EGU21-13906, updated on 04 Mar 2021
https://doi.org/10.5194/egusphere-egu21-13906
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using Deep Machine Learning to Understand Green Space Popularity, Activities, Functionality, and Design Implications: a comparison of Chicago and Stockholm

Si Chen1 and Brian Deal2
Si Chen and Brian Deal
  • 1Department of Information Science, University of Illinois at Urbana-Champaign, Champaign, USA (sichen12@illinois.edu)
  • 2Department of Landscape Architecture, University of Illinois at Urbana-Champaign, Champaign, USA (deal@illinois.edu)

How to cite: Chen, S. and Deal, B.: Using Deep Machine Learning to Understand Green Space Popularity, Activities, Functionality, and Design Implications: a comparison of Chicago and Stockholm, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13906, https://doi.org/10.5194/egusphere-egu21-13906, 2021.

This abstract has been withdrawn on 27 Apr 2021.