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

TL;DR

This paper presents UP-Diff, a latent diffusion model that predicts future urban layouts from existing layouts and change maps, aiding urban planning without needing paired images.

Contribution

The paper introduces UP-Diff, a novel latent diffusion approach that captures position-aware embeddings for urban prediction, improving flexibility and accuracy over traditional change detection methods.

Findings

Accurately predicts future urban layouts on LEVIRCD and SYSU-CD datasets.

Avoids the need for paired pre-change and post-change images.

Demonstrates high fidelity in urban layout prediction.

Abstract

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.