L3.03: GEOINT Data Requirements, Sources, and Collection Strategies


The purpose of GEOINT is to supply decision makers with timely geospatial insights that allow for informed, knowledgeable decision making. In order to fulfill this purpose, we prioritize the intelligence requirements of the decision maker. These requirements define the mission, functions, and structure of the GEOINT; they also should drive GEOINT data collection and analysis. The Intelligence Cycle, depicted in Figure 3.1, starts with requirements. The requirements are sorted and prioritized, and are then used to drive the collection activities. Once information has been collected, it is initially evaluated, processed, and reported to consumers. The cycle is then repeated until the intelligence requirements have been satisfied.

Figure 3.1: The Intelligence Cycle.

Based on the requirements, collection systems are given specific tasks to execute. Requirements are often difficult to define because they require a focus on long-term needs while most decision makers do not know what information they want until they actually need it.

Collection includes acquiring information and providing that information for processing and production. Collection management is the formal process within an intelligence organization of converting intelligence requirements into collection requirements, establishing priorities, tasking, coordinating with the collection sources, monitoring results, and re-tasking as required. The collection process encompasses the management of various activities including developing collection guidelines that ensure the best use of resources. There are four management criteria:

  • Is this intelligence necessary?
  • Is it feasible to collect this information?
  • Is this timely?
  • Is this intelligence requirement sufficiently specific?

A collection strategy seeks to determine if sources can satisfy the requirements. Three key goals of a collection strategy are to:

  • Meet the information need,
  • Collect information to detect deception, and
  • Provide redundancy in the event a source is unavailable.

Detecting deception requires information from a variety of sources so that one source can be verified. Multiple collection sources enable collection managers to cross-cue between different sources. Collection may require redundancy so that the loss or failure of one source can be compensated by another source.

GEOINT data is collected by computer systems, automated sensors, and humans. In general, "intelligence sources" are the means used to observe and record information relating to the condition, situation, or activities of a targeted location, organization, or individual. GEOINT data sources have traditionally included imagery and geospatial data. While imagery is still the dominate and most important source, GEOINT is evolving to integrate forms of intelligence and information beyond the traditional sources of geospatial information. This lesson recognizes this transition and structures the discussion in broad terms. The collection strategy addresses the approach pursued for GEOINT data collection, which I divided into the continuum of strategies including the categories of "persistent collection" and "discontinuous collection."

GEOINT Data sources can be categorized from discontinuous to persistent versus closed to open, shown in Table 3.1 below:

Table 3.1: GEOINT Data Sources and Strategies
  Collection Strategy:
Collection Strategy:
Source Access:
Example: The home location of the students taking this course. Example: A continuously monitored video camera along a street.
Source Access:
Example: The Coca-Cola Company's Coca-Cola recipe. Example: UAV equipped with a camera tracking a military target for 24 hours.

A strategy is a plan to achieve goals; it provides direction and scope for an effort. In this lesson the term "collection strategy" is a way to pursue GEOINT data collection. A collection strategy is important because the resources available to achieve goals are usually limited. We have divided a continuum of strategies into two categories of "persistent collection" and "discontinuous collection." The collection strategy determines frequency at which data are captured for a specific place on the earth. This frequency is termed temporal resolution.The more frequently data are captured by a particular sensor, the better or finer is the temporal resolution of that sensor. Temporal resolution is relevant when using imagery or elevations datasets captured successively over time to detect changes to the landscape.

Discontinuous collection

Discontinuous collection is not a full record of activity during a time period. It also might be termed non-persistent. Discontinuous collection is not a "permanent stare" at a target from one or more systems. Such a "permanent stare" might not be available, technically feasible, or an efficient use of resources. The practice of discontinuous data collection currently dominates the discipline.

Traditional remote sensing is an example of discontinuous source strategy. Here, sensors are mounted on board aircraft or spacecraft orbiting the earth. At present, there are several remote sensing satellites providing imagery for research and operational applications. Spaceborne remote sensing provides repetitive coverage of an area at a relatively low cost per unit area.

Discontinuous collection can also occur when a device sporadically provides data. For example, a roaming cellphone locates itself with signals from multiple antennas or through its GPS. There are numerous other examples of discontinuous GEOINT data. For instance, toll highway devices record your point of entry and point of exit to compute a toll. Twitter is another example. Tweets can be geotagged to record information about the location of the device that created the tweet. Images taken with a phone or a GPS-enabled camera can also contain location data.

Persistent collection

Persistent collection is defined as a strategy that emphasizes the ability to linger for a period of time to detect, locate, characterize, identify, track, target, and possibly provide near- or real-time data. Persistent collection facilitates the collection and prediction of human behavior, for example, pattern of life. The goal is to achieve near-perfect knowledge by increasing the rate of data collection and, therefore, understanding about the target. This enables a faster decision cycle from more detailed information. The collection goal is that a target will be unable to evade information collection. The purpose is to improve decision making while reducing risk. In different domains, Persistent Collection is called Persistent Surveillance, Intelligence, Surveillance, Reconnaissance (ISR), Persistent Stare, or Pervasive Knowledge. Unmanned Aerial Vehicles (UAVs) are frequently associated with persistent collection.