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satsim

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SatNet annotated image simulator using Tensorflow.

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# SatSim

[![Unit Tests](https://github.com/ssc-ai/satsim/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/ssc-ai/satsim/actions/workflows/unit-tests.yml)

**SatSim source code was developed under contract with AFRL/RDSM, and is approved for public release under Public Affairs release approval #AFRL-2022-1116.**

SatSim is a GPU-accelerated synthetic data generation engine for space domain awareness scenes. It renders electro-optical imagery of resident space objects (RSOs), produces labels and metadata for detection workflows, and includes analytical RADAR and passive-RF paths for measurement simulation. Configurations are plain JSON or YAML, so a single scenario can be fixed, procedurally sampled, or generated from Python functions.

![Real and SatSim-generated rate-track imagery](https://raw.githubusercontent.com/ssc-ai/satsim/master/docs/images/satsim-real-vs-synthetic.jpg)
_Real Raven rate-track imagery and a SatSim-rendered counterpart, adapted from the public SatSim SPIE Defense + Commercial Sensing paper._

![PANDORA wide-field synthetic imagery](https://raw.githubusercontent.com/ssc-ai/satsim/master/docs/images/satsim-pandora-wide-field.jpg)
_Synthetic PANDORA wide-field imagery generated for sensor modeling, adapted from the same paper._

## What SatSim Does

- Renders stars, RSOs, point-spread functions, sky background, dark current, shot noise, read noise, pixel response, bias, and analog-to-digital conversion.
- Supports fast 2D image-plane targets and 3D astrometric geometry using TLE/SGP4, two-body state vectors, ephemerides, great-circle tracks, and angles-only observations.
- Models Gaussian and POPPY physical-optics PSFs, optional turbulence, tiled rendering for large images, FITS compression, segmentation masks, and ground-truth planes.
- Adds environmental effects including skyglow, moonlight, twilight, daytime sky brightness, and procedural cloud attenuation and glow.
- Generates nominal, RPO, breakup, collision, Monte Carlo, and sensor-variation scenarios through `$sample`, `$generator`, `$function`, `$pipeline`, `$compound`, `$ref`, and `$import`.
- Writes SatNet-style annotations, FITS image frames, annotated JPEGs, APNG movies, CZML visualizations, analytical EO observations, RADAR observations, and passive-RF TDOA/FDOA observations.
- Can augment TensorFlow datasets by injecting synthetic targets into real imagery during training.

## Quick Start

Install the released package:

```bash
pip3 install satsim
```

Run an EO image simulation from a JSON, `.yml`, or `.yaml` configuration:

```bash
satsim --debug INFO run --device 0 --mode eager --output_dir output/ input/config.json
```

Use SatSim from Python:

```python
from satsim import gen_multi, load_json

ssp = load_json("input/config.json")
ssp["sim"]["samples"] = 50
ssp["geometry"]["obs"]["list"][0]["mv"] = 17.5

gen_multi(ssp, eager=True, output_dir="output/")
```

The CLI writes each run to a timestamped output directory. Typical EO products include `ImageFiles/*.fits`, `Annotations/*.json`, `AnnotatedImages/*.jpg`, optional segmentation TIFFs, optional ground-truth TIFF stacks, `movie.png`, and `satsim.czml`. EO, RADAR, and passive-RF runs preserve the transformed `config.json`; analytical products are written under `AnalyticalObservations`.

## Documentation

- [Installation](docs/installation.md)
- [Usage](docs/usage.md)
- [JSON schema](schema/v1/README.md)
- [Developer setup](CONTRIBUTING.md#getting-started-for-developers)
- [Release history](HISTORY.md)

satsim

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