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
arXiv:2606. 19956v1 Announce Type: new Abstract: Map generalization remains one of the fundamental tasks in cartography, especially for the simplification and aggregation of complex building footprints.
By Yanning Wang, Zhiyong Zhou, Zhouyu Liu, Mengni Yu, Yu Feng
arXiv:2610.33758v1 Announce Type: new
Abstract: Road infrastructure perception is a fundamental component of autonomous driving, as it recovers the structured road elements that define roadway geomet...
By Guoyang Zhao, Weiqing Qi, Kai Zhang, Zhihai Bi, Tianshuai Hu, Zeying Gong, Dezong Zhao, Jun Ma
arXiv:2601. 06135v3 Announce Type: replace Abstract: Spatial computation in geographic systems increasingly requires query-conditioned, local, interpretable aggregation under metric constraints.
By Zhaowen Fan, Yunxiang Han
The paper introduces View distance, a novel metric that projects high‑dimensional data onto all pairwise two‑dimensional planes and sums the Euclidean distances across these projections. It satisfies metric axioms, couples features, suppresses redundancy, and captures anisotropic geometry. To make it scalable, the authors propose a plane‑selection strategy using iterative Maximum Weight Matching, reducing complexity from ω(n²) to ω(k) and demonstrating competitive performance on twelve datasets.
By Yiqun Zhang, Hou-biao Li
arXiv:2606. 04513v1 Announce Type: new Abstract: Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane networks for hundreds of cities remains highly labor-intensive.
By Deguo Xia, Zihan Li, Haochen Zhao, Dong Xie, Yuyao Kong, Xiyan Liu, Jizhou Huang, Mengmeng Yang, Diange Yang
The paper introduces the Rashomon set for dimension reduction, a collection of equally good embeddings that preserve high‑dimensional structure. It proposes PCA‑informed alignment to make axes interpretable, concept‑alignment regularization to incorporate external knowledge, and a method to extract trustworthy nearest‑neighbor relationships across the Rashomon set for refined embeddings. These techniques aim to produce interpretable, robust, and goal‑aligned visualizations by leveraging multiple valid embeddings instead of a single one.
By Yiyang Sun, Haiyang Huang, Gaurav Rajesh Parikh, Cynthia Rudin