---
language:
- en
license: cc
task_categories:
- text-to-image
- image-to-image
tags:
- planet
- multimodal
- retrieval
- mars
- geospatial
---

# Global Geo-Localization

[**Paper**](https://huggingface.co/papers/2602.13961) | [**GitHub**](https://github.com/ml-stat-Sustech/MarsRetrieval)

## Dataset Summary
This dataset is Task 3 of [**MarsRetrieval**](https://github.com/ml-stat-Sustech/MarsRetrieval), a retrieval-centric benchmark for evaluating vision-language models (VLMs) on Mars geospatial discovery. 
Task 3 simulates **planetary-scale discovery** by localizing scientific concepts within the global CTX mosaic, which comprises over **1.4 million** CTX tiles.

Ground-truth reference points are compiled from published global scientific catalogues of five Martian landforms:

- Alluvial Fans
- Glacier-Like Forms
- Landslides
- Pitted Cones
- Yardangs

## Task Formulation

- **Text → Image** retrieval
- **Image → Image** retrieval

The model retrieves top-K candidate tiles from the global index. A retrieved tile is considered correct if its projected center falls within a spatial tolerance radius (default r = 0.5°) of a ground-truth catalogue coordinate.

## Metrics

Given the extreme sparsity of positives, we report:

- AUPRC (Area Under Precision–Recall Curve)
- Optimal F1@K\* (best F1 over retrieval depth K)

These metrics quantify planetary-scale distribution estimation rather than simple top-K accuracy.

## How to Use
```python
from datasets import load_dataset

ds = load_dataset("SUSTech/Mars-Global-Geolocalization")
print(ds)
```

For detailed instructions on the retrieval-centric protocol and official evaluation scripts, please refer to the [Official Dataset Documentation](https://github.com/ml-stat-Sustech/MarsRetrieval/blob/main/docs/DATASET.md).


## Citation

If you find this useful in your research, please consider citing:

```bibtex
@article{wang2026marsretrieval,
  title={MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars},
  author={Wang, Shuoyuan and Wang, Yiran and Wei, Hongxin},
  journal={arXiv preprint arXiv:2602.13961},
  year={2026}
}
```