Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Hong_2021_submission.pdf (4.8 MB)

ETD Abstract Container

Abstract Header

A Convolutional Neural Network for Detecting and Mapping Built Environment at Neighborhood Scale

Hong, Xin
http://rave.ohiolink.edu/etdc/view?acc_num=kent1626962900618429

Abstract Details

2021, PHD, Kent State University, College of Arts and Sciences / Department of Geography.
The increasing interest in the connection between built environment and health has encouraged the development of new tools for describing health-related built environment. With the development of deep learning, scholars have been exploring the applications of convolutional neural networks (CNNs) to the field of remote sensing. Nevertheless, applying deep learning in remote sensing is still a young field. Instead of treating deep learning as a “black-box” technology, this study embraced deep learning as the key to solving large-scale and high-resolution remote sensing scenes. This study applied U-net, an encoder-decoder CNN architecture, for detecting greenness at street level. A new operational definition of the concept of neighborhoods: sidewalk-homogenous neighborhoods, which corresponds to different economic levels and habits of using sidewalks, was also proposed as a novel and practical delineation of neighborhood boundaries. As a pilot study, this study tested that deep learning is a sufficient method for detecting built environment on high volume unmanned aerial vehicle (UAV) images. The sidewalk-homogenous neighborhoods is a reasonable spatial scale that can help to reveal the disparities in sidewalk environments between neighborhoods.
Scott Sheridan (Committee Chair)
James Tyner (Committee Member)
Timothy Assal (Committee Member)
Eric Jefferis (Committee Member)
107 p.
Geographic Information Science; Geography; Health
health geography; deep learning; UAV images; sidewalks

Recommended Citations

Citations

  • Hong, X. (2021). A Convolutional Neural Network for Detecting and Mapping Built Environment at Neighborhood Scale [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1626962900618429

    APA Style (7th edition)

  • Hong, Xin. A Convolutional Neural Network for Detecting and Mapping Built Environment at Neighborhood Scale. 2021. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1626962900618429.

    MLA Style (8th edition)

  • Hong, Xin. "A Convolutional Neural Network for Detecting and Mapping Built Environment at Neighborhood Scale." Doctoral dissertation, Kent State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=kent1626962900618429

    Chicago Manual of Style (17th edition)

kent1626962900618429
344
© 2021, all rights reserved.
This open access ETD is published by Kent State University and OhioLINK.