通过大型语言模型实现地图上的自动标签放置
Automated Label Placement on Maps via Large Language Models
AI总结：
提出利用大型语言模型将地图标签放置视为数据编辑问题，通过检索增强生成整合标注指南，并在新基准MAPLE上验证了该方法能生成符合制图标准的准确标签坐标。
摘要：
标签放置是地图设计中的一个关键方面，作为一种空间标注形式，直接影响地图的清晰度和可解释性。尽管其重要性不言而喻，但标签放置在很大程度上仍然依赖手动操作且难以规模化，因为现有的自动化系统难以整合制图规范、适应上下文或解读标注指令。在这项工作中，我们提出了一种新的自动标签放置（ALP）范式，将该任务表述为一个数据编辑问题，并利用大型语言模型（LLMs）进行上下文感知的空间标注。为了支持这一方向，我们整理了MAPLE，这是第一个已知的用于在真实世界地图上评估ALP的基准数据集，涵盖了来自开源数据的多种地标类型和标签放置标注。我们的方法利用检索增强生成（RAG）检索与每种地标类型相关的标注指南，将其整合到提示中，并采用经过指令微调的LLMs生成理想的标签坐标。我们在MAPLE上评估了四个开源LLMs，分析了整体性能以及在不同地标类型上的泛化能力。这包括零样本和指令微调的性能。我们的结果表明，当LLMs在结构化提示和领域特定检索的引导下，能够学会执行准确的空间编辑，使生成的输出与专家制图标准保持一致。总体而言，我们的工作提出了一个可扩展的AI辅助地图完成框架，并展示了基础模型在结构化数据编辑任务中的潜力。代码和数据可在https://github.com/HarryShomer/MAPLE获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
