Update README.md

README.md

@@ -1,3 +1,304 @@
 ---
-license: cc-by-4.0
+license: cc-by-nc-sa-4.0
+pretty_name: SPARK-2021 (SPAcecraft Recognition leveraging Knowledge of space environment)
+language:
+  - en
+size_categories:
+  - 100K<n<1M
+task_categories:
+  - image-classification
+  - object-detection
+task_ids:
+  - multi-class-image-classification
+tags:
+  - spacecraft
+  - satellite
+  - space-debris
+  - space-situational-awareness
+  - rgb-d
+  - multi-modal
+  - synthetic
+annotations_creators:
+  - machine-generated
+language_creators:
+  - machine-generated
+source_datasets:
+  - original
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/train/spark-train-*.tar
+      - split: validation
+        path: data/validation/spark-validation-*.tar
+dataset_info:
+  features:
+    - name: rgb
+      dtype: image
+    - name: depth
+      dtype: image
+    - name: label
+      dtype:
+        class_label:
+          names:
+            '0': AcrimSat
+            '1': Aquarius
+            '2': Aura
+            '3': Calipso
+            '4': Cloudsat
+            '5': CubeSat
+            '6': Debris
+            '7': Jason
+            '8': Sentinel-6
+            '9': Terra
+            '10': TRMM
+    - name: bbox
+      sequence: int32
+      length: 4
+    - name: filename
+      dtype: string
 ---
+
+# 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](https://cvi2.uni.lu/spark-2021/)
+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 |
+
+---
+
+## 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](https://nasa3d.arc.nasa.gov/). 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:
+
+```python
+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]
+```
+
+---
+
+## Usage
+
+```python
+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)
+
+```python
+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
+
+```python
+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:
+
+```python
+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.
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+## Citation
+
+If you use SPARK, please cite both the dataset paper and the challenge paper:
+
+```bibtex
+
+@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.