MapLlama: A Two-Stage Approach for Map Question Answering Using a Fine-tuned Large Language Model

A map is a common medium to convey rich information. People present data on maps and answer spatial-related questions, usually requiring the ability to analyze the map and work on reasoning. In this work, we propose a two-stage map question-answering (MapQA) system. First, map images are converted into structured data based on a query-based model. A fine-tuned large language model (LLM) is employed with the structured text data to answer the given questions. We point out several issues in the current MapQA, propose a realistic scenario, and tackle it with a fine-tuned LLM. The experimental results show that the proposed method works well for diverse questions and performs better than the state-of-the-art techniques.