Abstract
Historical map collections are highly diverse in style, scale, and geographic focus, often consisting of many single-sheet documents. Yet most work in map recognition focuses on specialist models tailored to homogeneous map series. In contrast, this article aims to develop generalizable semantic segmentation models and ontology. First, we introduce Semap, a new open benchmark dataset comprising 1,439 manually annotated patches designed to reflect the variety of historical map documents. Second, we present a segmentation framework that combines procedural data synthesis with multiscale integration to improve robustness and transferability. This framework achieves state-of-the-art performance on both the HCMSSD and Semap datasets, showing that a diversity-driven approach to map recognition is not only viable but also beneficial. The results indicate that segmentation performance remains largely stable across map collections, scales, geographic regions, and publication contexts. By proposing benchmark datasets and methods for the generic segmentation of historical maps, this work opens the way to integrating the long tail of cartographic archives to historical geographic studies.
Abstract (translated)
历史地图收藏在风格、比例尺和地理焦点方面具有高度多样性，通常由许多单张文档组成。然而，大多数地图识别工作集中在针对同质化地图系列的专用模型上。与此相反，本文旨在开发通用化的语义分割模型和本体论。首先，我们介绍了Semap，这是一个新的开放基准数据集，包含1,439个手动注释的补丁，旨在反映历史地图文档的多样性。其次，我们提出了一种结合程序化数据合成与多尺度集成的分割框架，以提高鲁棒性和迁移性。该框架在HCMSSD和Semap数据集上均取得了最先进的性能，表明多样性的驱动方法对于地图识别不仅可行而且有益。结果显示，在不同的地图集合、比例尺、地理区域及出版背景下，分割表现基本保持稳定。通过提出针对历史地图通用分割的基准数据集和方法，这项工作为将长尾制图档案整合到历史地理研究中开辟了道路。
URL
https://arxiv.org/abs/2603.05037
