The defense enterprise has no shortage of aerial video. The real shortage is usable training data. A single sortie can generate hours of EO/IR footage, but only a fraction is ever labeled with the quality, consistency, and provenance required for model training and evaluation. Collection has scaled; manual annotation has not. As a result, perception models are built from the narrow slice of footage that programs have the time and budget to review. For mission AI, the bottleneck is no longer gathering imagery. It is converting raw footage into trustworthy labels at operational scale.

Today, micro1 is announcing Flow for mission data—a machine-first annotation engine for aerial EO/IR video that labels entire corpora at machine speed while reserving human judgment for the cases where it can materially improve the outcome. In our published robotics work, this architecture reduced raw human correction load threefold at constant quality. We have now adapted the same engine to the scale, sensor characteristics, and quality demands of aerial mission data.

How it works

Most labeling tools begin with a human reviewing every frame and use automation to accelerate individual actions. Flow takes a machine-first approach. It processes the full dataset automatically, generates candidate annotations, and applies multiple layers of quality control before determining where human review is needed.

Instead of treating every record equally, Flow concentrates expert attention on the portions of the data that are difficult, unusual, or operationally important. Routine annotations can move through the pipeline with limited intervention, while records that require additional judgment are routed to qualified reviewers.

Humans remain on the loop throughout the process. Reviewers verify, correct, and adjudicate machine-generated annotations rather than creating every label from scratch. The resulting data retains a traceable record of how it was produced, reviewed, and accepted.

Flow also makes large collections easier to work with. Analysts can search and organize mission data, identify relevant examples, and move selected records into review, training, and evaluation workflows without manually inspecting entire video archives.

For aerial EO/IR data, this means a program can process long, complex video collections while directing human effort toward the moments most likely to affect model performance. The result is a labeling workflow that scales with the difficulty of the data—not simply with the number of frames collected.

Built for the mission environment

Aerial mission data does not behave like commercial imagery, and neither do the requirements around it. Flow can be configured around mission-specific ontologies and versioned annotation guidelines, making “correct” an explicit, auditable standard rather than an individual labeler’s interpretation. The platform is also designed to route difficult or ambiguous records to reviewers with the appropriate subject-matter expertise.

micro1 brings an established recruiting and qualification infrastructure to that challenge. Across its commercial operations, micro1 has built a network of hundreds of thousands of experts, with millions of candidates evaluated through its AI interview system, Zara. That infrastructure can be used to identify, screen, and onboard personnel with relevant backgrounds—including pilots, UAS operators, maintainers, and veterans—at the speed and scale that mission requirements demand.

From labeled data to decision advantage

The output of Flow is not just labels. It is a governed record: every annotation traceable to its source, model versions, validation results, and acceptance state, ready to be reused for training, calibration, and evaluation. Accepted corrections feed directly back into the models that produced them, so the pipeline gets better with every hour of footage it processes.

That changes the economics of mission AI. Labeling stops being a labor cost that scales with collection and becomes a system whose human cost scales with difficulty. Programs that could annotate a fraction of their take can now put all of it to work, and put their experts on the ten percent of frames that decide whether a model can be trusted downrange.

This is the first in a series on how micro1 builds data infrastructure for mission AI. Next: generating the training data that doesn't exist yet.

To learn more about Flow for government programs, contact jerome@micro1.ai