地理空间推理的基础模型：评估大型语言模型在理解几何与拓扑空间关系方面的能力
Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations
AI总结：
本研究评估GPT-3.5、GPT-4和DeepSeek-R1在理解几何与拓扑空间关系上的能力，发现基于嵌入和提示工程的方法平均准确率超0.6，其中GPT-4少样本提示达0.66以上，为地理基础模型发展提供见解。
摘要：
将AI基础模型直接应用于地理空间数据集仍然具有挑战性，因为它们表示和推理地理实体（特别是基于向量的几何体以及复杂空间关系的自然语言描述）的能力有限。为了解决这些问题，我们研究了当将地理空间向量数据传递给大型语言模型（LLM）时，几何体及其空间关系（例如拓扑谓词）的著名文本（WKT）表示在空间推理过程中被保留的程度，这些模型包括GPT-3.5-turbo、GPT-4和DeepSeek-R1-14B。我们的工作流程采用三种不同的方法来完成空间推理任务以进行比较，即基于几何嵌入的方法、基于提示工程的方法和基于日常语言的方法。我们的实验结果表明，在基于嵌入和基于提示工程的地理空间问答任务方法中，GPT模型在识别两个几何体之间的拓扑空间关系方面的平均准确率均超过0.6。在评估的模型中，采用少样本提示的GPT-4在拓扑空间关系推理中取得了最高性能，准确率超过0.66。此外，基于GPT的推理器能够正确理解逆拓扑空间关系，并且包含LLM生成的几何体可以增强地理实体检索的有效性。GPT-4还表现出将某些关于地点的方言描述转化为正式拓扑关系的能力，并且在提示中添加几何类型或地点类型上下文可以提高推理准确性，但这因实例而异。这些空间推理任务的表现为了解如何利用地理知识改进LLM、朝着能够进行地理空间推理的地理基础模型的发展提供了宝贵的见解。
英文摘要：
Applying AI foundation models directly to geospatial datasets remains challenging due to their limited ability to represent and reason with geographical entities, specifically vector-based geometries and natural language descriptions of complex spatial relations. To address these issues, we investigate the extent to which a well-known-text (WKT) representation of geometries and their spatial relations (e.g., topological predicates) are preserved during spatial reasoning when the geospatial vector data are passed to large language models (LLMs) including GPT-3.5-turbo, GPT-4, and DeepSeek-R1-14B. Our workflow employs three distinct approaches to complete the spatial reasoning tasks for comparison, i.e., geometry embedding-based, prompt engineering-based, and everyday language-based evaluation. Our experiment results demonstrate that both the embedding-based and prompt engineering-based approaches to geospatial question-answering tasks with GPT models can achieve an accuracy of over 0.6 on average for the identification of topological spatial relations between two geometries. Among the evaluated models, GPT-4 with few-shot prompting achieved the highest performance with over 0.66 accuracy on topological spatial relation inference. Additionally, GPT-based reasoner is capable of properly comprehending inverse topological spatial relations and including an LLM-generated geometry can enhance the effectiveness for geographic entity retrieval. GPT-4 also exhibits the ability to translate certain vernacular descriptions about places into formal topological relations, and adding the geometry-type or place-type context in prompts may improve inference accuracy, but it varies by instance. The performance of these spatial reasoning tasks offers valuable insights for the refinement of LLMs with geographical knowledge towards the development of geo-foundation models capable of geospatial reasoning.
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