Sensor archives are rich in routine imagery. The real shortage is coverage. Perception models depend on examples across object classes, viewing angles, ranges, backgrounds, weather, and sensor conditions, but the cases that are hardest to collect are usually the least represented: rare object classes, unfamiliar visual signatures, unusual viewpoints, partial occlusion, or uncommon combinations of environmental and sensor conditions.

Those gaps can persist even after additional collection. A program may gather thousands of new frames and still lack the precise combination of class and conditions needed to evaluate or improve a model. Synthetic data generation provides a way to define that gap and produce controlled examples on demand.

In our last post, we introduced Flow for mission data, a machine-first engine that converts raw sensor data into trustworthy labels at scale. While Flow helps programs turn existing footage into governed training data, it leaves one key problem: coverage. Today, micro1 is announcing Flow-gen, a synthetic data generation capability that turns coverage requirements into controlled, physics-based scenarios.

From requirement to scenario

Flow-gen combines physics-based simulation with generative AI to create realistic sensor data for specific coverage gaps. Simulation provides control over scene geometry, environmental conditions, sensor parameters, and labels, while generative models reproduce the textures and sensor characteristics of real-world imagery. Together, they create a data-on-demand capability for conditions that are difficult, costly, or impractical to collect.

Flow-gen begins with a coverage requirement. A team might specify a rare object class, an uncommon viewing angle, partial occlusion, degraded visibility, and a particular sensor configuration. The system translates those parameters into a controlled data-generation workflow, allowing teams to vary one condition at a time rather than depend on an exact scenario appearing in collected footage.

The requirement is compiled into a versioned scenario manifest that records scene composition, object attributes, visibility, environment, sensor configuration, and generation parameters. Every delivered image links back to its manifest, allowing teams to reproduce a dataset, vary individual conditions, and compare results across versions.

A single simulated scene can also produce aligned outputs across supported sensor modalities while preserving shared geometry and scene state. Flow-gen then transforms the simulated imagery to reflect the characteristics of the selected sensor while preserving the underlying scene structure.

Validating the delivered data

The simulated scene provides object identity, location, pose, depth, visibility, and other labels. These annotations provide a strong starting point, but rendering and sensor-domain transformation can alter object boundaries or visibility in the delivered frame. Validation therefore focuses on the final image-label pair.

Flow-gen checks each delivered output and routes uncertain cases to qualified reviewers. Failed samples are corrected or removed. Every accepted image carries its source tag, scenario manifest, generation configuration, and validation history. Programs can trace each sample and control how synthetic data enters a training or evaluation run.

Qualified reviewers also assess sensor realism and help construct representative evaluation sets from real data. Their decisions become training and quality-control signals for the models that automate the pipeline. As those decisions accumulate, the system applies the same standards across a growing volume of generated data.

Measuring performance on real sensor data

Synthetic imagery enters the dataset as a distinct, tagged data source. Teams can separate synthetic and real sensor data during experiments and measure the effect of specific scenario sets.

The effect of synthetic data is evaluated on held-out real sensor data and reported by object class and operating condition. The evaluation answers a practical question: did the generated data improve performance on the real imagery the program will encounter?

The results guide the next version of the scenario manifests. A dataset advances when it produces a measurable improvement on real imagery. Weak results lead to a focused iteration: revise the scene geometry, environment, sensor model, or sampling strategy, then generate and test the next version.

Real-world collection remains essential for grounding and evaluation. Synthetic generation expands the training coverage available between collection opportunities and supplies focused examples for classes and conditions that appear infrequently in the archive.

Flow-gen gives programs a repeatable process for addressing a coverage gap: define the requirement, generate controlled scenarios, validate the outputs, test transfer on real data, and refine the manifest. Programs can change one parameter at a time and measure its effect without organizing a new collection effort for every iteration.

Flow and Flow-gen address complementary parts of mission data readiness. Flow converts collected footage into labeled, governed data. Flow-gen adds controlled coverage for rare classes and operating conditions. Together, they provide a repeatable pipeline for training and evaluating perception and threat-detection models across the conditions they will encounter in real-world use.

This is the second in a series on how micro1 builds data infrastructure for mission AI.

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