A practical algorithm for the external annotation of area aeatures
One of the subtasks of automated map labelling that has received little attention so far is the labelling of areas. Geographic areas are often are represented by concave polygons which pose severe limitations on straightforward solutions due to their great variety of shape, a fact worsened by the la...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2017
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| In: |
The cartographic journal
Year: 2016, Jahrgang: 54, Heft: 1, Pages: 61-76 |
| ISSN: | 1743-2774 |
| DOI: | 10.1179/1743277414Y.0000000091 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1179/1743277414Y.0000000091 Verlag, Volltext: https://doi.org/10.1179/1743277414Y.0000000091 
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| Verfasserangaben: | Maxim Rylov, Andreas Reimer |
| Zusammenfassung: | One of the subtasks of automated map labelling that has received little attention so far is the labelling of areas. Geographic areas are often are represented by concave polygons which pose severe limitations on straightforward solutions due to their great variety of shape, a fact worsened by the lack of measures for quantifying feature-label relationships. We introduce a novel and efficient algorithm for labelling area features externally, i.e. outside their polygonal boundary. Two main contributions are presented in the following. First, it is a highly optimized algorithm of generating candidate placements utilizing algorithms from the field of computational geometry. Second, we describe a measure for scoring label positions. Both solutions based on a series of well-established cartographic precepts about name positioning in the case of semantic enclaves such as islands or lakes. The results of our experiments show that our algorithm can efficiently place labels with a quality that is close to the quality of traditional cartographic products made by human cartographers. |
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| Beschreibung: | Gesehen am 28.05.2018 Published online: 12 Jul 2016 |
| Beschreibung: | Online Resource |
| ISSN: | 1743-2774 |
| DOI: | 10.1179/1743277414Y.0000000091 |