Lan, Li, Peng and Gong – AUTOMATED LABELLING OF SCHEMATIC MAPS BY OPTIMIZATION WITH KNOWLEDGE ACQUIRED FROM EXISTING MAPS 2020

Description

Uncover a novel approach to automated schematic map labeling in this 18-page research article, “Automated labeling of schematic maps by optimization with knowledge acquired from existing maps,” published in 2020 by *Transactions in GIS* (Wiley). Authored by Tian Lan, Zhilin Li, Qian Peng, and Xinyu Gong from The Hong Kong Polytechnic University and Southwest Jiaotong University, this peer-reviewed paper offers a robust and trustworthy solution to a critical cartographic challenge. The study introduces a unique methodology that acquires labeling rules, including potential positions and preferences, directly from a diverse set of existing schematic maps, overcoming limitations of manually defined rules. These empirically derived rules are then integrated into a sophisticated optimization algorithm. Illustrated with numerous high-quality figures, including actual metro maps from Tianjin and Hong Kong used for experimental validation, the study demonstrates its method’s effectiveness in generating clear and user-friendly labels. This is a must-read for cartographers, GIS professionals, and anyone interested in advancing automated map design.