Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

LarsH

Lars Harrie

Professor

LarsH

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

Författare

  • 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.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • Lantmäteri (CI)
  • eSSENCE: The e-Science Collaboration
  • Centrum för geografiska informationssystem (GIS-centrum)

Publiceringsår

2023

Språk

Engelska

Sidor

1828-1852

Publikation/Tidskrift/Serie

International Journal of Digital Earth

Volym

16

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

Taylor & Francis

Ämne

  • Other Earth and Related Environmental Sciences
  • Physical Geography

Nyckelord

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

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1753-8947