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.

翻译：行星表面通常通过自然语言中的高层语义概念进行分析，然而海量的轨道图像档案仍以像素级别进行组织。这种不匹配限制了行星表面可扩展的开放式探索。本文提出MarScope，一个行星尺度的视觉-语言框架，能够实现自然语言驱动的、无标签的火星地貌制图。MarScope将行星图像与文本对齐到共享语义空间中，该模型基于超过20万个精选的图像-文本对进行训练。该框架通过以灵活的语义检索取代预定义分类，彻底改变了火星全球地貌制图方式，可在5秒内响应针对整个星球的任意用户查询，F1分数高达0.978。应用案例进一步表明，其能力不仅限于形态分类，还能促进行星尺度的过程导向分析和基于相似性的地貌制图。MarScope确立了一种新范式，使自然语言成为海量地理空间数据科学发现的直接交互接口。