Oct 20, 2025 06:49 PM
(This post was last modified: Oct 20, 2025 06:53 PM by Magical Realist.)
Here's a clear case of later technology confirming the authenticity of old photographs of uaps. It is hard to deny what is staring right in your face..
https://www.facebook.com/reel/1894002721495966
"Artificial intelligence is increasingly used to detect Unidentified Aerial Phenomena (UAPs) by analyzing large-scale sensor data and video footage to filter out mundane objects and identify genuine anomalies. Prominent efforts in this field include NASA's research and the Galileo Project at Harvard University, both of which are developing advanced AI models to bring scientific rigor to UAP studies.
How AI detects UAPs
Continuous sky monitoring: Organizations are deploying observatories equipped with multi-spectral sensors—including optical, infrared, and radio—that continuously scan the sky. This approach provides a consistent, high-volume data stream, unlike sporadic human observations.
Filtering known objects: AI and machine learning are uniquely suited to the rapid analysis of this large-scale data. Algorithms are trained on vast datasets of known objects, such as birds, planes, drones, and weather balloons, to accurately classify and dismiss them. The Galileo Project, for instance, uses algorithms like "You Only Look Once" (YOLO) to identify and filter out these known phenomena.
Identifying anomalies: After filtering, the AI focuses on identifying outliers and anomalies that do not fit the patterns of known objects. This can include analyzing unusual flight paths, speeds, or electromagnetic signatures. A machine learning model called HyperNeuron has been developed specifically to detect these signal anomalies and reduce false positives caused by sensor glitches.
Video analysis: For existing UAP video footage, computer vision techniques are used to analyze trajectories and movement. Researchers can reconstruct the object's flight path and use trigonometric calculations to assess if the visual information, such as perceived speed, is distorted by factors like parallax.
Multi-sensor fusion: Advanced systems combine data from various sensors (visible light cameras, infrared, LiDAR, and quantum radar) to create a more complete picture. AI is used to integrate this data and address resolution gaps, improving the accuracy of both detection and tracking."
https://www.facebook.com/reel/1894002721495966
"Artificial intelligence is increasingly used to detect Unidentified Aerial Phenomena (UAPs) by analyzing large-scale sensor data and video footage to filter out mundane objects and identify genuine anomalies. Prominent efforts in this field include NASA's research and the Galileo Project at Harvard University, both of which are developing advanced AI models to bring scientific rigor to UAP studies.
How AI detects UAPs
Continuous sky monitoring: Organizations are deploying observatories equipped with multi-spectral sensors—including optical, infrared, and radio—that continuously scan the sky. This approach provides a consistent, high-volume data stream, unlike sporadic human observations.
Filtering known objects: AI and machine learning are uniquely suited to the rapid analysis of this large-scale data. Algorithms are trained on vast datasets of known objects, such as birds, planes, drones, and weather balloons, to accurately classify and dismiss them. The Galileo Project, for instance, uses algorithms like "You Only Look Once" (YOLO) to identify and filter out these known phenomena.
Identifying anomalies: After filtering, the AI focuses on identifying outliers and anomalies that do not fit the patterns of known objects. This can include analyzing unusual flight paths, speeds, or electromagnetic signatures. A machine learning model called HyperNeuron has been developed specifically to detect these signal anomalies and reduce false positives caused by sensor glitches.
Video analysis: For existing UAP video footage, computer vision techniques are used to analyze trajectories and movement. Researchers can reconstruct the object's flight path and use trigonometric calculations to assess if the visual information, such as perceived speed, is distorted by factors like parallax.
Multi-sensor fusion: Advanced systems combine data from various sensors (visible light cameras, infrared, LiDAR, and quantum radar) to create a more complete picture. AI is used to integrate this data and address resolution gaps, improving the accuracy of both detection and tracking."
