| --- |
| license: cc-by-4.0 |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': test |
| '1': train |
| '2': valid |
| splits: |
| - name: train |
| num_bytes: 137284657 |
| num_examples: 837 |
| - name: validation |
| num_bytes: 17066298 |
| num_examples: 104 |
| - name: test |
| num_bytes: 17015125 |
| num_examples: 103 |
| download_size: 171383973 |
| dataset_size: 171366080 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| - split: test |
| path: data/test-* |
| --- |
| |
| # Dataset Card: SIVED (SAR Image Vehicle Detection) |
|
|
| ## Important Notice |
|
|
| **This dataset was NOT created by the uploader.** It was originally curated and published by the researchers listed below. It is shared here on Kaggle and Hugging Face solely for accessibility and ease of use in machine learning pipelines. All credit belongs to the original authors. |
|
|
| ## Authors |
|
|
| **Xin Lin, Bo Zhang, Fan Wu, Chao Wang, Yali Yang, and Huiqin Chen** |
|
|
| CAESAR-Radi Research Group |
|
|
| ## Original Sources |
|
|
| | Source | Link | |
| |--------|------| |
| | GitHub Repository | https://github.com/CAESAR-Radi/SIVED | |
| | Paper (Remote Sensing, MDPI) | https://www.mdpi.com/2072-4292/15/11/2825 | |
| | Google Drive (original download) | https://drive.google.com/drive/folders/1ntqNSMQqMwu8RE4uK2z632cMQEi4yid6 | |
|
|
| ## Dataset Description |
|
|
| SIVED is a SAR (Synthetic Aperture Radar) image dataset designed for vehicle detection using rotatable bounding box annotations. It aggregates high-resolution SAR imagery from three distinct radar sources across multiple frequency bands. |
|
|
| | Property | Value | |
| |----------|-------| |
| | Total Images | 1,044 (512 x 512 px, grayscale) | |
| | Total Annotations | 12,013 oriented bounding boxes | |
| | Classes | 1 (Vehicle) | |
| | Annotation Format | DOTA (8-point rotated bounding box) | |
| | Splits | Train: 837 / Valid: 104 / Test: 103 | |
|
|
| ### Radar Sources |
|
|
| | Source | Organization | Band | Polarization | Resolution | |
| |--------|-------------|------|--------------|------------| |
| | FARAD | Sandia National Laboratory | Ka/X | VV/HH | 0.1m x 0.1m | |
| | MiniSAR | Sandia National Laboratory | Ku | - | 0.1m x 0.1m | |
| | MSTAR | U.S. Air Force | X | HH | 0.3m x 0.3m | |
|
|
| ### Annotation Format |
|
|
| Each annotation provides eight coordinates defining the four corners of an oriented bounding box, a class label, and a difficulty flag: |
|
|
| ``` |
| x1 y1 x2 y2 x3 y3 x4 y4 class_name difficulty |
| ``` |
|
|
| - `difficulty=0`: Easy (clearly visible vehicles) |
| - `difficulty=1`: Hard (partially occluded, low contrast, or very small) |
|
|
| ## License |
|
|
| The original SIVED repository on GitHub does not specify an explicit license file. The accompanying paper was published in *Remote Sensing* (MDPI), an open-access journal that publishes under the **Creative Commons Attribution (CC BY 4.0)** license, which permits sharing and adaptation with appropriate credit. |
|
|
| Users of this dataset should cite the original paper and comply with any terms set by the original authors. If there is any concern regarding redistribution rights, please refer to the original repository or contact the authors directly. |
|
|
| ## Citation (Required) |
|
|
| If you use this dataset in any capacity, please cite the original work: |
|
|
| ```bibtex |
| @Article{rs15112825, |
| author = {Lin, Xin and Zhang, Bo and Wu, Fan and Wang, Chao and Yang, Yali and Chen, Huiqin}, |
| title = {SIVED: A SAR Image Dataset for Vehicle Detection Based on Rotatable Bounding Box}, |
| journal = {Remote Sensing}, |
| volume = {15}, |
| number = {11}, |
| pages = {2825}, |
| year = {2023}, |
| doi = {10.3390/rs15112825}, |
| url = {https://www.mdpi.com/2072-4292/15/11/2825} |
| } |
| ``` |
|
|
| ## Related Work |
|
|
| - Scientific Reports (Nature, 2025): https://www.nature.com/articles/s41598-025-28755-3 |
