Evaluating large language models on geospatial tasks: a multiple geospatial task benchmarking study

The emergence of large language models like ChatGPT and Gemini has highlighted the need to assess their diverse capabilities. However, their performance on geospatial tasks remains underexplored. This study introduces a novel multi-task spatial evaluation dataset to address this gap, covering twelve task types, including spatial understanding and route planning, with verified answers. We evaluated several models, including OpenAI's gpt-3.5-turbo, gpt-4-turbo, gpt-4o, ZhipuAI's glm-4, Anthropic's claude-3-sonnet-20240229, and MoonShot's moonshot-v1-8k, using a two-phase testing approach: zero-shot testing followed by difficulty-based categorization and prompt tuning. Results show that gpt-4o had the highest overall accuracy in the first phase at 71.3%. Though moonshot-v1-8k performed slightly worse overall, it outperformed gpt-4o in place name recognition tasks. The study also highlights the impact of prompt strategies on performance, such as the Chain-of-Thought strategy, which boosted gpt-4o's accuracy in route planning from 12.4% to 87.5%, and a one-shot strategy that raised moonshot-v1-8k's accuracy in mapping tasks from 10.1% to 76.3%.