SPARK-2021: SPAcecraft Recognition leveraging Knowledge of space environment

SPARK is a large-scale multi-modal (RGB + depth) synthetic image dataset for space object recognition and detection, generated under a photo-realistic space simulation environment. It was released by the CVI² group at SnT, University of Luxembourg in the context of the SPARK Challenge at IEEE ICIP 2021.

The dataset targets Space Situational Awareness (SSA) applications — on-orbit servicing, active debris removal, formation flying, and rendezvous & proximity operations — where the scarcity of annotated spaceborne imagery is a primary bottleneck for data-driven perception.

Modalities	RGB, depth (segmentation masks available in the original release)
Images	~150k RGB + ~150k depth
Classes	11 (10 satellite models + 1 combined debris class)
Annotations	Class label + 2D bounding box per image
Simulator	Unity3D, LEO scenarios around a photo-realistic Earth
Type	Fully synthetic

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Dataset structure

The dataset is published as WebDataset shards so that it streams efficiently and pairs the two modalities inside a single sample:

data/
├── train/
│   ├── spark-train-000000.tar
│   ├── spark-train-000001.tar
│   └── ...
└── validation/
    ├── spark-validation-000000.tar
    └── ...

Each sample inside a shard has the form:

<key>.rgb.jpg      # RGB image
<key>.depth.png    # 16-bit depth map, same geometry as the RGB frame
<key>.json         # {"label": 3, "class": "Calipso", "bbox": [R_min, C_min, R_max, C_max]}

Splits

Split	Samples	Notes
train	TODO	Public training split of the SPARK 2021 challenge
validation	TODO	Public validation split (labels released)
test	not included	Challenge test labels were kept private

Class composition of the full release: 12,500 images per satellite class (10 classes) and 5,000 images per debris object across 5 debris models, all merged into a single Debris class (25,000 images) — 150,000 images in total per modality.

Classes

Index	Class	Type
0	AcrimSat	Satellite
1	Aquarius	Satellite
2	Aura	Satellite
3	Calipso	Satellite
4	Cloudsat	Satellite
5	CubeSat	Satellite (1RU generic CubeSat)
6	Debris	Debris (5 models merged)
7	Jason	Satellite
8	Sentinel-6	Satellite
9	Terra	Satellite
10	TRMM	Satellite

Satellite models come from NASA 3D Resources. Debris objects are corrupted-texture parts of satellites and rockets: space shuttle external tank, orbital docking system, damaged communication dish, thermal protection tiles, and connector ring.

⚠️ Bounding-box convention

Boxes follow the original SPARK convention, which is row/column ordered, not the usual x, y ordering:

bbox = [R_min, C_min, R_max, C_max]   #  ==  [y_min, x_min, y_max, x_max]

Conversions:

r_min, c_min, r_max, c_max = bbox

# Pascal VOC / torchvision  (x1, y1, x2, y2)
voc = [c_min, r_min, c_max, r_max]

# COCO  (x, y, w, h)
coco = [c_min, r_min, c_max - c_min, r_max - r_min]

# YOLO (normalised cx, cy, w, h) for an image of size (H, W)
yolo = [((c_min + c_max) / 2) / W, ((r_min + r_max) / 2) / H,
        (c_max - c_min) / W, (r_max - r_min) / H]

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Usage

from datasets import load_dataset

ds = load_dataset("<org>/spark-2021", split="train")
sample = ds[0]

sample["rgb"]      # PIL.Image, RGB
sample["depth"]    # PIL.Image, 16-bit single channel
sample["label"]    # int in [0, 10]
sample["bbox"]     # [R_min, C_min, R_max, C_max]

Streaming (recommended — the full dataset is large)

ds = load_dataset("<org>/spark-2021", split="train", streaming=True)
for sample in ds.take(8):
    print(sample["label"], sample["bbox"], sample["rgb"].size)

RGB-only classification

ds = load_dataset("<org>/spark-2021", split="train").remove_columns("depth")

Depth handling

Depth maps are stored as 16-bit PNGs. Convert to a float array before use:

import numpy as np
depth = np.asarray(sample["depth"], dtype=np.float32)   # raw sensor units

Note that the released depth maps are known to be noisy and to contain holes; several challenge entries applied morphological opening / hole filling before using them.

