Abstract

Black-and-white draft geological maps, produced during fieldwork, often contain dense handwritten annotations overlaid on monochromatic contour basemaps. Although interpretable in grayscale, the lack of color makes it difficult to visually distinguish overlapping or adjacent geological units, especially when boundaries are unclear and annotation styles vary. However, colorizing these draft maps is labor-intensive but essential, as they may be the only source of detailed geological information for certain regions. This hinders both human interpretation and downstream tasks such as map digitization and critical mineral resource assessment. We target the problem of automated colorization of draft geological maps. The challenge lies in interpreting noisy visual cues from uncolored sketches and assigning appropriate colors according to their geological categories. We propose a novel machine learning approach that …

Date

November 3, 2025

Authors

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

Book

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

Pages

1126-1129