Understanding the Geospatial Reasoning Capabilities of LLMs: A Trajectory Recovery Perspective

ICLR 2026 Conference Submission16249 Authors

Keywords: geospatial, llm, navigation, road network

TL;DR: We evaluate geospatial understanding capabilities of LLM by how well they can read road network and perform navigation

Abstract: We explore the geospatial reasoning capabilities of Large Language Models (LLMs), specifically, whether LLMs can read road network maps and perform navigation. We frame trajectory recovery as a proxy task, which requires models to reconstruct masked GPS traces, and introduce GLOBALTRACE, a dataset with over 4,000 real-world trajectories across diverse regions and transportation modes. Using road network as context, our prompting framework enables LLMs to generate valid paths without accessing any external navigation tools. Experiments show that LLMs outperform off-the-shelf baselines and specialized trajectory recovery models, with strong zero-shot generalization. Fine-grained analysis shows that LLMs have strong comprehension of the road network and coordinate systems, but also pose systematic biases with respect to regions and transportation modes. Finally, we demonstrate how LLMs can enhance navigation experiences by reasoning over maps in flexible ways to incorporate user preferences.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 16249