Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 153, Issue -, Pages 48-58Publisher
ELSEVIER
DOI: 10.1016/j.isprsjprs.2019.04.017
Keywords
Street-level imagery; Urban physical environment; Urban mobility; Social sensing; Deep learning
Categories
Funding
- National Key R&D Program of China [2017YFB0503602]
- National Natural Science Foundation of China [41830645, 41625003]
- China Postdoctoral Science Foundation [2018M641068]
Ask authors/readers for more resources
Street-level imagery has covered the comprehensive landscape of urban areas. Compared to satellite imagery, this new source of image data has the advantage in fine-grained observations of not only physical environment but also social sensing. Prior studies using street-level imagery focus primarily on urban physical environment auditing. In this study, we demonstrate the potential usage of street-level imagery in uncovering spatio-temporal urban mobility patterns. Our method assumes that the streetscape depicted in street-level imagery reflects urban functions and that urban streets of similar functions exhibit similar temporal mobility patterns. We present how a deep convolutional neural network (DCNN) can be trained to identify high-level scene features from street view images that can explain up to 66.5% of the hourly variation of taxi trips along with the urban road network. The study shows that street-level imagery, as the counterpart of remote sensing imagery, provides an opportunity to infer fine-scale human activity information of an urban region and bridge gaps between the physical space and human space. This approach can therefore facilitate urban environment observation and smart urban planning.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available