期刊
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
卷 10, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/ijgi10110733
关键词
object detection; social media; urban planning; bicycle infrastructure; computer vision; volunteered geographical information; visual analytics
资金
- DFG Priority Programme [SPP 1894/2]
- COVMAP [RO 2497/12-2]
- TOVIP [SCHI 1008/11-1]
- EVA-VGI 2 [BU 2605/8-2]
Analyzing the demand for bicycle parking facilities in urban areas using object detection of social media images can provide valuable information for urban bicycle infrastructure planning, aiding in identifying potential locations for new bike parking facilities.
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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