开源大模型推动地理智能发展，提升可定制性与透明度。
The Role of Open-Source LLMs in Shaping the Future of GeoAI
- 用开源框架定制地理分析工具，支持强化学习与空间索引技术。
- 促进研究可复现性，符合数据共享的FAIR原则。
- 适合科研人员、政策制定者及需要透明地理决策的团队。
大型语言模型（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能力。本文探讨开源范式在这一变革中的关键作用。尽管专有模型易于获取，但常限制定制化、互操作性和透明度，不利于专业地理任务。相反，开源模型通过增强适应性、可复现性与社区创新，显著推动地理信息科学（GIScience）发展。开放框架使研究者能定制解决方案，集成前沿方法（如强化学习、先进空间索引），并遵循FAIR原则。然而，对任何模型的依赖都需警惕安全漏洞、伦理风险及生成结果的治理问题。本文主张，地理信息科学的进步不依赖单一模型类型，而应构建融合开源基础、定制地理模型与跨学科协作的多样化、可互操作生态系统。通过批判性评估开源大模型在整体GeoAI中的机遇与挑战，本研究为以公平、可持续且科学严谨的方式推进空间研究、政策与决策提供重要讨论基础。
原文摘要 · Abstract (English)
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.
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