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.
Cite
@article{arxiv.2601.15949,
title = {Natural Language-Driven Global Mapping of Martian Landforms},
author = {Yiran Wang and Shuoyuan Wang and Zhaoran Wei and Jiannan Zhao and Zhonghua Yao and Zejian Xie and Songxin Zhang and Jun Huang and Bingyi Jing and Hongxin Wei},
journal= {arXiv preprint arXiv:2601.15949},
year = {2026}
}