Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery
Joint Authors
Yao, Yao
Qian, Chen
Hong, Ye
Guan, Qingfeng
Chen, Jingmin
Dai, Liangyang
Jiang, Zhangwei
Liang, Xun
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-14
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The spatial distribution pattern of jobs and housing plays a vital role in urban planning and traffic construction.
However, obtaining the jobs-housing distribution at a fine scale (e.g., the perspective of individual jobs-housing attribute) presents difficulties due to a lack of social media data and useful models.
With user data acquired from a location-based service provider in China, this study employs a deep bag-of-features network (BagNet) to classify remote-sensing (RS) images into various jobs-housing types.
Considering Wuhan, one of the fastest developing cities in China, as a case study area, three jobs-housing types (i.e., only working, only living, and both working and living) at the land-parcel level are obtained.
We demonstrate that the multiscale random sampling method can reduce the influence of image noise, increase the utilization of training data, and reduce network overfitting.
By altering the network structure and the training strategy, BagNet achieved excellent fitting accuracy for identifying each jobs-housing type (overall accuracy > 0.84 and kappa > 0.8).
For the first time, we demonstrate that urban socioeconomic characteristics can be obtained from high-resolution RS images using deep learning techniques.
Additionally, we conclude that the total level of mixing within Wuhan is not high at present; however, Wuhan is continuously improving the mixture of jobs and housing.
This study has reference value for extracting urban socioeconomic characteristics from RS images and could be used in urban planning as well as government management.
American Psychological Association (APA)
Yao, Yao& Qian, Chen& Hong, Ye& Guan, Qingfeng& Chen, Jingmin& Dai, Liangyang…[et al.]. 2020. Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1144029
Modern Language Association (MLA)
Yao, Yao…[et al.]. Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1144029
American Medical Association (AMA)
Yao, Yao& Qian, Chen& Hong, Ye& Guan, Qingfeng& Chen, Jingmin& Dai, Liangyang…[et al.]. Delineating Mixed Urban “Jobs-Housing” Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1144029
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144029