Diffusion Transformers as Open-World Spatiotemporal Foundation Models

Understand

The urban environment is characterized by complex spatio-temporal dynamics arising from diverse human activities and interactions.

• Effectively modeling these dynamics is essential for understanding and optimizing urban systems.
• In this work, we introduce UrbanDiT, a foundation model for open-world urban spatio-temporal learning that successfully scales up diffusion transformers in this field.
• UrbanDiT pioneers a unified model that integrates diverse data sources and types while learning universal spatio-temporal patterns across different cities and scenarios.