UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction

Zeyu Wang, Zecheng Hao, Yuhan Zhang, Yuchao Feng, Yufei Guo

Abstract: Remote sensing (RS) technology has become essential for monitoring urban development, including applications like population growth analysis, transportation congestion forecasting, and climate change detection (CD). However, its potential for future urban planning (UP), particularly in predicting future urban layouts remains largely unexplored. This study introduces UP-Diff, a novel method leveraging generative models for UP, to address this gap. UP-Diff leverages information from current urban layouts and planned change maps to predict future urban configurations. Key challenges addressed include the integration of urban layouts and change maps into latent diffusion model (LDM) through careful architecture improvements and the mitigation of limited training data by employing a pretrained stable diffusion (SD) model with fixed weights, trainable ConvNeXt, and trainable cross-attention layers. Our method significantly streamlines the UP process by automating layout predictions, thus reducing the time and effort required compared to traditional manual methods. Comprehensive evaluations on the learning, vision, and RS dataset (LEVIR-CD) and Sun Yat-Sen University dataset (SYSU-CD) validate that UP-Diff achieves high-fidelity predictions of future urban layouts, demonstrating its effectiveness and potential for advancing RS-based UP methodologies. Our code and model weights are available at https://github.com/zeyuwang-zju/UP-Diff.