Abstract Digitizing historical topographic maps is essential for spatial analysis in GIS; however, conventional methods for digitizing these maps are labor-intensive and challenging due to non-explicit boundaries and inconsistent map styles. We address these challenges by proposing a new Map Style Segmentation (MapStyleSeg) method that employs unsupervised domain adaptation (UDA) from deep learning (DL) to enhance cross-style, cross-year automatic map segmentation and conversion.
