The paper introduces Semap, an open benchmark dataset of 1,439 manually annotated map patches that captures the diversity of historical map collections. It proposes a generalizable semantic segmentation workflow that combines procedural data synthesis with multiscale integration, achieving the best performance among evaluated methods on heterogeneous map data. The study demonstrates that segmentation accuracy remains stable across different map styles, scales, geographic regions, and publication contexts, thereby facilitating the integration of diverse cartographic archives into historical geographic research.
By Remi Petitpierre
arXiv:2608. 20218v1 Announce Type: new Abstract: Electronic Navigational Charts (ENCs) are geospatial vector datasets used in maritime navigation systems that represent hydrographic and navigational information such as depths, navigational aids, traffic schemes, and hazards.
By Jacob Arndt, Abhishek Potnis, Alexandre Sorokine
arXiv:2608. 02306v1 Announce Type: cross Abstract: We introduce a mathematical framework for shape comparison based on mapping functions from the shape domain to a common reference domain.
By Roua Rouatbi, Juan-Esteban Suarez Cardona, Ivo F. Sbalzarini
The paper introduces a two-stage framework for detecting cartographic signs in historical maps and generating interpretable embeddings that capture both semantics and stylistic variations. It presents CaSiDD, an open benchmark dataset covering multiple sign categories and diverse cartographic styles, and demonstrates the effectiveness of YOLOv10-X for automated sign detection. The authors also develop a method based on an adapted RADIOv4 backbone to encode sign form and semantics while suppressing confounding factors, providing a foundation for scalable evidence extraction in cartographic culture and geohistorical research.
By Remi Petitpierre
We introduce a mathematical framework for shape comparison based on mapping functions from the shape domain to a common reference domain. This Push-Forward Transform enables invariant and robust comparison of shapes, preserving intrinsic geometric information.
arXiv:2608.22044v1 Announce Type: new
Abstract: Modern machine learning (ML) methods are highly effective for prediction tasks, but many commonly used representations reduce complex data to fixed dim...
By George Babus, Farzana Nasrin