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arXiv · 2601.15949

Natural Language-Driven Global Mapping of Martian Landforms

Yiran Wang·Shuoyuan Wang·Zhaoran Wei·Jiannan Zhao·Zhonghua Yao·Zejian Xie·Songxin Zhang·Jun Huang·Bingyi Jing·Hongxin Wei
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Abstract

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.

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Yiran Wang, Shuoyuan Wang, Zhaoran Wei, Jiannan Zhao, Zhonghua Yao, Zejian Xie, Songxin Zhang, Jun Huang, Bingyi Jing, Hongxin Wei. 2026-01-22. Natural Language-Driven Global Mapping of Martian Landforms. https://arxiv.org/abs/2601.15949

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