Automated Label Placement on Maps via Large Language Models

2025-07-29

AI Summary

Map labeling has traditionally relied on manual labor, while existing automated approaches struggle to simultaneously satisfy cartographic standards, contextual adaptability, and interpretability. This paper introduces the first large language model (LLM)-based paradigm for automatic map labeling, framing label placement as a spatial data editing task. Our method integrates retrieval-augmented generation (RAG), cartographic-standard-aware structured prompting, and instruction fine-tuning. We construct MAPLE—the first real-world benchmark dataset for map labeling—and design a prompt framework supporting multi-source geospatial data fusion and contextual understanding. Experiments demonstrate that multiple open-source LLMs, under both zero-shot and fine-tuned settings, generate label coordinates compliant with professional cartographic standards. Our approach significantly improves accuracy, generalizability, and interpretability of automated map labeling compared to prior methods.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Automating label placement on maps for better clarity and interpretability
• Overcoming manual label placement challenges with context-aware LLMs
• Evaluating LLMs for accurate spatial edits using cartographic standards

Innovation

• Leverages LLMs for context-aware label placement
• Uses retrieval-augmented generation for guideline integration
• Introduces MAPLE dataset for benchmarking ALP