Imagery intelligence (IMINT), pronounced as either as Im-Int or I-Mint, is an intelligence gathering discipline wherein ry is analyzed (or "exploited") to identify information of intelligence value. Imagery used for defense intelligence purposes is generally collected via or aerial photography.
As an intelligence gathering discipline, IMINT production depends heavily upon a robust intelligence collection management system. IMINT is complemented by non-imaging MASINT electro-optical and radar sensors.
Aerial reconnaissance#History
Aerial photography#History
Although aerial photography was first used extensively in the First World War, it was only in the Second World War that specialized imagery intelligence operations were initiated. High quality images were made possible with a series of innovations in the decade leading up to the war. In 1928, the RAF developed an electric heating system for the aerial camera. This allowed reconnaissance aircraft to take pictures from very high altitudes without the camera parts freezing.
In 1939, Sidney Cotton and Flying Officer Maurice Longbottom of the RAF suggested that airborne reconnaissance may be a task better suited to fast, small aircraft which would use their speed and high service ceiling to avoid detection and interception. They proposed the use of Spitfires with their armament and radios removed and replaced with extra fuel and cameras. This led to the development of the Spitfire PR variants. These planes had a maximum speed of 396 mph at 30,000 feet with their armaments removed, and were used for photo-reconnaissance missions. The aircraft were fitted with five cameras which were heated to ensure good results.
The systematic collection and interpretation of the huge amounts of aerial reconnaissance intelligence data soon became imperative. Beginning in 1941, RAF Medmenham was the main interpretation centre for photographic reconnaissance operations in the European and Mediterranean theatres.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Imagery intelligence (IMINT), pronounced as either as Im-Int or I-Mint, is an intelligence gathering discipline wherein ry is analyzed (or "exploited") to identify information of intelligence value. Imagery used for defense intelligence purposes is generally collected via or aerial photography. As an intelligence gathering discipline, IMINT production depends heavily upon a robust intelligence collection management system. IMINT is complemented by non-imaging MASINT electro-optical and radar sensors.
Military intelligence is a military discipline that uses information collection and analysis approaches to provide guidance and direction to assist commanders in their decisions. This aim is achieved by providing an assessment of data from a range of sources, directed towards the commanders' mission requirements or responding to questions as part of operational or campaign planning. To provide an analysis, the commander's information requirements are first identified, which are then incorporated into intelligence collection, analysis, and dissemination.
The Lockheed U-2, nicknamed "Dragon Lady", is an American single-engine, high altitude reconnaissance aircraft operated from the 1950s by the United States Air Force (USAF) or the Central Intelligence Agency (CIA). It provides day and night, high-altitude (), all-weather intelligence gathering. Lockheed Corporation originally proposed it in 1953, it was approved in 1954, and its first test flight was in 1955. It was flown during the Cold War over the Soviet Union, China, Vietnam, and Cuba.
Artificial intelligence, big data, and advances in computing power have triggered a technological revolution that may have enormous bearing on the workplace and the labor market. This course provides
Introduction aux techniques de l'Intelligence Artificielle, complémentée par des exercices de programmation qui montrent les algorithmes et des exemples de leur application à des problèmes pratiques.
Explores how AI/ML is shaping the future workplace, focusing on enterprise systems and processes, and discusses the current state of AI/ML adoption in enterprises.