Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden

With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure a...

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Autores principales:	Martin Knura, Florian Kluger, Moris Zahtila, Jochen Schiewe, Bodo Rosenhahn, Dirk Burghardt
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	object detection social media urban planning bicycle infrastructure computer vision volunteered geographical information Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/163b512fd6bc46ce9b88ea72181896d5
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id	oai:doaj.org-article:163b512fd6bc46ce9b88ea72181896d5
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spelling	oai:doaj.org-article:163b512fd6bc46ce9b88ea72181896d52021-11-25T17:52:49ZUsing Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden10.3390/ijgi101107332220-9964https://doaj.org/article/163b512fd6bc46ce9b88ea72181896d52021-10-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/733https://doaj.org/toc/2220-9964With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain.Martin KnuraFlorian KlugerMoris ZahtilaJochen SchieweBodo RosenhahnDirk BurghardtMDPI AGarticleobject detectionsocial mediaurban planningbicycle infrastructurecomputer visionvolunteered geographical informationGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 733, p 733 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	object detection social media urban planning bicycle infrastructure computer vision volunteered geographical information Geography (General) G1-922
spellingShingle	object detection social media urban planning bicycle infrastructure computer vision volunteered geographical information Geography (General) G1-922 Martin Knura Florian Kluger Moris Zahtila Jochen Schiewe Bodo Rosenhahn Dirk Burghardt Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
description	With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain.
format	article
author	Martin Knura Florian Kluger Moris Zahtila Jochen Schiewe Bodo Rosenhahn Dirk Burghardt
author_facet	Martin Knura Florian Kluger Moris Zahtila Jochen Schiewe Bodo Rosenhahn Dirk Burghardt
author_sort	Martin Knura
title	Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
title_short	Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
title_full	Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
title_fullStr	Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
title_full_unstemmed	Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden
title_sort	using object detection on social media images for urban bicycle infrastructure planning: a case study of dresden
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/163b512fd6bc46ce9b88ea72181896d5
work_keys_str_mv	AT martinknura usingobjectdetectiononsocialmediaimagesforurbanbicycleinfrastructureplanningacasestudyofdresden AT floriankluger usingobjectdetectiononsocialmediaimagesforurbanbicycleinfrastructureplanningacasestudyofdresden AT moriszahtila usingobjectdetectiononsocialmediaimagesforurbanbicycleinfrastructureplanningacasestudyofdresden AT jochenschiewe usingobjectdetectiononsocialmediaimagesforurbanbicycleinfrastructureplanningacasestudyofdresden AT bodorosenhahn usingobjectdetectiononsocialmediaimagesforurbanbicycleinfrastructureplanningacasestudyofdresden AT dirkburghardt usingobjectdetectiononsocialmediaimagesforurbanbicycleinfrastructureplanningacasestudyofdresden
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Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden

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