Update README.md

README.md

@@ -6,4 +6,63 @@ tags:
 - planet
 - multimodal
 - retrieval
----
+---
+
+# Global Geo-Localization
+
+## 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
+
+# Load the ground-truth catalogs for the 5 landforms
+gt_catalogs = load_dataset("SUSTech/marsretrieval-t3-geolocalization", "ground_truth")
+
+# Load image queries used for image-based localization
+queries = load_dataset("SUSTech/marsretrieval-t3-geolocalization", "image_queries")
+```
+
+For detailed instructions on the retrieval-centric protocol and official evaluation scripts, please refer to our [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},
+  year={2026}
+}
+```