Automatic (Tactile) Map Generation—A Systematic Literature Review

Wabínski, Jakub; Mościcka, Albina

doi:10.3390/ijgi8070293

Cited by 25 publications

(23 citation statements)

References 71 publications

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“…At step (3), the vector geographic data collected at the previous step needs to be simplified to meet the drastic specifications of tactile maps. Many tools already exist for this task, many of them based on OpenStreetMap data, a quite complete list of such applications can be found in (Wabiński and Mościcka, 2019). But as illustrated by the Mapy.cz application (Červenka et al, 2016), the maps remain often too complex to be fully usable for visually impaired people, or even as base maps for transcriptors.…”

Section: Adapting Maps For Visually Impaired Studentsmentioning

confidence: 99%

“…But as illustrated by the Mapy.cz application (Červenka et al, 2016), the maps remain often too complex to be fully usable for visually impaired people, or even as base maps for transcriptors. The use and adaptation of map generalisation techniques, firstly designed for scale reduction in topographic maps seems really promising to achieve tactile maps that are simple enough (Štampach and Mulíčková, 2016, Wabiński and Mościcka, 2019.…”

Section: Adapting Maps For Visually Impaired Studentsmentioning

confidence: 99%

“…Geography teachers use many maps from their textbooks in class, and when they have visually impaired students, they need tactile equivalents, and even when they have access to a transcriptor, which is not always the case, it would take too long to create all these equivalents. This is why it would be useful to apply advanced cartography techniques to accelerate tactile mapmaking (Lobben, 2015), or even to automate it (Touya et al, 2019, Wabiński andMościcka, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Inferring the Scale and Content of a Map Using Deep Learning

Touya

Brisebard

Quinton

et al. 2020

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

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Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).

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Section: Adapting Maps For Visually Impaired Studentsmentioning

confidence: 99%

Section: Adapting Maps For Visually Impaired Studentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring the Scale and Content of a Map Using Deep Learning

Touya

Brisebard

Quinton

et al. 2020

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

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“…[18,23,36]). However, a comprehensive solution for automatic tactile (let alone interactive) map generation does not exist [35]. Besides, when maps are created automatically, the content is more difficult to modify for the users.…”

Section: Creation Of Accessible Interactive Tactile Drawingsmentioning

confidence: 99%

Creating Accessible Interactive Audio-Tactile Drawings using Spatial Augmented Reality

Thévin¹,

Jouffrais

Rodier

et al. 2019

Proceedings of the 2019 ACM International Conference on Interactive Surfaces and Spaces

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Figure 1.A teacher draws interactive zones directly on a tactile map with his finger and records the associated audio-feedback using a microphone. Touching the same zone will later launch the recorded audio-feedback. ABSTRACTInteractive tactile graphics have shown a true potential for people with visual impairments, for instance for acquiring spatial knowledge. Until today, however, they are not well adopted in real-life settings (e.g. special education schools). One obstacle consists in the creation of these media, which requires specific skills, such as the use of vector-graphic software for drawing and inserting interactive zones, which is challenging for stakeholders (social workers, teachers, families of people with visual impairments, etc.). We explored how a Spatial Augmented Reality approach can enhance the creation of interactive tactile graphics by sighted users. We developed the system using a participatory design method. A user study showed that the augmented reality device allowed stakeholders (N=28) to create interactive tactile graphics more efficiently than with a regular vector-drawing software (baseline), independently of their technical background.

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“…Typically, linear building patterns can be categorized into collinear alignments and curvilinear alignments [1,3]. As landscape configuration, building patterns are crucial components of urban structures, which have to be preserved when spatial scales decrease during the process of map generalization [4][5][6][7]. In addition, linear building patterns in topographic maps are important for understanding geographic space, such as exploring the semantic classification of urban structures and functions based on extracted linear building patterns [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Linear Building Patterns in Topographic Data by Using Two New Indices based on Delaunay Triangulation

Deng

Luo

2020

IJGI

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Building pattern recognition is fundamental to a wide range of downstream applications, such as urban landscape evaluation, social analyses, and map generalization. Although many studies have been conducted, there is still a lack of satisfactory results, due to the imprecision of the relative direction model of any two adjacent buildings and the ineffective extraction methods. This study aims to provide an alternative for quantifying the direction and the spatial continuity of any two buildings on the basis of the Delaunay triangulation for the recognition of linear building patterns. First, constrained Delaunay triangulations (CDTs) are created for all buildings within each block and every two adjacent buildings. Then, the spatial continuity index (SCI), the direction index (DI), and other spatial relations (e.g., distance) of every two adjacent buildings are derived using the CDT. Finally, the building block is modelled as a graph based on derived matrices, and a graph segmentation approach is proposed to extract linear building patterns. In the segmentation process, the edges of the graph are removed first, according to the global thresholds of the SCI and distance, and are subsequently subdivided into subgraphs on direction rules. The proposed method is tested using three datasets. The experimental results suggest that the proposed method can recognize both collinear and curvilinear building patterns, given that the correctness values are all above 92% for the three study areas. The results also demonstrate that the novel SCI can effectively filter many insignificant neighbor relationships in the graph segmentation process. It is noteworthy that the proposed DI is capable of measuring building relative directions accurately and works efficiently in linear building pattern extraction.

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Automatic (Tactile) Map Generation—A Systematic Literature Review

Cited by 25 publications

References 71 publications

Inferring the Scale and Content of a Map Using Deep Learning

Inferring the Scale and Content of a Map Using Deep Learning

Creating Accessible Interactive Audio-Tactile Drawings using Spatial Augmented Reality

Recognizing Linear Building Patterns in Topographic Data by Using Two New Indices based on Delaunay Triangulation

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