[论文解读] Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality

Danfeng Hong, Chenyu Li+3 more|arXiv (Cornell University)|Mar 1, 2026

Geographic Information Systems Studies被引用 0

一句话总结

本论文提供了基础模型在遥感领域的全面综述，追踪其从单模态到多模态方法的演变，并为实际应用提供教程性指引。

ABSTRACT

Remote sensing (RS) techniques are increasingly crucial for deepening our understanding of the planet. As the volume and diversity of RS data continue to grow exponentially, there is an urgent need for advanced data modeling and understanding capabilities to manage and interpret these vast datasets effectively. Foundation models present significant new growth opportunities and immense potential to revolutionize the RS field. In this paper, we conduct a comprehensive technical survey on foundation models in RS, offering a brand-new perspective by exploring their evolution from unimodality to multimodality. We hope this work serves as a valuable entry point for researchers interested in both foundation models and RS and helps them launch new projects or explore new research topics in this rapidly evolving area. This survey addresses the following three key questions: What are foundation models in RS? Why are foundation models needed in RS? How can we effectively guide junior researchers in gaining a comprehensive and practical understanding of foundation models in RS applications? More specifically, we begin by outlining the background and motivation, emphasizing the importance of foundation models in RS. We then review existing foundation models in RS, systematically categorizing them into unimodal and multimodal approaches. Additionally, we provide a tutorial-like section to guide researchers, especially beginners, on how to train foundation models in RS and apply them to real-world tasks. The survey aims to equip researchers in RS with a deeper and more efficient understanding of foundation models, enabling them to get started easily and effectively apply these models across various RS applications.

研究动机与目标

• 解释遥感中的基础模型是什么，以及它们为何对 EO 数据重要。
• 系统性地将 RS 基础模型分为单模态和多模态两组进行分类。
• 强调 RS 基础模型的挑战、机会与待解决的问题。
• 提供一个面向研究者的培训与在实际任务中应用预训练 RS 基础模型的教程式指南。

提出的方法

• 评述并将现有的 RS 基础模型分为单模态和多模态。
• 通过论文、会议和地理分布的统计概览分析趋势。
• 描述 RS 基础模型的自监督预训练加微调范式。
• 总结 RS 基础模型中使用的代表性预训练数据集与模态。
• 为研究者在 RS 任务中训练和应用基础模型提供实用教程。

实验结果

研究问题

• RQ1 遥感中的基础模型是什么，为什么需要它们？
• RQ2 RS 基础模型如何从单模态演变到多模态，驱动因素有哪些？
• RQ3 哪些实际指导可以帮助研究者在真实任务中训练并部署 RS 基础模型？
• RQ4 阻碍应用的主要挑战有哪些，如何解决？

主要发现

• RS 的基础模型正从单模态向多模态转变，越来越强调整合多样数据源。
• 大量 RS 基础模型研究通过 arXiv 传播，IEEE TGRS 与 CVPR 为突出的期刊/会议。
• 中国、美国和澳大利亚在 RS 基础模型研究方面处于领先地位。
• 自监督预训练加微调是实现向下游 RS 任务迁移的主导学习范式。
• 存在日益增长的基准、模型仓库与标准化评估数据集生态系统，以支持 RS 基础模型。