地理空间基础模型的新兴范式：从预训练到智能推理
The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning
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中文总结 AI 辅助
研究地理空间基础模型的新兴范式，通过职责分离实现预训练与特定任务微调，探讨不同类型模型能力及实现考虑因素，提出模型适应策略分类法和框架，展望智能地理空间推理推动领域从感知到认知的发展。
中文摘要 AI 辅助
随着基础模型的出现，卫星和航空图像分析进入了一个新时代。本文描述了地理空间基础模型（GeoFMs）的概念，它是通过各种方法在大量地理空间数据集上预训练的人工智能/机器学习（AI/ML）模型。首先阐述了GeoFMs带来的核心范式转变，即职责分离，大规模模型提供商进行计算密集型预训练，领域专家可快速微调或提示这些模型用于特定关键任务，这种方法在保持下游任务安全保密的同时，使人们能使用先进的AI/ML。接着探讨了不同类型GeoFMs解锁的新能力，区分了自监督技术产生的可微调视觉模型和对比学习产生的视觉语言模型。然后讨论了实现GeoFMs的实际考虑因素，介绍了模型适应策略分类法并提出框架。最后展望了智能地理空间推理的前景，大型语言模型作为智能协调器，利用GeoFMs作为工具以自然语言回答高级用户查询并自动化复杂分析工作流程，推动该领域从感知走向认知。
英文摘要
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.
发表机构
- Google Public Sector（谷歌公共部门）
机构由 AI 辅助整理，请以论文原文为准。
