Urban Region Representation Learning

Urban region representation learning aims to create numerical representations (embeddings) of urban areas that capture their complex characteristics from diverse data sources, such as mobility patterns, points of interest, and satellite imagery. Current research emphasizes integrating multiple data types using advanced techniques like contrastive learning, graph neural networks (particularly heterogeneous graph attention networks), and attentive fusion mechanisms to generate more robust and informative embeddings. These improved representations are crucial for various downstream tasks, including predicting commuting flows, land use, socioeconomic indicators, and facilitating more effective urban planning and resource management.