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

Harrie, Lars LU orcid ; Oucheikh, Rachid LU ; Nilsson, Åsa ; Oxenstierna, Andreas ; Cederholm, Pontus LU ; Wei, Lai ; Richter, Kai Florian and Olsson, Perola LU (2022) In Journal of Geovisualization and Spatial Analysis 6(1).
Abstract

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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Automated cartography, City wayfinding maps, Deep learning, Generative adversarial networks, Image synthesis, Map labeling, Map production challenges
in
Journal of Geovisualization and Spatial Analysis
volume
6
issue
1
article number
16
publisher
Springer
external identifiers
  • scopus:85130713237
ISSN
2509-8829
DOI
10.1007/s41651-022-00115-z
language
English
LU publication?
yes
id
47f25744-a403-4058-8ea0-c29e464e7785
date added to LUP
2022-12-28 11:19:50
date last changed
2023-05-10 11:06:33
@article{47f25744-a403-4058-8ea0-c29e464e7785,
  abstract     = {{<p>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.</p>}},
  author       = {{Harrie, Lars and Oucheikh, Rachid and Nilsson, Åsa and Oxenstierna, Andreas and Cederholm, Pontus and Wei, Lai and Richter, Kai Florian and Olsson, Perola}},
  issn         = {{2509-8829}},
  keywords     = {{Automated cartography; City wayfinding maps; Deep learning; Generative adversarial networks; Image synthesis; Map labeling; Map production challenges}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Springer}},
  series       = {{Journal of Geovisualization and Spatial Analysis}},
  title        = {{Label Placement Challenges in City Wayfinding Map Production—Identification and Possible Solutions}},
  url          = {{http://dx.doi.org/10.1007/s41651-022-00115-z}},
  doi          = {{10.1007/s41651-022-00115-z}},
  volume       = {{6}},
  year         = {{2022}},
}