UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction
This addresses urban planning challenges by enabling dynamic adjustments to city plans without requiring paired pre- and post-change images, though it is incremental as it adapts existing diffusion models to a new domain-specific task.
This paper tackles the problem of forecasting future urban layouts for urban planning by introducing a Remote Sensing Urban Prediction task and proposes UP-Diff, a Latent Diffusion Model that predicts layouts from existing layouts and planned change maps, achieving high-fidelity predictions on LEVIRCD and SYSU-CD datasets.
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.