Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image

Published in ISPRS Journal of Photogrammetry and Remote Sensing, 2023

This paper presents a novel approach to identify urban functions using a two-layer spatially dependent graph neural network with time-series street view images. The research was published in ISPRS Journal of Photogrammetry and Remote Sensing, a top-tier journal in the field (SCI, Q1, IF=11.774).

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Key Points:

  • Developed a two-layer spatially dependent graph neural network
  • Utilized time-series street view images for urban function identification
  • Published in a high-impact journal (IF=11.774)
  • Contributes to the field of urban science and remote sensing

Additional Information:

  • This research is part of our ongoing work in urban function analysis and GeoAI
  • The methodology can be applied to various urban planning and management scenarios
  • Future work will focus on integrating this approach with other urban data sources

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Read more about our urban research (zh)

Zhang, Y., Liu, P., & Biljecki, F. (2023). Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 153-168. (ESI 3%)

Recommended citation: Zhang Y, Liu P, Biljecki F. (2023). "Knowledge and topology: A two layer spatially dependent graph neural networks to identify urban functions with time-series street view image" ISPRS Journal of Photogrammetry and Remote Sensing
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