Automated Label Placement on Maps via Large Language Models

TL;DR

This paper introduces a novel approach for automated map label placement using large language models, leveraging data editing and retrieval-augmented generation to improve scalability and adherence to cartographic standards.

Contribution

It formulates label placement as a data editing task with LLMs, introduces the MAPLE dataset, and demonstrates effective, context-aware label placement through retrieval and instruction tuning.

Findings

LLMs can generate accurate label coordinates aligned with cartographic standards.

Retrieval-augmented prompts improve LLM performance in label placement.

The framework is scalable and adaptable to diverse map types.

Abstract

Label placement is a critical aspect of map design, serving as a form of spatial annotation that directly impacts clarity and interpretability. Despite its importance, label placement remains largely manual and difficult to scale, as existing automated systems struggle to integrate cartographic conventions, adapt to context, or interpret labeling instructions. In this work, we introduce a new paradigm for automatic label placement (ALP) that formulates the task as a data editing problem and leverages large language models (LLMs) for context-aware spatial annotation. To support this direction, we curate MAPLE, the first known benchmarking dataset for evaluating ALP on real-world maps, encompassing diverse landmark types and label placement annotations from open-source data. Our method retrieves labeling guidelines relevant to each landmark type leveraging retrieval-augmented generation (RAG), integrates them into prompts, and employs instruction-tuned LLMs to generate ideal label coordinates. We evaluate four open-source LLMs on MAPLE, analyzing both overall performance and generalization across different types of landmarks. This includes both zero-shot and instruction-tuned performance. Our results demonstrate that LLMs, when guided by structured prompts and domain-specific retrieval, can learn to perform accurate spatial edits, aligning the generated outputs with expert cartographic standards. Overall, our work presents a scalable framework for AI-assisted map finishing and demonstrates the potential of foundation models in structured data editing tasks. The code and data can be found at https://github.com/HarryShomer/MAPLE.