Enhancing semantic information of vector road networks using tile maps
Year: 2026
Type: article
Abstract: Semantic information is a critical component of spatial data; however, it is often incomplete, limiting the usability of vector road networks. Tile maps are freely accessible, information-rich raster data containing complete road annotations. However, optical character recognition (OCR) applied to tile maps often produces fragmented text boxes and detects non-road text owing to resolution limitations and varying text orientations. To address these challenges, a method for enhancing the semantic information of vector road networks using tile maps is proposed in this study. The vector road network and tile maps are co-registered and segmented. An improved text extraction and merging strategy based on PaddleOCR is developed using geometric constraints to reconstruct fragmented road-name text. Furthermore, a geometry-constrained text classification model, termed the RoadSense Classifier, is introduced to filter non-road text and retain accurate road names. Matching the extracted road text to its corresponding road segments enriches the semantic information. Experiments using tile map data from the Third Ring Road area of Zhengzhou City and the main urban area of Xinxiang show that the proposed method achieves a precision of 98.86% and 97.64%, recall of 85.07% and 83.98%, and F1-score of 91.45% and 89.32%, respectively, improving vector road attribute completeness.
Authors: Mengwei Zhang, Haizhong Qian, Xingui Liu, Chengyi Liu, Xianyong Gong, Xiao Wang, Zhekun Huang
Institution: PLA Information Engineering University
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
Award: G2445744329
OpenAlex ID: W7155171584