Cross City

Cross-city research focuses on developing methods to analyze and model urban environments across different geographical locations, overcoming the limitations of single-city studies. Current efforts concentrate on creating robust and generalizable models using techniques like domain adaptation, meta-learning, and generative adversarial networks, often incorporating multimodal data sources such as satellite imagery, point clouds, and sensor data. These advancements enable improved urban planning, resource allocation, and prediction of phenomena like traffic flow and disease spread, ultimately contributing to more efficient and resilient cities. The development of large-scale, publicly available datasets is also a key focus, facilitating broader collaboration and accelerating progress in the field.