizations can enrich their decision-making by measuring the impact of past decisions and understanding why something went right (or wrong). Here are three industry-specific examples:
What once began as a military concept is becoming mainstream in more and more industries. It all starts with giving decision-makers an easy way to interact with data so they can explore relevant geospatial intelligence applications for their business.
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