Home CrowdAI’s Novel Machine Learning Technology Helps Identify the Make and Model of Imaged Aircraft in a Matter of Seconds

CrowdAI’s Novel Machine Learning Technology Helps Identify the Make and Model of Imaged Aircraft in a Matter of Seconds

by Ant Sh
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CrowdAI's Novel Machine Learning Technology Helps Identify the Make and Model of Imaged Aircraft in a Matter of Seconds

CrowdAI, the leader in code-free AI enablement tools for computer vision (CV), today announced its latest innovation: globally-scalable aircraft identification from satellite imagery. A breakthrough approach now enables a single model to detect and ID a massive catalog of global military and civilian aircraft. Importantly, the list of recognizable aircraft can be amended in minutes from as few as a single image. Without having to curate and label thousands of examples of each aircraft to train a CV model, for the first time, remotely monitoring airports, globally, is a commercial reality and a national security boon.

The traditional CV approach to a multi-class aircraft identification model for Air Order of Battle (AOB) would be to curate and label thousands of examples of each airframe for every sensor. Quickly, the scale of the problem becomes apparent. Not only is the gathering and labeling of data a Herculean task, but finding personnel capable of correctly tagging Su-25, Il-76, COKEs, and CURLs, etc., means a lot of training and quality checks. A lot. Those limitations have kept AOB narrowly scoped, ignoring far more targets than were being put into analytic production. CrowdAI’s novel approach not only achieves analytic performance expectations, but scales globally with relatively minimal effort.

The time it takes to add a new aircraft to the model is measured in seconds, rather than the weeks or months it takes following industry’s or academia’s standard approaches. CrowdAI ensembles time-tested GEOINT methodologies with deep learning, the combination of which is a performant computer vision model that can detect any aircraft. What’s more, that same model can correctly identify any aircraft after seeing only a single example.

The production-ready model is instantiated in an automated workflow that pulls imagery over areas of interest, runs inference, and outputs model predictions in standard GOEINT format for inclusion into a database of record or common operating picture. Absent new types of aircraft arriving in the region, an analyst can “set it and forget it.”

Using GEOINT “first principles,” there is no practical limit to the scalability of CrowdAI’s approach to automating AOB analysis. Rather than slog through massive amounts of data labeling and the what, when, and where of foreign air operations, CrowdAI-empowered analysts can focus on the why and what’s next that are critical to maintaining information dominance and decision advantage.