scholarly journals On the Edge Label Placement problem

Author(s):  
Konstantinos G. Kakoulis ◽  
Ioannis G. Tollis
Author(s):  
Christoph Daniel Schulze ◽  
Nis Wechselberg ◽  
Reinhard von Hanxleden

2007 ◽  
Vol 15 (2) ◽  
pp. 147-164 ◽  
Author(s):  
Glaydston Mattos Ribeiro ◽  
Luiz Antonio Nogueira Lorena

2014 ◽  
Vol 234 (3) ◽  
pp. 802-808 ◽  
Author(s):  
Rômulo Louzada Rabello ◽  
Geraldo Regis Mauri ◽  
Glaydston Mattos Ribeiro ◽  
Luiz Antonio Nogueira Lorena

2019 ◽  
Vol 8 (5) ◽  
pp. 237 ◽  
Author(s):  
Fuyu Lu ◽  
Jiqiu Deng ◽  
Shiyu Li ◽  
Hao Deng

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.


2009 ◽  
Vol 192 (2) ◽  
pp. 396-413 ◽  
Author(s):  
Adriana C.F. Alvim ◽  
Éric D. Taillard

2005 ◽  
Vol 15 (03) ◽  
pp. 261-277 ◽  
Author(s):  
YU-SHIN CHEN ◽  
D. T. LEE ◽  
CHUNG-SHOU LIAO

In this paper, we consider a map labeling problem where the points to be labeled are restricted on a line. It is known that the 1d-4P and the 1d-4S unit-square label placement problem and the Slope-4P unit-square label placement problem can both be solved in linear time and the Slope-4S unit-square label placement problem can be solved in quadratic time in Ref. [8]. We extend the result to the following label placement problem: Slope-4P fixed-height (width) label or elastic label placement problem and present a linear time algorithm for it provided that the input points are given sorted. We further show that if the points are not sorted, the label placement problems have a lower bound of Ω(n log n), where n is the input size, under the algebraic computation tree model. Optimization versions of these point labeling problems are also considered.


2008 ◽  
pp. 22-45 ◽  
Author(s):  
Jill Phelps Kern ◽  
Cynthia A. Brewer

The placement of feature name labels on maps has challenged mapmakers throughout history. Before the development of mapping software, placing labels in manual map production could consume up to half or more of overall map production time. This paper explores the extent to which current GIS software can place labels legibly, without overlap, and with good visual association between features and labels. This evaluation takes place in the context of a densely featured municipal sewer utility map book. The primary research objective is to evaluate the ability of current GIS software to automate label placement; the research also identifies factors that make manual refinement of automated label placement necessary in order to complete the labeling process. The research compares map-labeling tools from ESRI TM ’s ArcMap TM 9.2: the Standard Labeling Engine and the Maplex TM labeling extension. Label placement success is assessed by both quantity and quality metrics, using a methodology developed and tailored specifically for evaluation of sewer map label placement. The research found that Maplex placed almost seven percent more labels overall than the Standard Labeling Engine. For the labels they did place, both products provided equally good quality label placement: About 93 percent of labels were placed with no overlap, and virtually 100 percent of labels were placed in their preferred position. After conversion to annotation, manual label position refinement eliminated all overlaps but at the cost of a nine percent decline in the preferred position metric.


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