用大模型和注意力机制自动标注历史地图，无需精细人工标签。
Leveraging LLMs and attention-mechanism for automatic annotation of historical maps
- 用大模型生成低分辨率图块粗分类，再用注意力机制提升到高分辨率。
- 对树木和聚落的召回率超90%，交并比达84.2%和72.0%。
- 不依赖细粒度标注，适合大规模历史地图自动化分析。
历史地图是理解过去地理景观的重要资源，广泛应用于历史、地理与城市研究，有助于重建历史环境与分析空间变迁。然而，纸质或扫描版地图仅能由人工解读，难以扩展。近年来，计算机视觉与大语言模型（LLMs）的发展为自动识别与分类历史地图中的特征提供了新路径。本文提出一种新型知识蒸馏方法，利用LLMs生成低分辨率图像块的粗略分类标签，并通过注意力机制将其细化至高分辨率。实验表明，优化后的标签召回率超过90%。对树木和聚落的交并比（IoU）分别为84.2%和72.0%，精确率分别为87.1%和79.5%，且标签与真实标注高度一致。值得注意的是，该方法在训练中未使用细粒度人工标注，凸显其在高效、可扩展的历史地图分析中的潜力。
原文摘要 · Abstract (English)
Historical maps are essential resources that provide insights into the geographical landscapes of the past. They serve as valuable tools for researchers across disciplines such as history, geography, and urban studies, facilitating the reconstruction of historical environments and the analysis of spatial transformations over time. However, when constrained to analogue or scanned formats, their interpretation is limited to humans and therefore not scalable. Recent advancements in machine learning, particularly in computer vision and large language models (LLMs), have opened new avenues for automating the recognition and classification of features and objects in historical maps. In this paper, we propose a novel distillation method that leverages LLMs and attention mechanisms for the automatic annotation of historical maps. LLMs are employed to generate coarse classification labels for low-resolution historical image patches, while attention mechanisms are utilized to refine these labels to higher resolutions. Experimental results demonstrate that the refined labels achieve a high recall of more than 90%. Additionally, the intersection over union (IoU) scores--84.2% for Wood and 72.0% for Settlement--along with precision scores of 87.1% and 79.5%, respectively, indicate that most labels are well-aligned with ground-truth annotations. Notably, these results were achieved without the use of fine-grained manual labels during training, underscoring the potential of our approach for efficient and scalable historical map analysis.
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