Update README.md

README.md

@@ -66,4 +66,66 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc
+language:
+- en
+tags:
+- planet
+- multimodal
+- retrieval
+size_categories:
+- 1K<n<10K
 ---
+
+# Landform Retrieval
+
+## Dataset Summary
+
+This dataset is Task 2 of [**MarsRetrieval**](https://github.com/ml-stat-Sustech/MarsRetrieval), a retrieval-centric benchmark for evaluating vision-language models (VLMs) on Mars geospatial discovery. Task 2 evaluates **concept-to-instance generalization** for Martian geomorphology. Given a textual geomorphic concept, the model must retrieve its corresponding visual instances from a curated Martian image gallery. The dataset comprises **1,185** carefully curated image patches collected from CTX and HiRISE imagery. The landforms follow a two-level geomorphology taxonomy:
+
+- **7 major genetic classes** (e.g., Aeolian, Volcanic and Fluvial processes)
+- **48 geomorphic subclasses** (e.g., Aeolian Dunes, Dust Devil Tracks, Yardangs)
+
+## Task Formulation
+
+We formulate this task as a **text-to-image multi-positive retrieval problem**:
+
+- A text query describes a geomorphic subclass.
+- Multiple image instances in the gallery are considered valid positives.
+- The goal is to rank all gallery images by cosine similarity in the embedding space.
+
+
+### Metrics
+
+We report metrics suitable for long-tailed multi-positive retrieval:
+
+- Macro mean Average Precision (mAP)
+- nDCG@10
+- Hits@10
+
+
+## How to Use
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("SUSTech/marsretrieval-t2-landforms")
+
+# Access a sample image and its geomorphic label
+print(dataset["train"][0]["image"])
+print(dataset["train"][0]["label"])
+```
+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}
+}
+```