Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.15949 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917217638023168 |
|---|---|
| author | Wang, Yiran Wang, Shuoyuan Wei, Zhaoran Zhao, Jiannan Yao, Zhonghua Xie, Zejian Zhang, Songxin Huang, Jun Jing, Bingyi Wei, Hongxin |
| author_facet | Wang, Yiran Wang, Shuoyuan Wei, Zhaoran Zhao, Jiannan Yao, Zhonghua Xie, Zejian Zhang, Songxin Huang, Jun Jing, Bingyi Wei, Hongxin |
| contents | Planetary surfaces are typically analyzed using high-level semantic concepts in natural language, yet vast orbital image archives remain organized at the pixel level. This mismatch limits scalable, open-ended exploration of planetary surfaces. Here we present MarScope, a planetary-scale vision-language framework enabling natural language-driven, label-free mapping of Martian landforms. MarScope aligns planetary images and text in a shared semantic space, trained on over 200,000 curated image-text pairs. This framework transforms global geomorphic mapping on Mars by replacing pre-defined classifications with flexible semantic retrieval, enabling arbitrary user queries across the entire planet in 5 seconds with F1 scores up to 0.978. Applications further show that it extends beyond morphological classification to facilitate process-oriented analysis and similarity-based geomorphological mapping at a planetary scale. MarScope establishes a new paradigm where natural language serves as a direct interface for scientific discovery over massive geospatial datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15949 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Natural Language-Driven Global Mapping of Martian Landforms Wang, Yiran Wang, Shuoyuan Wei, Zhaoran Zhao, Jiannan Yao, Zhonghua Xie, Zejian Zhang, Songxin Huang, Jun Jing, Bingyi Wei, Hongxin Artificial Intelligence Instrumentation and Methods for Astrophysics Planetary surfaces are typically analyzed using high-level semantic concepts in natural language, yet vast orbital image archives remain organized at the pixel level. This mismatch limits scalable, open-ended exploration of planetary surfaces. Here we present MarScope, a planetary-scale vision-language framework enabling natural language-driven, label-free mapping of Martian landforms. MarScope aligns planetary images and text in a shared semantic space, trained on over 200,000 curated image-text pairs. This framework transforms global geomorphic mapping on Mars by replacing pre-defined classifications with flexible semantic retrieval, enabling arbitrary user queries across the entire planet in 5 seconds with F1 scores up to 0.978. Applications further show that it extends beyond morphological classification to facilitate process-oriented analysis and similarity-based geomorphological mapping at a planetary scale. MarScope establishes a new paradigm where natural language serves as a direct interface for scientific discovery over massive geospatial datasets. |
| title | Natural Language-Driven Global Mapping of Martian Landforms |
| topic | Artificial Intelligence Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2601.15949 |