A Practical Algorithm for the External Annotation of Area Features

Rylov, Maxim; Reimer, Andreas W.

doi:10.1179/1743277414y.0000000091

Cited by 14 publications

(5 citation statements)

References 52 publications

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“…In this case, the area features are assumed as point features. For example, Rylov and Reimer [21] proposed a novel algorithm that placed the label outside the area feature if the feature did not have enough internal space. In many prior automatic label placement algorithms, the feature label was often restricted to a specific area around features.…”

Section: Related Workmentioning

confidence: 99%

Multiple Geographical Feature Label Placement Based on Multiple Candidate Positions in Two Degrees of Freedom Space

Deng

Guo

Lessani³

2021

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Multiple Geographical Feature Label Placement Based on Multiple Candidate Positions in Two Degrees of Freedom Space

Deng

Guo

Lessani³

2021

IEEE Access

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“…The problem of labeling linear features was addressed among others in Barrault and Lecordix (1995), Edmondson et al (1996) and Chirié (2000). Van Roessel (1989), Barrault (2001), and Rylov and Reimer (2014b) proposed methods for labeling areal features. The problem of labeling feature groups has also received some attention.…”

Section: Related Workmentioning

confidence: 99%

A formal approach to the automated labeling of groups of features

Reimer

Goethem

Rylov

et al. 2015

Cartography and Geographic Information Science

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A variety of recurring geographic entities form collections of point, line, or area features. Examples are groups of islands (Archipelago), relief features in deserts, periglacial lakes or geomorphological forms, such as drumlins and sinkholes. All these groups of features might be best identified with a single label. Surprisingly, the (automated) labeling of groups of features has received little attention so far. We propose a framework to determine formal measures that describe the geometric aspects of the cartographic design space for labeling feature groups. Our framework gives rise to a large variety of geometrically optimal labels. We list the optimal label positions and shapes for which labels can already be computed using existing algorithms. However, in many of the cases computing optimal label placements is still an open algorithmic question which can readily be investigated in future work. Once the necessary algorithms are developed, our framework provides an objective basis to investigate the geometric measures used by cartographers to label groups of features. We preliminarily explore the applicability of our framework using one of the geometric optimality choices.

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“…Most of the recent literature [21] has adopted the so-called skeleton method in which the labels should be placed on skeleton curves of the areas. Rylov and Reimer [22] introduced a novel and efficient algorithm for labeling area features externally, i.e., outside the polygonal boundary, and claimed to achieve efficient label placement that is close to the quality of manually produced cartographic products.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid of Differential Evolution and Genetic Algorithm for the Multiple Geographical Feature Label Placement Problem

Deng

et al. 2019

IJGI

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Label placement is a difficult problem in automated map production. Many methods have been proposed to automatically place labels for various types of maps. While the methods are designed to automatically and effectively generate labels for the point, line and area features, less attention has been paid to the problem of jointly labeling all the different types of geographical features. In this paper, we refer to the labeling of all the graphic features as the multiple geographical feature label placement (MGFLP) problem. In the MGFLP problem, the overlapping and occlusion among labels and corresponding features produces poorly arranged labels, and results in a low-quality map. To solve the problem, a hybrid algorithm combining discrete differential evolution and the genetic algorithm (DDEGA) is proposed to search for an optimized placement that resolves the MGFLP problem. The quality of the proposed solution was evaluated using a weighted metric regarding a number of cartographical rules. Experiments were carried out to validate the performance of the proposed method in a set of cartographic tasks. The resulting label placement demonstrates the feasibility and the effectiveness of our method.

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A Practical Algorithm for the External Annotation of Area Features

Cited by 14 publications

References 52 publications

Multiple Geographical Feature Label Placement Based on Multiple Candidate Positions in Two Degrees of Freedom Space

Multiple Geographical Feature Label Placement Based on Multiple Candidate Positions in Two Degrees of Freedom Space

A formal approach to the automated labeling of groups of features

A Hybrid of Differential Evolution and Genetic Algorithm for the Multiple Geographical Feature Label Placement Problem

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