README.md

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license: cc-by-4.0
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<h1>🌍 CVSBench: Cross-View Spatial Reasoning and Dreaming Benchmark</h1>




<h3>
  <a href="http://arxiv.org/abs/2606.22476">📄 Paper</a> |
  <a href="https://earth-insights.github.io/CVSBench/">🌐 Project Page</a> |
  <a href="https://huggingface.co/datasets/zlyzlyzly/CVSBench">🤗 Dataset</a> |
  <a href="https://github.com/earth-insights/CVSBench">💻 GitHub</a>
</h3>

</div>

<h1>🔗 Links</h1>

| Resource | Link |
| --- | --- |
| 📑 Paper | [Arxiv](http://arxiv.org/abs/2606.22476) |
| 🌐 Project Page | [earth-insights/CVSBench](https://earth-insights.github.io/CVSBench/) |
| 💻 GitHub | [earth-insights/CVSBench](https://github.com/earth-insights/CVSBench) |
| 🤗 Dataset (Hugging Face) | [zlyzlyzly/CVSBench](https://huggingface.co/datasets/zlyzlyzly/CVSBench) |

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<h1>🧭 What Is In This Dataset</h1>

CVSBench is a benchmark for evaluating whether vision-language models can reason across views, align satellite and street-view observations, localize corresponding objects, and imagine unseen visual content from partial evidence.

This repository contains the released dataset splits and task files. It focuses on cross-view understanding between:

- satellite imagery
- street-view imagery

The benchmark is designed to test abilities such as:

- cross-view correspondence
- spatial reasoning
- grounding and localization
- viewpoint understanding
- visual imagination

Unlike traditional cross-view benchmarks that mainly focus on retrieval or recognition, CVSBench explicitly emphasizes:

- cross-view spatial reasoning
- cross-view grounding
- view understanding and matching
- visual imagination from partial observations

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<h1>✨ Benchmark Highlights</h1>

- Covers both satellite-to-ground and ground-to-satellite reasoning.
- Includes QA-style, grounding-style, and view-matching tasks.
- Contains multiple benchmark subsets rather than a single uniform task setting.
- Extends beyond recognition and matching to evaluate spatial reasoning and visual imagination.

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<h1>🧩 Tasks</h1>

CVSBench currently contains two major subsets:

- `cvusa/`
- `fov/`

These subsets are complementary rather than identical, and they contain different task families.

### `cvusa/`

- `g2s`: Ground-to-Satellite reasoning tasks
- `s2g`: Satellite-to-Ground reasoning tasks
- `gs_grounding`: cross-view object grounding and bounding-box localization tasks

### `fov/`

- `data`: raw image and supporting resources
- `g2s`: Ground-to-Satellite reasoning tasks
- `s2g`: Satellite-to-Ground reasoning tasks
- `gs_grounding`: cross-view object grounding and bounding-box localization tasks
- `gs_view`: cross-view view-matching tasks with two settings:
  - `View-Arrow`: given a directional arrow or viewing direction, find the corresponding street-view image
  - `View-Image`: given a street-view image, find the corresponding directional arrow or viewing direction
- `nanobanana`: generated 3D miniature building-model images used as auxiliary visual inputs for FOV-based visual imagination experiments

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<h1>🗂️ Dataset Structure</h1>

The released dataset is organized as follows:

```text
CVSBench/
├── cvusa/
│   ├── data/
│   ├── g2s/
│   ├── s2g/
│   └── gs_grounding/
└── fov/
    ├── data/
    ├── g2s/
    ├── s2g/
    ├── gs_grounding/
    ├── gs_view/
    └── nanobanana/
```

Typical files include:

- train / test JSONL annotations
- task-specific metadata
- image path references
- grounding annotations for bbox evaluation

--------

<h1>📝 Example Data Format</h1>

Below is a simplified example from a grounding-style task:

```json
{
  "img_id": "0001119_0",
  "task": "Ground2Sat",
  "source_image": "cvusa/data/streetview/0001119.jpg",
  "target_image": "cvusa/data/bingmap/input0001119.png",
  "target_bbox": [121.0, 196.6, 153.0, 234.6],
  "questions": [
    {
      "level": 3,
      "question": "First image shows a street-view with a bounding box. In the second satellite image, provide the pixel bounding box coordinates [x_min, y_min, x_max, y_max] for the corresponding object.",
      "answer": [121.0, 196.6, 153.0, 234.6]
    }
  ],
  "dataset": "cvusa"
}
```

Exact fields may vary across task families.

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<h1>⬇️ Download</h1>

You can download the dataset with:

```bash
huggingface-cli download zlyzlyzly/CVSBench \
    --repo-type dataset \
    --local-dir data/CVSBench
```

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<h1>🚀 Usage</h1>

CVSBench can be used for:

- benchmarking vision-language models on cross-view reasoning
- evaluating object grounding across satellite and street-view images
- studying viewpoint alignment and cross-view matching
- studying visual imagination from sparse or partial observations

Official code and evaluation scripts are available in the GitHub repository:

- GitHub: [earth-insights/CVSBench](https://github.com/earth-insights/CVSBench)
- Dataset: [zlyzlyzly/CVSBench](https://huggingface.co/datasets/zlyzlyzly/CVSBench)
- Project Page: [earth-insights/CVSBench](https://earth-insights.github.io/CVSBench/)

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<h1>📚 Recommended Citation</h1>

If you use CVSBench in your research, please cite:

```bibtex
@article{cvsbench2026,
  title={CVSBench: A Comprehensive Benchmark for Cross-View Spatial Reasoning and Dreaming},
  author={[TBD]},
  journal={ECCV},
  year={2026}
}
```

The official citation will be updated after the paper metadata is finalized.

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<h1>⚖️ License</h1>

License: `CC-BY-4.0`

If parts of the dataset inherit licensing or usage constraints from underlying sources, please also follow the original source terms.

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<h1>🙏 Acknowledgements</h1>

CVSBench is built on top of existing cross-view data resources. We sincerely thank the creators and maintainers of the following datasets and projects:

- [CVUSA](https://mvrl.cse.wustl.edu/datasets/cvusa/)
- [University-1652](https://github.com/layumi/University1652-Baseline)
- FOV and cross-view benchmark contributors
- open-source vision-language model and benchmark tooling communities

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<h1>📮 Contact</h1>

For questions, issues, or collaboration requests:


- Email: [zlyzly@stu.xjtu.edu.cn](mailto:zlyzly@stu.xjtu.edu.cn)