Synthetic Aerial Data for AI Training and Simulation
Generate high-quality synthetic aerial imagery to train robust autonomous systems for AI-powered maritime surveillance, terrestrial monitoring, infrastructure inspection, and UAV autonomy.
Trusted by airborne ISR and autonomy programs
Object Detection from Unmanned Aerial Vehicles (UAVs)
From open oceans to remote terrestrial terrain, simulate synthetic aerial imagery for any detection scenario across varying altitudes and environmental conditions.
Terrestrial Object Detection from UAVs
Generate high-quality synthetic aerial imagery to train AI models for accurate detection, classification, and tracking of ground-based objects across complex terrestrial environments, from automobiles and trucks to military vehicles. Build robust UAV perception systems for defense surveillance, border monitoring, and infrastructure security.
Counter UAV Detection and Classification Systems
Enable systems to identify and distinguish between different UAV types, including their operational roles and configurations. Simulate scenarios involving overlapping airspace, where multiple drones must be tracked and categorized in real time.
Synthetic Aerial Data Generation Platform for UAV Perception
Configure altitude, camera perspective, distance, and multiple sensor modalities including Electro-Optical (EO), thermal, infrared (IR), radar, and depth to replicate real-world operating conditions. Generate diverse aerial scenarios across variable weather, lighting, and terrain, with support for occluded, partially visible, and camouflaged objects.
Airport Inspection from Unmanned Aerial Vehicles (UAVs)
Identify and classify commercial planes, helicopters, and military aircraft from an overhead perspective. Simulate runway environments, aircraft positioning, and fleet distributions under varying operational conditions. Enable accurate aerial classification and structured airport intelligence at scale.
UAV Collision Avoidance and Airspace Monitoring
Generate synthetic aerial imagery to train UAV systems for detecting and tracking nearby aircraft, helicopters, drones, and eVTOL platforms in shared airspace. Simulate near-miss events, congested flight corridors, and varying visibility conditions to improve collision avoidance, airspace awareness, and autonomous navigation performance.
Aerial Asset Library: UAV & Drone Assets for Scenario Generation
Build aerial scenarios using a large-scale library of real-world UAVs and drones, including quadcopters, fixed-wing UAVs, VTOL systems, and tactical unmanned platforms. Simulate diverse aerial operations across maritime zones, coastlines, industrial zones, remote terrains, forests and deserts.
Every asset comes with structured metadata and automated annotations, enabling teams to generate consistent, high-quality synthetic aerial datasets for UAV perception, detection, tracking, and autonomous navigation.
Identify and Fix UAV Perception Failures Faster
Stardust helps teams uncover blind spots in UAV perception models by recreating failure scenarios in simulation. Generate targeted synthetic datasets for rare events, difficult operating conditions, and edge cases that are underrepresented in real-world data.
Accelerate model improvement by systematically testing, validating, and addressing performance gaps before deployment, without costly field data collection.
Altitude, camera perspective, distance and sensor configuration are all set per scenario, so the imagery matches your platform and payload.
How is synthetic aerial data used for UAV object detection?
Stardust renders aerial scenes at the altitude, angle and sensor configuration of your platform, with every object labeled. Teams train detectors on that data and test them against held-out real imagery.
How are aerial datasets labeled?
Automatically. Every frame comes with pixel-perfect segmentation, bounding boxes and scenario metadata generated from the 3D scene itself, so there is no manual annotation or QA pass.
How does tiny object detection in aerial images work with synthetic data?
Objects that span a few pixels from altitude are where hand labeling is least reliable. Synthetic data labels them exactly, across ranges and look angles, so models learn the hardest cases with clean ground truth.
Can aerial datasets be used for object tracking?
Yes. Stardust generates labeled sequences with consistent object identities across frames, for multi-object tracking and counter-UAS work.
What types of environments are used in aerial datasets?
Open ocean and coastlines, forests, deserts, highways, airports and industrial zones, under varying weather, lighting and visibility.
Unlock Unlimited Aerial Datasets
See Stardust generate a dataset for your platform, payload and targets.
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