Natural Language-Driven Global Mapping of Martian Landforms

• URL: http://arxiv.org/abs/2601.15949v1
• Date: Thu, 22 Jan 2026 13:38:13 GMT
• Title: Natural Language-Driven Global Mapping of Martian Landforms
• Authors: Yiran Wang, Shuoyuan Wang, Zhaoran Wei, Jiannan Zhao, Zhonghua Yao, Zejian Xie, Songxin Zhang, Jun Huang, Bingyi Jing, Hongxin Wei,
• Abstract summary: MarScope is a vision-language framework enabling natural language-driven, label-free mapping of Martian landforms.<n>It aligns planetary images and text in a shared semantic space, trained on over 200,000 curated image-text pairs.<n>This framework transforms global geomorphic mapping on Mars by replacing pre-defined classifications with flexible semantic retrieval.
• Score: 25.54158424879149
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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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