Improving label placement quality by considering basemap detail with a raster-based approach

Rylov, Maxim; Reimer, Andreas W.

doi:10.1007/s10707-014-0214-6

Cited by 12 publications

(6 citation statements)

References 53 publications

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“…Map labeling rules concern the whole labeling process which includes the following: (1) the choice of labels to show and their classification, (2) determination of font characteristics, and (3) label placement (Yoeli, 1972). In this study, we are mainly interested in the placement of labels, for which several general rules must be obeyed (for more details, see Imhof 1975;Wood 2000;van Dijk 2002;Rylov and Reimer 2015):…”

Section: General Label Placement Rulesmentioning

confidence: 99%

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

Harrie

Oucheikh

Nilsson³

et al. 2022

J geovis spat anal

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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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Section: General Label Placement Rulesmentioning

confidence: 99%

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

Harrie

Oucheikh

Nilsson³

et al. 2022

J geovis spat anal

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“…We present a new approach for this problem and propose a system that shows details in an area that is too large to be shown on the screen within a coherent dynamic visualization. To this end, we resort to a grid-based mathematical optimization similar to Birsak et al [15] and Rylov et al [24].…”

Section: Related Workmentioning

confidence: 99%

“…To allow an easier and structured placement of detail lenses, we follow the design guidelines of recent work (see [15] and [24]) and partition the map into a 2d Cartesian grid and choose the side length of the cells so that a small integer number of cells results in well-visible detail lenses. We identified a value of about 40 meters to deliver good results.…”

Section: Cell Domainmentioning

confidence: 99%

Dynamic Path Exploration on Mobile Devices

Birsak

Weiss

Wonka

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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Abstract-We present a novel framework for visualizing routes on mobile devices. Our framework is suitable for helping users explore their environment. First, given a starting point and a maximum route length, the system retrieves nearby points of interest (POIs). Second, we automatically compute an attractive walking path through the environment trying to pass by as many highly ranked POIs as possible. Third, we automatically compute a route visualization that shows the current user position, POI locations via pins, and detail lenses for more information about the POIs. The visualization is an animation of an orthographic map view that follows the current user position. We propose an optimization based on a binary integer program (BIP) that models multiple requirements for an effective placement of detail lenses. We show that our path computation method outperforms recently proposed methods and we evaluate the overall impact of our framework in two user studies.

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“…As mentioned before, our labeling strategy prefers to consider the PFLP as a problem of combinatorial optimization, which require two components to be defined: a discrete search space and an objective function [28]. Then, the problem can be solved using a variety of approximations and heuristics search approaches.…”

Section: Label Placement Processmentioning

confidence: 99%

“…Further, the framework of the slider model is more applicable to label number maximization. For the optimization of the label quality, which synthesizes all of the important quality criteria (such as label association, aesthetics, and priority [27,28]), the combinatorial optimization strategy, which is widely used by the fixed-position model, is preferred. Moreover, because the slider model is mostly applied to axis-parallel rectangular labeling, an exploration of non-textual, arbitrarily shaped label placement has not been performed.…”

Section: Introductionmentioning

confidence: 99%

A Labeling Model Based on the Region of Movability for Point-Feature Label Placement

Lin

Zhang

Zhu

et al. 2016

IJGI

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Automatic point-feature label placement (PFLP) is a fundamental task for map visualization. As the dominant solutions to the PFLP problem, fixed-position and slider models have been widely studied in previous research. However, the candidate labels generated with these models are set to certain fixed positions or a specified track line for sliding. Thus, the whole surrounding space of a point feature is not sufficiently used for labeling. Hence, this paper proposes a novel label model based on the region of movability, which comes from plane collision detection theory. The model defines a complete conflict-free search space for label placement. On the premise of no conflict with the point, line, and area features, the proposed model utilizes the surrounding zone of the point feature to generate candidate label positions. By combining with heuristic search method, the model achieves high-quality label placement. In addition, the flexibility of the proposed model enables placing arbitrarily shaped labels.

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Improving label placement quality by considering basemap detail with a raster-based approach

Cited by 12 publications

References 53 publications

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

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

Dynamic Path Exploration on Mobile Devices

A Labeling Model Based on the Region of Movability for Point-Feature Label Placement

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