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Dataset creation

SPARK was rendered in Unity3D, with:

• Earth model — high-resolution textured 16k-polygon model based on the NASA Blue Marble collection, including clouds, cloud shadows, and atmospheric outer scattering.
• Background — high-resolution ESO panorama of the Milky Way.
• Target — one of the 10 satellite models or 5 debris models, randomly placed inside the camera field of view, in LEO.
• Chaser — observer platform carrying a pinhole RGB camera with known intrinsics plus a depth camera.

The Sun and the Earth are randomly rotated about their axes in every frame. The dataset is deliberately spanned along four axes of variation:

1. Scene illumination — including extreme cases where sunlight directly faces the sensor or reflects off the target/Earth, producing lens flare and sensor blooming.
2. Scene background — Earth-in-background (rich texture, ocean/cloud specularity) vs. deep space (featureless, sparse stars).
3. Range — varying camera-to-target distance, i.e. varying target occupation of the frame.
4. Sensor noise — zero-mean white Gaussian noise at varying levels, emulating the high dynamic range and small-sensor noise of spaceborne imagers.

The baseline study in the SPARK paper found accuracy degrading systematically with lower illumination, longer range, and increasing noise, with the far-range + low-illumination subset being the hardest regime. Fine-tuning ImageNet-pretrained backbones outperformed both random initialisation and frozen feature extraction, and RGB-D fusion reached 90.05% validation accuracy versus 75% (RGB only) and 88.01% (depth only) at 64×64 input resolution.

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Original challenge protocol

The ICIP 2021 competition defined two tasks and two dedicated metrics.

Task 1 — Classification. Errors were weighted by severity: misclassifying a satellite as another satellite (level 1/4), a satellite as debris (level 2/4), and — most severely — debris as a satellite (level 4/4). Ranking used an F2-score-based metric combined with the proportion of correctly classified non-debris samples.

Task 2 — Detection. Inspired by the COCO protocol: the proportion of images with both a correct class prediction and an IoU above threshold, averaged over several IoU thresholds.

These metrics are documented here for reproducibility; this repository does not host an evaluation server.

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Intended uses

• Spacecraft and debris classification and detection under space imaging conditions
• Multi-modal RGB-D fusion research
• Robustness studies with respect to illumination, range, and sensor noise
• Pretraining / representation learning for downstream proximity-operations perception

Out of scope and limitations

• Fully synthetic. Models trained on SPARK alone will exhibit a substantial sim-to-real domain gap and should not be treated as flight-qualified without hardware-in-the-loop or on-orbit validation.
• Renderer artefacts. Illumination, flare, and noise are approximations of the true space radiometric environment; depth maps are simulated, not from a flight-representative sensor.
• Class imbalance. The single Debris class aggregates five geometrically distinct objects.
• No pose labels. SPARK provides class and bounding box only. For 6-DoF pose, see SPEED / SPEED+ or URSO.

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Citation

If you use SPARK, please cite both the dataset paper and the challenge paper:

@inproceedings{musallam2021sparkchallenge,
  title     = {Spacecraft Recognition Leveraging Knowledge of Space Environment:
               Simulator, Dataset, Competition Design and Analysis},
  author    = {Musallam, Mohamed Adel and Gaudilli{\`e}re, Vincent and Ghorbel, Enjie and
               Al Ismaeil, Kassem and Perez, Marcos Damian and Poucet, Michel and Aouada, Djamila},
  booktitle = {IEEE International Conference on Image Processing Challenges (ICIPC)},
  pages     = {11--15},
  year      = {2021},
  doi       = {10.1109/ICIPC53495.2021.9620184}
}

Acknowledgements

Dataset produced by the Computer Vision, Imaging & Machine Intelligence (CVI²) research group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, in collaboration with LMO.

Contact

Project page: https://cvi2.uni.lu/spark-2021/ Issues with this Hugging Face mirror: open a discussion on this repository.