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

Lars Harrie, Rachid Oucheikh, Åsa Nilsson, Andreas Oxenstierna, Pontus Cederholm, Lai Wei, Kai Florian Richter, Perola Olsson

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish
Article number16
JournalJournal of Geovisualization and Spatial Analysis
Issue number1
Publication statusPublished - 2022 Jun

Subject classification (UKÄ)

  • Other Computer and Information Science

Free keywords

  • Automated cartography
  • City wayfinding maps
  • Deep learning
  • Generative adversarial networks
  • Image synthesis
  • Map labeling
  • Map production challenges


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