Details
| Original language | English |
|---|---|
| Article number | 733 |
| Journal | ISPRS International Journal of Geo-Information |
| Volume | 10 |
| Issue number | 11 |
| Publication status | Published - 28 Oct 2021 |
Abstract
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.
Keywords
- Bicycle infrastructure, Computer vision, Object detection, Social media, Urban planning, Visual analytics, Volunteered geographical information
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: ISPRS International Journal of Geo-Information, Vol. 10, No. 11, 733, 28.10.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Using object detection on social media images for urban bicycle infrastructure planning
T2 - A case study of Dresden
AU - Knura, Martin
AU - Kluger, Florian
AU - Zahtila, Moris
AU - Schiewe, Jochen
AU - Rosenhahn, Bodo
AU - Burghardt, Dirk
N1 - Funding Information: Funding: This collaboration was realized within the DFG Priority Programme (SPP 1894/2) and supported by grants COVMAP (RO 2497/12-2), TOVIP (SCHI 1008/11-1) and EVA-VGI 2 (BU 2605/8-2).
PY - 2021/10/28
Y1 - 2021/10/28
N2 - 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.
AB - 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.
KW - Bicycle infrastructure
KW - Computer vision
KW - Object detection
KW - Social media
KW - Urban planning
KW - Visual analytics
KW - Volunteered geographical information
UR - http://www.scopus.com/inward/record.url?scp=85119020016&partnerID=8YFLogxK
U2 - 10.3390/ijgi10110733
DO - 10.3390/ijgi10110733
M3 - Article
AN - SCOPUS:85119020016
VL - 10
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
SN - 2220-9964
IS - 11
M1 - 733
ER -