[2603.26831] Envisioning global urban development with satellite imagery and generative AI

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.26831 (cs) [Submitted on 27 Mar 2026] Title:Envisioning global urban development with satellite imagery and generative AI Authors:Kailai Sun, Yuebing Liang, Mingyi He, Yunhan Zheng, Alok Prakash, Shenhao Wang, Jinhua Zhao, Alex "Sandy'' Pentland View a PDF of the paper titled Envisioning global urban development with satellite imagery and generative AI, by Kailai Sun and 7 other authors View PDF HTML (experimental) Abstract: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...