Automatic Merging Method for Sectional Map Based on Deep Learning

Owing to time and scene constraints, a significant number of sectional maps exist in paper form. These maps contain a vast amount of data and hold high information value. However, they often suffer from issues such as annotations, stains, deformation, and missing content during preservation. Traditional processing methods require a large amount of manual image registration, which is extremely inconvenient. In this study, a map image labeling program is designed using OpenCV to prepare a map image dataset, and the U2Net-p algorithm for map segmentation is trained on this dataset. Furthermore, a comprehensive method for automatically merging sectional maps is designed and implemented, which can repair and process sectional maps and seamlessly integrate them into target grids according to map sheet numbering rules. This method has been applied to the production of base maps for natural resource demarcation projects, achieving a stitching accuracy of 96.67% on marked anchor points and considerably improving processing speed. This indicates that our approach has broad application value in the field of automatic stitching and fusion of sectional map images.