大语言模型（LLMs）正在重塑地理空间人工智能（GeoAI），在数据处理、空间分析和决策支持方面带来新的能力。本文探讨了开源范式在此变革中的关键作用。尽管专有大语言模型具有易用性，但通常限制了定制化、互操作性和透明度，而这些对于专业地理空间任务至关重要。相反，开源替代方案通过促进更高的适应性、可复现性以及社区驱动的创新，显著推动了地理信息科学（GIScience）的发展。开放框架使研究人员能够定制解决方案，集成前沿方法（如强化学习、高级空间索引），并遵循FAIR（可发现、可访问、可互操作、可重用）原则。然而，对任何大语言模型日益增长的依赖，要求我们审慎考虑安全漏洞、伦理风险以及对人工智能生成地理空间成果的健全治理。本文认为，地理信息科学的进步不应依赖单一模型类型，而应通过培育一个多样化、可互操作的生态系统来实现，该系统结合开源基础以促进创新、定制化的地理空间模型以及跨学科协作。通过对开源大语言模型在更广泛GeoAI格局中机遇与挑战的批判性评估，本文为有效利用大语言模型推动空间研究、政策制定与决策的公平、可持续且科学严谨的发展，贡献了一场深入的学术讨论。

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.