  This study introduces a novel Remote Sensing (RS) Urban Prediction (UP) task
focused on future urban planning, which aims to forecast urban layouts by
utilizing information from existing urban layouts and planned change maps. To
address the proposed RS UP task, we propose UP-Diff, which leverages a Latent
Diffusion Model (LDM) to capture positionaware embeddings of pre-change urban
layouts and planned change maps. In specific, the trainable cross-attention
layers within UP-Diff's iterative diffusion modules enable the model to
dynamically highlight crucial regions for targeted modifications. By utilizing
our UP-Diff, designers can effectively refine and adjust future urban city
plans by making modifications to the change maps in a dynamic and adaptive
manner. Compared with conventional RS Change Detection (CD) methods, the
proposed UP-Diff for the RS UP task avoids the requirement of paired prechange
and post-change images, which enhances the practical usage in city development.
Experimental results on LEVIRCD and SYSU-CD datasets show UP-Diff's ability to
accurately predict future urban layouts with high fidelity, demonstrating its
potential for urban planning. Code and model weights are available at
https://github.com/zeyuwang-zju/UP-Diff.
