Add task categories and link to paper/GitHub (#1)

- Add task categories and link to paper/GitHub (8d97b927c013b0b6560277b0061d592e4f5f9551)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,15 +1,21 @@
 ---
-license: cc
 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.
@@ -38,8 +44,6 @@ Given the extreme sparsity of positives, we report:
 
 These metrics quantify planetary-scale distribution estimation rather than simple top-K accuracy.
 
-
-
 ## How to Use
 ```python
 from datasets import load_dataset
@@ -48,7 +52,7 @@ ds = load_dataset("SUSTech/Mars-Global-Geolocalization")
 print(ds)
 ```
 
-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).
+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
@@ -62,4 +66,4 @@ If you find this useful in your research, please consider citing:
   journal={arXiv preprint arXiv:2602.13961},
   year={2026}
 }
-```
+```