| | --- |
| | language: |
| | - en |
| | license: cc |
| | task_categories: |
| | - image-text-to-text |
| | 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} |
| | } |
| | ``` |
