由大语言模型（LLM）驱动的地理空间人工智能（GeoAI）正通过自然语言接口及具身式自主地理信息系统（GIS）工作流，拓展对空间信息的查询、生成与解释能力。这一能力引发了一系列治理挑战，而通用人工智能伦理讨论尚无法充分涵盖这些挑战，包括从移动轨迹中被动推断位置、受空间自相关性与尺度效应驱动的空间结构化偏见放大、空间事实幻觉，以及多模态地理空间输入中不确定性逐级累积等问题。本叙事综述识别出LLM赋能GeoAI中八类反复出现的问题：数据来源与知情同意、空间隐私与推断风险、算法偏见与空间不公、作为空间结构性风险的空间机制（空间自相关性、可修改区域单元问题及尺度效应）、LLM特有的技术风险、可解释性、政策与监管缺口、公众赋能与人才队伍建设。针对每一问题，我们阐释其底层机制，援引文献中的典型实例予以佐证，并评估当前技术或制度响应的进展状态，范围涵盖基本未被触及、正处于积极辩论之中，或已纳入新兴政策议程。基于该综合分析，我们提出一种面向治理感知的LLM赋能自主GIS架构，将上述每类问题映射至地理空间数据全生命周期中可执行的管控措施与可审计的产出物，并以一个洪水响应路径规划的实际案例予以说明。综述指出一项持续存在的证据缺口：现有应对方案大多仍停留于概念层面，针对LLM赋能GeoAI的治理管控措施尚缺乏经实地验证的评估。最后，我们提出一项强调实证验证的研究议程。

Geospatial artificial intelligence (GeoAI) powered by large language models (LLMs) is expanding the capacity to query, generate, and interpret spatial information through natural-language interfaces and agentic autonomous GIS workflows. This capability creates governance challenges that general AI ethics discussions do not fully capture, including passive location inference from mobility traces, spatially structured bias amplification driven by spatial autocorrelation and scale effects, hallucinated spatial facts, and uncertainty compounding across multimodal geospatial inputs. This narrative review identifies eight recurring issues in LLM-enabled GeoAI: data provenance and consent, spatial privacy and inference risk, algorithmic bias and spatial inequity, spatial mechanisms as structural risk (spatial autocorrelation, the modifiable areal unit problem, and scale effects), LLM-specific technical risks, explainability, policy and regulatory gaps, and public enablement and workforce development. For each issue, we characterize the underlying mechanism, ground it in an illustrative example from the literature, and assess the current state of technical or institutional responses, ranging from largely unaddressed to actively debated or subject to emerging policy. Building on this synthesis, we propose a governance-aware architecture for LLM-enabled autonomous GIS that maps each issue to enforceable controls and auditable artifacts across the geospatial data lifecycle, illustrated through a worked flood-response routing scenario. The review highlights a persistent evidence gap: proposed responses remain largely conceptual, and field-tested evaluations of governance controls for LLM-enabled GeoAI remain limited. We close by outlining a research agenda emphasizing empirical validation, spatially specific interpretability tools, and workforce training aligned with these emerging risks.