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arXiv Computer Vision By Remi Petitpierre

Automated Detection and Encoding of Cartographic Signs in Historical Maps

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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