大语言模型（LLMs）正在变革地理空间人工智能（GeoAI），为数据处理、空间分析与决策支持提供新能力。本文考察开源范式在此变革中的关键作用。尽管专有LLMs具备易用性，但其往往限制了针对专业地理空间任务所必需的定制化、互操作性与透明度。相比之下，开源替代方案通过促进更强的适应性、可复现性及社区驱动的创新，显著推动了地理信息科学（GIScience）的发展。开源框架使研究人员能够定制解决方案、集成前沿方法（例如强化学习、高级空间索引），并契合FAIR原则（可发现、可访问、可互操作、可重用）。然而，对任何LLM日益增长的依赖均要求审慎考量其安全漏洞、伦理风险，以及针对AI生成地理空间输出的稳健治理机制。本文主张，GIScience的最佳发展路径并非依赖单一模型类型，而在于构建一个多元、互操作的生态系统——该系统以开源基础支撑创新，融合定制化地理空间模型，并促进跨学科协作。通过对开源LLMs在更广泛GeoAI格局中的机遇与挑战进行批判性评估，本研究为如何以公平、可持续且科学严谨的方式利用LLMs推进空间研究、政策制定与决策支持，提供了全面的学术讨论。

Large Language Models (LLMs) are transforming geospatial artificial intelligence (GeoAI), offering new capabilities in data processing, spatial analysis and decision support. This paper examines the open-source paradigm’s critical role in this transformation. While proprietary LLMs offer accessibility, they often limit the customization, interoperability and transparency vital for specialized geospatial tasks. Conversely, open-source alternatives significantly advance Geographic Information Science (GIScience) by fostering greater adaptability, reproducibility and community-driven innovation. Open frameworks empower researchers to tailor solutions, integrate cutting-edge methodologies (e.g. reinforcement learning, advanced spatial indexing) and align with FAIR (Findable, Accessible, Interoperable and Reusable) principles. However, the growing reliance on any LLM necessitates careful consideration of security vulnerabilities, ethical risks and robust governance for AI-generated geospatial outputs. This paper argues that GIScience advances best not through a single model type, but by cultivating a diverse, interoperable ecosystem combining open-source foundations for innovation, custom geospatial models and interdisciplinary collaboration. By critically evaluating the opportunities and challenges of open-source LLMs within the broader GeoAI landscape, this work contributes to a thorough discourse on leveraging LLMs to effectively advance spatial research, policy and decision-making in an equitable, sustainable and scientifically rigorous manner.