A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation
Saved in:
| Title: | A graph autoencoder network to measure the geometric similarity of drainage networks in scaling transformation |
|---|---|
| Authors: | Yu, Huafei, Ai, Tinghua, Yang, Min, Huang, Weiming, Harrie, Lars |
| Contributors: | Lund University, Faculty of Science, Dept of Physical Geography and Ecosystem Science, Lunds universitet, Naturvetenskapliga fakulteten, Institutionen för naturgeografi och ekosystemvetenskap, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator, Lund University, Faculty of Science, Dept of Physical Geography and Ecosystem Science, Centre for Geographical Information Systems (GIS Centre), Lunds universitet, Naturvetenskapliga fakulteten, Institutionen för naturgeografi och ekosystemvetenskap, Centrum för geografiska informationssystem (GIS-centrum), Originator |
| Source: | International Journal of Digital Earth. 16(1):1828-1852 |
| Subject Terms: | Natural Sciences, Earth and Related Environmental Sciences, Other Earth Sciences (including Geographical Information Science), Naturvetenskap, Geovetenskap och relaterad miljövetenskap, Annan geovetenskap (Här ingår: Geografisk informationsvetenskap), Physical Geography, Naturgeografi |
| Description: | 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. |
| Access URL: | https://doi.org/10.1080/17538947.2023.2212920 |
| Database: | SwePub |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | 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. |
|---|---|
| ISSN: | 17538947 17538955 |
| DOI: | 10.1080/17538947.2023.2212920 |