Generalizable Multiscale Segmentation of Heterogeneous Map Collections

TL;DR

This paper introduces Semap, a new dataset and a multiscale segmentation framework that enhances the generalizability of historical map recognition across diverse styles, scales, and regions.

Contribution

It presents a novel benchmark dataset and a robust segmentation method that together improve transferability and performance in heterogeneous map collections.

Findings

State-of-the-art performance on multiple datasets

Segmentation stability across diverse map styles and regions

Effective combination of procedural synthesis and multiscale integration

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.