Using object detection on social media images for urban bicycle infrastructure planning : a case study of Dresden

ORCID
0000-0002-1678-866X
Affiliation
Lab for Geoinformatics and Geovisualization (g2lab), HafenCity University Hamburg
Knura, Martin;
ORCID
0000-0002-8557-5238
Affiliation
Institute of Information Processing, Leibniz University Hannover
Kluger, Florian;
ORCID
0000-0002-5652-7050
Affiliation
Institute of Cartography, Dresden University of Technology
Zahtila, Moris;
GND
1022555103
VIAF
180149294077080520280
ORCID
0000-0002-6717-0923
Affiliation
Lab for Geoinformatics and Geovisualization (g2lab), HafenCity University Hamburg
Schiewe, Jochen;
GND
1156360668
VIAF
25488374
ORCID
0000-0003-3861-1424
Affiliation
Institute of Information Processing, Leibniz University Hannover
Rosenhahn, Bodo;
GND
1048748588
VIAF
161427481
ORCID
0000-0003-2949-4887
Affiliation
Institute of Cartography, Dresden University of Technology
Burghardt, Dirk

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.

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