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arXiv Computer Vision By Remi Petitpierre

Generalizable Multiscale Segmentation of Heterogeneous Map Collections

Read the original on arXiv Computer Vision →

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.

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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