用自然语言在火星上快速搜索地貌，5秒响应，准确率97.8%。
Natural Language-Driven Global Mapping of Martian Landforms
- 将图像与文本对齐到同一语义空间，实现无标签的自然语言检索。
- 支持全球范围查询，5秒内完成，最高F1得分0.978。
- 适合地质学家和科研人员探索海量火星影像数据。
行星表面通常通过自然语言中的高级语义概念进行分析，但庞大的轨道图像档案仍以像素级别组织，这种不匹配限制了对行星表面的大规模、开放式探索。本文提出马尔斯视角（MarScope），一个行星尺度的视觉-语言框架，实现基于自然语言的无标签火星地貌全局映射。该框架在超过20万组精心筛选的图文配对上训练，将行星图像与文本对齐至共享语义空间。它通过替换预定义分类体系，实现灵活的语义检索，使用户可在5秒内对整颗火星进行任意查询，最高F1分数达0.978。应用表明，该框架不仅支持形态分类，还能推进面向过程的分析及基于相似性的全球地貌映射。MarScope建立了一种新范式：自然语言成为探索大规模地理空间数据集的直接科学发现接口。
原文摘要 · Abstract (English)
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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