Envisioning global urban development with satellite imagery and generative AI
For urban planners and researchers, this work provides a generative tool for envisioning and planning sustainable urban development at a global scale, though it is an incremental application of existing generative AI methods to a new domain.
This study develops a multimodal generative AI framework that generates high-fidelity urban satellite imagery for the 500 largest metropolitan areas worldwide, enabling users to specify urban development goals and generate diverse scenarios. The framework also encodes latent representations of urban form that enhance downstream tasks like carbon emission prediction, with human expert evaluation confirming generated images are comparable to real ones.
Urban development has been a defining force in human history, shaping cities for centuries. However, past studies mostly analyze such development as predictive tasks, failing to reflect its generative nature. Therefore, this study designs a multimodal generative AI framework to envision sustainable urban development at a global scale. By integrating prompts and geospatial controls, our framework can generate high-fidelity, diverse, and realistic urban satellite imagery across the 500 largest metropolitan areas worldwide. It enables users to specify urban development goals, creating new images that align with them while offering diverse scenarios whose appearance can be controlled with text prompts and geospatial constraints. It also facilitates urban redevelopment practices by learning from the surrounding environment. Beyond visual synthesis, we find that it encodes and interprets latent representations of urban form for global cross-city learning, successfully transferring styles of urban environments across a global spatial network. The latent representations can also enhance downstream prediction tasks such as carbon emission prediction. Further, human expert evaluation confirms that our generated urban images are comparable to real urban images. Overall, this study presents innovative approaches for accelerated urban planning and supports scenario-based planning processes for worldwide cities.