# Leveraging LLMs and attention-mechanism for automatic annotation of historical maps

arXiv: https://arxiv.org/abs/2504.11050 · HTML: https://mlsanity.com/paper/2504.11050/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [Semantic Image Segmentation: Two Decades of Research](https://mlsanity.com/paper/2302.06378/) (2023)
- [Generating Evidential BEV Maps in Continuous Driving Space](https://mlsanity.com/paper/2302.02928/) (2023)
- [Semantic Segmentation for Sequential Historical Maps by Learning from Only One Map](https://mlsanity.com/paper/2501.01845/) (2025)

## Built on by (most cited first, 2 of 2)

- [Automatic Uncertainty-Aware Synthetic Data Bootstrapping for Historical Map Segmentation](https://mlsanity.com/paper/2511.15875/) (2025)
- [Rethinking Side-Channel Analysis: Automated Discovery and Analysis of Side-Channel Leakage with LLM-Assisted Agents](https://mlsanity.com/paper/2605.17406/) (2026)

## Abstract (arXiv)

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.