Urban Region Profiling

Urban region profiling aims to create detailed, low-dimensional representations of urban areas, capturing diverse characteristics like demographics and economic activity to aid urban planning and development. Recent research heavily emphasizes multi-modal learning, integrating data from satellite imagery, street views, Points of Interest (POIs), and textual descriptions using techniques like contrastive learning, vision-language pretraining, and graph neural networks to improve prediction accuracy and interpretability. These advancements are crucial for creating more effective and equitable urban policies by providing nuanced insights into urban dynamics and addressing data scarcity challenges through techniques like federated learning and self-supervised learning.