Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Delineating Mixed Urban "jobs-Housing" Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery

Yao, Yao ; Qian, Chen ; Hong, Ye LU orcid ; Guan, Qingfeng ; Chen, Jingmin ; Dai, Liangyang ; Jiang, Zhangwei and Liang, Xun (2020) In Complexity 2020.
Abstract

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... (More)

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.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
in
Complexity
volume
2020
article number
8018629
publisher
John Wiley & Sons Inc.
external identifiers
  • scopus:85084183736
ISSN
1076-2787
DOI
10.1155/2020/8018629
language
English
LU publication?
no
additional info
Publisher Copyright: © 2020 Yao Yao et al.
id
228df066-731b-4fce-bb46-1e1468cf8867
date added to LUP
2026-06-08 19:18:26
date last changed
2026-06-11 03:36:47
@article{228df066-731b-4fce-bb46-1e1468cf8867,
  abstract     = {{<p>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 &gt; 0.84 and kappa &gt; 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.</p>}},
  author       = {{Yao, Yao and Qian, Chen and Hong, Ye and Guan, Qingfeng and Chen, Jingmin and Dai, Liangyang and Jiang, Zhangwei and Liang, Xun}},
  issn         = {{1076-2787}},
  language     = {{eng}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Complexity}},
  title        = {{Delineating Mixed Urban "jobs-Housing" Patterns at a Fine Scale by Using High Spatial Resolution Remote-Sensing Imagery}},
  url          = {{http://dx.doi.org/10.1155/2020/8018629}},
  doi          = {{10.1155/2020/8018629}},
  volume       = {{2020}},
  year         = {{2020}},
}