From buildings to blocks: Graph contrastive learning for integrated urban architectural morphology classification

2026/01/01 by Ziyu Liu, Linghao Wang, Hao Xu +3 · 1 voice
Engineering · Environmental Science · #3D Modeling in Geospatial Applications #Remote Sensing and LiDAR Applications #Urban Design and Spatial Analysis

paper · doi:10.1016/j.foar.2025.11.004

crossref issued 2026/01/01 · crossref published 2026/01/01 · crossref published-print 2026/01/01 · openalex publication_date 2026/01/01 · crossref created 2026/01/12 · openalex created_date 2026/01/14 · crossref deposited 2026/05/01 · crossref indexed 2026/05/04 · openalex updated_date 2026/06/11

Citations

• Global Building Morphology Indicators
• A graph-based neural network approach to integrate multi-source data for urban building function classification
• Predicting building characteristics at urban scale using graph neural networks and street-level context
• CGAP: Urban Region Representation Learning with Coarsened Graph Attention Pooling
• Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive
• Graph Contrastive Learning with Augmentations
• Graph Clustering with Graph Neural Networks
• PCAAE: Principal Component Analysis Autoencoder for organising the latent space of generative networks
• Machine Learning on Graphs: A Model and Comprehensive Taxonomy
• Graph Input Representations for Machine Learning Applications in Urban Network Analysis
• FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
• Self-Attention Graph Pooling
• Hierarchical Graph Representation Learning with Differentiable Pooling
• How block density and typology affect urban vitality: an exploratory analysis in Shenzhen, China
• Graph Attention Networks

Discussions

• Ziyu Liu, Linghao Wang, Hao Xu, Xiao Wang, Dongqing Han, Yacheng Song, From buildings to blocks: Graph contrastive learning for integrated urban architectural morphology classification, Frontiers of A [bsky, 0 points, 0 comments]