---
license: cc-by-4.0
---
🌍 CVSBench: Cross-View Spatial Reasoning and Dreaming Benchmark
🔗 Links
| 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) |
---------
🧭 What Is In This Dataset
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
---------
✨ Benchmark Highlights
- 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.
---------
🧩 Tasks
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
---------
🗂️ Dataset Structure
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
--------
📝 Example Data Format
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.
---------
⬇️ Download
You can download the dataset with:
```bash
huggingface-cli download zlyzlyzly/CVSBench \
--repo-type dataset \
--local-dir data/CVSBench
```
---------
🚀 Usage
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/)
---------
📚 Recommended Citation
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.
---------
⚖️ License
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.
---------
🙏 Acknowledgements
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
---------
📮 Contact
For questions, issues, or collaboration requests:
- Email: [zlyzly@stu.xjtu.edu.cn](mailto:zlyzly@stu.xjtu.edu.cn)