Label Placement Challenges in City Wayfinding Map Production—Identification and Possible Solutions

Abstract Map label placement is an important task in map production, which needs to be automated since it tedious and requires a significant amount of manual work. In this paper, we identify five cartographic labeling situations that present challenges by causing intensive work production city wayfinding maps, e.g., high density areas , utilizing true geometries methods creating good relationship between text labels icons . We evaluate these open source tool (QGIS), provide results from preliminary study, discuss if there are other techniques could applicable solving challenges. These based on quantified rules or machine learning. focus deep learning for several examples application domains might have potential placement. The aim the paper explore those recommend future practical studies each identified production. believe targeting revealed using proposed solutions will significantly raise automation level producing thus, having real, measurable impact time costs.