Abstract

Historical maps often suffer from coloring errors caused by artifacts during map production or scanning. These errors result in color mismatches between important map features (e.g., polygon layers) and their corresponding map keys, which hinders both human interpretation and automated feature extraction. This paper targets the problem of automatically correcting polygon coloring errors in historical maps using only in-map information, such as the map keys. The challenge lies in the diverse visual representations of map keys and variations in coloring errors, which differ significantly both within and across maps. We propose a machine-learning model that automatically identifies and corrects color inconsistencies between map polygon layers and their visual appearances defined by the map keys on the same map. Our approach leverages polygon metadata, such as map keys describing the visual and semantic …

Date

November 3, 2025

Authors

Fandel Lin, Craig A Knoblock, Binh Vu, Yao-Yi Chiang

Book

Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems

Pages

1122-1125