卫星与航空影像分析已随着基础模型的出现进入新纪元。本文阐述了地理空间基础模型（GeoFM）的概念，即通过多种方法在海量地理空间数据集上进行预训练的人工智能/机器学习（AI/ML）模型。我们首先阐明GeoFM所推动的核心范式转变：职责分离——大规模模型提供商承担计算密集型的预训练任务，使领域专家能够快速对这些模型进行微调或提示工程，以完成特定的关键任务。该方法在保障下游任务安全性与保密性的同时，实现了前沿AI/ML技术的普惠化访问。随后，我们探讨不同类GeoFM所解锁的新能力，区分由掩码自编码等自监督技术生成的可微调视觉模型，与由对比学习生成的视觉-语言模型；后者支持零样本任务，例如开放词汇图像分析。接着，我们讨论GeoFM实际部署中的关键考量因素，涵盖性能-成本分析及更广泛的MLOps生态系统。为此，我们提出一种模型适配策略分类法，并构建一个框架，协助领域专家为其特定任务集选择最具成本效益的适配方法。最后，我们展望“智能体地理空间推理”（Agentic Geospatial Reasoning）的未来图景：大型语言模型（LLM）作为智能编排器，将GeoFM作为工具，以自然语言响应高层用户查询，并自动化复杂的分析工作流，推动该领域从感知迈向认知。

The analysis of satellite and aerial imagery has entered a new era with the advent of foundation models. This paper describes the concept of Geospatial Foundation Models (GeoFMs), which are artificial intelligence/machine learning (AI/ML) models pre-trained on massive geospatial datasets through varied methodologies. We first articulate the core paradigm shift that GeoFMs enable: a separation of duties, where large-scale model providers perform the computationally intensive pretraining, allowing domain experts to rapidly fine-tune or prompt these models for specific, mission-critical tasks. This approach democratizes access to state-of-the-art AI/ML while maintaining the security and confidentiality of the downstream task. We then explore the novel capabilities unlocked by different types of GeoFMs, distinguishing between the finetunable vision models produced by self-supervised techniques like masked auto-encoding, and the vision-language models produced by contrastive learning which enable zero-shot tasks like open-vocabulary image analysis. Next, we discuss the practical considerations for operationalizing GeoFMs, from performance-cost analysis to the broader MLOps ecosystem. To that end, we introduce a taxonomy of model adaptation strategies and propose a framework for domain experts to select the most cost-effective adaptation approach for their particular mission set. Finally, we present a forward-looking vision of Agentic Geospatial Reasoning, where Large Language Models act as intelligent orchestrators, leveraging GeoFMs as tools to answer high-level user queries in natural language and automate complex analytical workflows, moving the field from perception to cognition.