构建通用历史地图分割模型，突破风格尺度差异限制。
Generalizable Multiscale Segmentation of Heterogeneous Map Collections
- 融合程序化数据生成与多尺度整合，提升模型泛化能力。
- 在HCMSSD和Semap上达当前最优，跨集合表现稳定。
- 适合历史地理、数字人文研究者拓展古地图分析能力。
历史地图集在风格、比例尺和地理范围上高度多样，通常由多张单页文档构成。然而，现有地图识别研究多针对同质地图系列设计专用模型。本文旨在开发可泛化的语义分割模型与本体。首先，提出Semap——一个包含1,439个手工标注图像块的新开放基准数据集，反映历史地图的多样性。其次，设计一种结合程序化数据合成与多尺度融合的分割框架，显著提升鲁棒性与迁移能力。该框架在HCMSSD和Semap数据集上均达到当前最优性能，表明以多样性为导向的地图识别不仅可行，且具优势。结果表明，分割效果在不同地图集、比例尺、地理区域及出版背景下保持稳定。通过提供基准数据集与通用分割方法，本工作为将长尾地图档案融入历史地理研究开辟了路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。