Custom Satellite Imagery Datasets
Physics-accurate, mission-specific training data for on-board AI built to your sensor, orbit, and target constellation.
Design Your Dataset ↓Mission-specific training data, built to your specifications
Getting high-quality, mission-specific training data for on-board AI is hard. Public benchmarks like SPEED, SPARK 2022, and DLVS³ are anchored to a handful of fixed spacecraft, wrong targets, wrong sensors, wrong orbits for your mission.
SatScenes delivers custom photorealistic labeled datasets built to your requirements: your sensor geometry, your orbital scenario, your target constellation. Every dataset is generated with a physics-accurate rendering engine producing imagery that is photometrically faithful to real space cameras, not just visually plausible.
Each frame independently samples the full scene parameter space: target scale, in-frame position, attitude, lighting condition, background type, and focus, ensuring no two frames are alike and your model trains on genuinely diverse data. Class composition is controlled by probability weights, so you define exactly which satellite models appear and in what proportion.
Output is RGB frames with per-frame JSON metadata: bounding box annotations, camera intrinsics, satellite model, position and attitude, ready to convert into YOLO, COCO, or any annotation format your pipeline expects.
Train satellite detection and classification models across LEO, MEO, and GEO orbits. Datasets cover diverse lighting conditions, background types, and target attitudes, giving your model the breadth to perform in real operational environments.
Enable autonomous proximity operations, rendezvous, and satellite inspection with models trained on photorealistic imagery matched to your camera geometry and mission profile.
Don't train against irrelevant targets. Define exactly which satellites your system will encounter and generate a dataset focused on your catalogue, improving model precision and reducing false positives in deployment.
Future dataset types will support position, velocity, and pose estimation, extending the service to academic research and next-generation on-board capabilities. New satellite models can be added without pipeline changes.
Powered by the SatScenes rendering pipeline
Custom datasets are generated through SatScenes, a physics-accurate rendering pipeline built specifically for this purpose. From a single JSON configuration file to a fully labeled dataset, the entire process is automated: no manual scene setup, no hand-labeling.
The sensor model goes beyond geometry: it simulates quantum efficiency per color channel, 12-bit ADC, readout noise, dark current, pixel response non-uniformity, and Gaussian PSF, producing imagery that faithfully replicates the photometric characteristics of real space cameras. Support for lens distortion coefficients (radial k1/k2/k3, tangential p1/p2) is in active development and will enable faithful replication of any specific camera geometry.
Define target models, class weights, orbit altitude range, sensor profile and scene diversity parameters via JSON config.
The rendering engine executes the full scene graph (Earth, target satellite, lighting, camera), one frame per parameter sample, fully automated.
RGB frames with per-frame JSON metadata, including bounding box coordinates, camera intrinsics, and full scene parameters, ready to convert into YOLO, COCO, or any custom annotation format.
Examples of deliverable imagery
I'm Lorenzo Voli, a Space Engineer and AI researcher with 10+ years of software experience in particular for spacecraft avionics and spacecraft HIL framework development, currently serving as Institutional & Defense BU Tech Strategist at D-Orbit S.p.A.
My work spans the full stack of spacecraft software: from RTOS embedded systems and CAN bus protocols on ION spacecraft, to leading avionics SW development for a SAR satellite of the Iride constellation, including payload data handling, FDIR, and symmetric encryption subsystems.
I hold an MSc in Space Engineering from Politecnico di Milano, where my thesis research on adaptive hybrid control for robotic manipulators was published in the Journal of Intelligent & Robotic Systems (Springer, 2018).
SatScenes is a personal research initiative born from the need for flexible, physics-accurate training data for on-board AI, a gap I encountered firsthand in operational satellite programs.
Journal of Intelligent & Robotic Systems, Springer, 2018
Read on Springer ↗Interested in a custom dataset, a collaboration, or just curious about what I'm building? I'd love to hear from you.
If you are a university professor or student working on AI applied to space technology (detection, navigation, pose estimation), I'd be glad to collaborate on research datasets and joint exploration.
If you're a space and AI enthusiast looking for adventure, let's connect, share dreams, and see where curiosity takes us.
If you're a company integrating AI on-board and need custom training datasets to accelerate your development: I'm building exactly what you need. Don't miss the opportunity to lead the race.
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