No more Autobahn!

Runge, Nina; Samsonov, Pavel; Degraen, Donald; Schöning, Johannes

doi:10.1145/2856767.2856804

Cited by 30 publications

(13 citation statements)

References 7 publications

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“…In addition, we as technologist can help to collect missing attribute information to improve the route generation of wheelchair routing algorithms. Automatic and semi-automatic approaches, similar to the ones proposed by [2,9,16,20], could be used to fill this gap.…”

Section: Resultsmentioning

confidence: 99%

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Disabled, but at what cost?

Tannert

Schöning

2018

Proceedings of the 20th International Conference on Human-Computer Interaction With Mobile Devices and Services

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Platforms like Google Maps or Bing Maps are used by a large number of users to find the shortest path to their destinations. While these services mainly focus on supporting drivers and pedestrians, first services exist that support wheelchair users. Routing algorithms for wheelchair users try to avoid obstacles like stairs or bollards and optimize on criteria like surface properties and slope of the route. In this study, we undertake the first controlled examination of wheelchair routing approaches. By analyzing three routing platforms, including two wheelchair routing algorithms and three pedestrian routing algorithms, across fifteen major cities in Germany, our results highlight that the routes for wheelchair users are significantly longer and partially also more complex than those for pedestrians. In addition, we show that today's pedestrian routing algorithms also output very diverse routes.

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Section: Resultsmentioning

confidence: 99%

“…slope of sidewalks or different surface properties like sand or cobblestone, which are difficult to overcome in a wheelchair). Various approaches analyzed the accessibility of sidewalks with sensors and technical equipment such as depth cameras or acceleration sensors [3,4,5,6,9,12,16,19,20].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Disabled, but at what cost?

Tannert

Schöning

2018

Proceedings of the 20th International Conference on Human-Computer Interaction With Mobile Devices and Services

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“…Some areas such as construction sites, garbage dumps, and empty lots are commonly considered potential breeding sites [24,51] but GSV images do not provide sufficient coverage to detect containers in them. They may be best accounted for by using scene recognition techniques [36], like those used in the work of Runge et al [35] and are not the focus of this study. In addition, indoor breeding sites and sites in backyards are not considered in this study due to the particular coverage of GSV images.…”

Section: Methodsmentioning

confidence: 99%

“…Runge et al [35] made use of the scene recognition convolutional neural net of Zhou et al [36] to label GSV images and assembled them into maps to find scenic routes for autonomous vehicle navigation. Although their application differs from ours, their pipeline and the structure of their feature maps are similar to those in this study.…”

Section: Introductionmentioning

confidence: 99%

Large scale detailed mapping of dengue vector breeding sites using street view images

et al. 2019

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Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale.

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“…Our work was highly inspired by the Autobahn system of Runge et al [13]. Their system was designed to generate scenic routes using GSV images to classify route segments based on their visual characteristics in order to enhance the driving experience.…”

Section: Related Workmentioning

confidence: 99%