GEOINT Singularity
GEOINT Singularity describes a hypothetical future time when capabilities of geospatial intelligence have advanced to full information availability and transparency. Physical activity on the earth's surface would then be monitored, analyzed and made available in real time and the information would be used by government, business, and individuals for decision making.
The concept of the GEOINT Singularity was first proposed by at in describing the convergence of three major trends – the proliferation of remote sensing technologies, the increasing use of artificial intelligence analytics to process and analyze large data sets, and the expansion of communication networks. The convergence of these trends would eventually lead monitoring and analyzing all physical activity on the earth's surface.
The potential consequences of approaching a GEOINT Singularity are not yet fully understood but could include improvements in agriculture, disaster response, and environmental monitoring. However, there are also concerns about potential negative consequences such in privacy, security and the general misuse of information.
Use of GEOINT Singularity
In the book "National Security Intelligence and Ethics", the former NGA Director Robert Cardillo describes the GEOINT Singularity as technology enabling continuous sensing of all of the world's activity. The author postulated "Who will guard the guards?" discussing privacy rights, benefits of technology, and potential misuse.Texas A&M University, uses GEOINT Singularity as required reading in a course on Defense Intelligence. The course exposes students to "historical and contemporary Defense Intelligence capabilities as part of the military decision-making environment".
describes in a Popular Science Magazine "Meet the GEOINT Singularity" that the increasing transparency would allow municipalities to fact-check housing developments, or international organization to monitor global shipping traffic.
GEOINT Singularity Composite Index
The GEOINT Singularity Composite Index includes a factorial combination of the following components and relevant indices.- Satellite remote sensors in orbit
- Spatial resolution
- Spectral resolution and spectral bands
- Data availability
- Data accuracy
- Machine Learning Training Time
- Data Connectivity