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--- |
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language: |
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- en |
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license: cc |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- text-to-image |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': Aeolian_Bedforms |
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'1': Aeolian_Dunes |
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'2': Aeolian_Ripples |
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'3': Barchan_Dunes |
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'4': Boulder_Track |
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'5': Brain_Terrain |
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'6': Bright_Rays_Craters |
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'7': Central_Peak_Crater |
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'8': Chaos |
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'9': Cliff |
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'10': Concentric_Crater_Fill |
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'11': Crater_Chain |
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'12': Crater_Cluster |
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'13': Dark_Ray_Craters |
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'14': Double_Ring_Basin |
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'15': Doublet_Crater |
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'16': Dune_Field |
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'17': Dust_Devil_Tracks |
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'18': Fan_Shape_Deposit |
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'19': Fractured_Mounds |
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'20': Fresh_Crater |
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'21': Gully |
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'22': Landslide |
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'23': Lava_Flow_Front |
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'24': Lava_Tubes |
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'25': Layers |
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'26': Linear_Dunes |
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'27': Lobate_Debris_Apron |
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'28': Outflow_Channel |
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'29': Pancake_Crater |
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'30': Pedestal_Crater |
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'31': Pitted_Cone |
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'32': Pitted_Terrain |
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'33': Polar_Layered_Deposits |
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'34': Polygons |
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'35': Rampart_Crater |
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'36': Rocky_Ejecta_Crater |
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'37': Scalloped_Depression |
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'38': Slope_Streaks |
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'39': Spider |
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'40': Swiss_Cheese |
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'41': Transverse_Aeolian_Ridges |
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'42': Troughs |
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'43': Valley_Networks |
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'44': Volcano |
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'45': Wind_Streaks |
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'46': Wrinkle_Ridges |
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'47': Yardangs |
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splits: |
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- name: train |
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num_bytes: 763505091 |
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num_examples: 1185 |
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download_size: 758103040 |
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dataset_size: 763505091 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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tags: |
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- planet |
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- multimodal |
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- retrieval |
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--- |
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# Landform Retrieval |
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[**Paper**](https://huggingface.co/papers/2602.13961) | [**Code**](https://github.com/ml-stat-Sustech/MarsRetrieval) |
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## Dataset Summary |
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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: |
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- **7 major genetic classes** (e.g., Aeolian, Volcanic and Fluvial processes) |
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- **48 geomorphic subclasses** (e.g., Aeolian Dunes, Dust Devil Tracks, Yardangs) |
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## Task Formulation |
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We formulate this task as a **text-to-image multi-positive retrieval problem**: |
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- A text query describes a geomorphic subclass. |
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- Multiple image instances in the gallery are considered valid positives. |
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- The goal is to rank all gallery images by cosine similarity in the embedding space. |
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### Metrics |
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We report metrics suitable for long-tailed multi-positive retrieval: |
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- Macro mean Average Precision (mAP) |
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- nDCG@10 |
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- Hits@10 |
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## How to Use |
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```python |
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from datasets import load_dataset |
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# Load the dataset |
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dataset = load_dataset("SUSTech/Mars-Landforms") |
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# Access a sample image and its geomorphic label |
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print(dataset["train"][0]["image"]) |
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print(dataset["train"][0]["label"]) |
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``` |
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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). |
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## Citation |
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If you find this useful in your research, please consider citing: |
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```bibtex |
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@article{wang2026marsretrieval, |
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title={MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars}, |
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author={Wang, Shuoyuan and Wang, Yiran and Wei, Hongxin}, |
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journal={arXiv preprint arXiv:2602.13961}, |
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year={2026} |
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} |
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``` |
