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LarsHarrie

Lars Harrie

Professor

LarsHarrie

A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation

Author

  • Huafei Yu
  • Tinghua Ai
  • Min Yang
  • Weiming Huang
  • Lars Harrie

Summary, in English

Similarity measurement has been a prevailing research topic in geographic information science. Geometric similarity measurement in scaling transformation (GSM_ST) is critical to ensure spatial data quality while balancing detailed information with distinctive features. However, GSM_ST is an uncertain problem due to subjective spatial cognition, global and local concerns, and geometric complexity. Traditional rule-based methods considering multiple consistent conditions require subjective adjustments to characteristics and weights, leading to poor robustness in addressing GSM_ST. This study proposes an unsupervised representation learning framework for automated GSM_ST, using a Graph Autoencoder Network (GAE) and drainage networks as an example. The framework involves constructing a drainage graph, designing the GAE architecture for GSM_ST, and using Cosine similarity to measure similarity based on the GAE-derived drainage embeddings in different scales. We perform extensive experiments and compare methods across 71 drainage networks during five scaling transformations. The results show that the proposed GAE method outperforms other methods with a satisfaction ratio of around 88% and has strong robustness. Moreover, our proposed method also can be applied to other scenarios, such as measuring similarity between geographical entities at different times and data from different datasets.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • Surveying (M.Sc.Eng.)
  • eSSENCE: The e-Science Collaboration
  • Centre for Geographical Information Systems (GIS Centre)

Publishing year

2023

Language

English

Pages

1828-1852

Publication/Series

International Journal of Digital Earth

Volume

16

Issue

1

Document type

Journal article

Publisher

Taylor & Francis

Topic

  • Other Earth and Related Environmental Sciences
  • Physical Geography

Keywords

  • drainage network
  • Geometric similarity measurement
  • graph autoencoder network
  • scaling transformation

Status

Published

ISBN/ISSN/Other

  • ISSN: 1753-8947