Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps

The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognit...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of geographical information science : IJGIS Ročník 35; číslo 3; s. 490 - 512
Hlavní autoři: Yan, Xiongfeng, Ai, Tinghua, Yang, Min, Tong, Xiaohua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 04.03.2021
Taylor & Francis LLC
Témata:
Buildings
Coding
Cognition
Convolution
deep learning
Feature extraction
graph convolutional autoencoder (GCAE)
Representations
Shape coding
Shape recognition
spatial cognition
Unsupervised learning
vector building data
ISSN:1365-8816, 1362-3087, 1365-8824
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2020.1768260