A methodology for placement and evaluation of area map labels

Barrault, Mathieu

doi:10.1016/s0198-9715(00)00039-9

Cited by 24 publications

(8 citation statements)

References 5 publications

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“…length, clearance from boundaries, symmetry, horizontal placement, and conformity, which are derived from generally accepted standards for manual label placement. For the placement of curved label, Barrault (2001) has introduced a new measure to evaluate the fitness of a circular arc with respect to the boundary of the area being labelled. Furthermore, a near-real time method for curved label placement has been proposed based on the approach developed by Barrault (2001), through improving the efficiency of the evaluation process utilizing the skeleton of polygon (Krumpe and Mendel, 2020).…”

Section: Related Researchesmentioning

confidence: 99%

Automatic Label Placement of Area-Features Using Deep Learning

Li¹,

Sakamoto²,

Shinohara³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Label placement is one of the most essential tasks in the fields of cartography and geographic information systems. Numerous studies have been conducted on the automatic label placement for the past few decades. In this study, we focus on automatic label placement of area-feature, which has been relatively less studied than that of point-feature and line-feature. Most of the existing approaches have adopted a rule-based algorithm, and there are limitations in expressing the characteristics of label placement for area-features of various shapes utilizing handcrafted rules, criteria, objective functions, etc. Hence, we propose a novel approach for automatic label placement of area-feature based on deep learning. The aim of the proposed approach is to obtain the complex and implicit characteristics of area-feature label placement by manual operation directly and automatically from training data. First, the area-features with vector format are converted into a binary image. Then a key-point detection model, which simultaneously detect and localize specific key-points from an image, is applied to the binary image to estimate the candidate positions of labels. Finally, the final label placement positions for each area-feature are determined via simple post-process. To evaluate the proposed approach, the experiments with cadastral data were conducted. The experimental results show that the ratios of the estimation errors within 1.2 m (corresponding to one pixel of the input image) were 92.6% and 94.5% in the center and upper-left placement style, respectively. It implies that the proposed approach could place the labels for area-features automatically and accurately.

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Section: Related Researchesmentioning

confidence: 99%

Automatic Label Placement of Area-Features Using Deep Learning

Li¹,

Sakamoto²,

Shinohara³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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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“…Among the most interesting heuristics that deal with the calculation of label placement are: the Tabu Search Heuristic (Yamamoto, et al, 2002); Simulated Annealing (Christensen, et al, 1995;Zoraster, 1997); the Greedy Randomized Adaptive Search Procedure (Cravo, et al, 2008); Multiple Choice Integer Programming (Zoraster, 1990); and Genetic Algorithms (van Dijk, 2001). A number of solutions have also been proposed to specifically label linear objects (Edmondson, et al, 1996;Wolff, et al, 2000), and polygonal objects (Barrault, 2001). Furthermore, different applications for map displays have been taken into account in the construction of the heuristics.…”

Section: Towards Efficient Cartographic Displaysmentioning

confidence: 99%