An automated system for name placement which complies with cartographic quality criteria: The hydrographic network

Barrault, Mathieu

doi:10.1007/3-540-63623-4_71

Cited by 10 publications

(12 citation statements)

References 1 publication

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“…Algorithms for point labeling were developed in a sliding model by Van Kreveld, Strijk, and Wolff (1999) or in a comprehensive model by Rylov and Reimer (2014a). 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.…”

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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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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“…Since there are only ve edges which belong to the same segment that an edge f can intersect, we can always choose Olog n groups at that together form the edges in E without those belonging to the same segment as f. The numberof clauses is multiplied by a factor log n since for all Olog n groups chosen we need a clause. Theorem 3 The decision segment labeling problem for arbitrary segments can be solved in On 4 3 polylog n time and space.…”

Section: Labeling Arbitrary Segmentsmentioning

confidence: 99%

“…For a bibliography of map labeling papers see 15 . Besides point feature map labeling, there are a few papers which deal with line labeling. Barrault 3,4 proposed an algorithm for river labeling and for road network labeling. Kakoulis A rectilinear map consists of n disjoint horizontal and vertical line segments.…”

Section: Introductionmentioning

confidence: 99%

Labeling a rectilinear map more efficiently

Kreveld

1999

Information Processing Letters

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Given a rectilinear map consisting of n disjoint line segments, the corresponding labeling problem is to place a rectangle at each segment, allowing three possible positions, such that the rectangles do not intersect. This problem has a decision and a height maximization version. This paper improves results from Poon, Zhu and Chin: A polynomial time solution for labeling a rectilinear map IPL 65 1998, pp. 201 207. An algorithm is proposed that improves the running time of the decision problem from On 2 t o On log n n, with n the inverse of the Ackermann function. An algorithm for the height maximization problem is presented that lowers the running time from On 2 log n t o On log 2 n n. These bounds are for the pointer machine model; in the RAM model we can drop the n factor. We also describe an algorithm to solve the decision labeling problem where segments of arbitrary directions are allowed in On 4 3 polylog n time and space.

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“…The text labels could be placed at any rectangle around the anchor point, if an edge of the rectangle passes through the point (Kreveld et al 1998). For polyline labeling, Barrault (1997) has taken curved labels into account, where polylines are split into short segments for labeling according to their lengths and branches. Strijk and Wolff (2001) considered labeling with polylines by using a strip to find suitable label candidates.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multi-resolution labeling in virtual landscapes

Chen¹,

Zhang²,

Ma³

et al. 2010

International Journal of Geographical Information Science

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Labeling plays an important role in map production, attaching specific texts to related geographic elements to provide clear environmental references. In three-dimensional geographical information systems (3DGISs), however, cluttering happens fairly commonly because of the unexpected overlapping and occlusion among labels and related objects, and results in an ambiguous and obscure environment. It generally also takes large computing power and memory to visualize spatial entities. Aimed at both unambiguous and efficient 3D map display, this article proposes an adaptive multi-resolution labeling method to deal with point, polyline, and polygon features labeling in a 3D landscape. It implements adaptive placement and view-driven label filtering without obscuring other visual features. The experiments indicated that the display of overlapping labels and label popping are reduced significantly with less computation burden while retaining the rendering quality.

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An automated system for name placement which complies with cartographic quality criteria: The hydrographic network

Cited by 10 publications

References 1 publication

A formal approach to the automated labeling of groups of features

A formal approach to the automated labeling of groups of features

Labeling a rectilinear map more efficiently

Adaptive multi-resolution labeling in virtual landscapes

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