TL;DR
ChangeBridge is a novel spatiotemporal image generation model for remote sensing that uses a drift-asynchronous diffusion bridge to generate coherent future scenes conditioned on pre-event images and multimodal controls.
Contribution
It introduces a new diffusion-based framework with three modules for modeling cross-temporal variations in remote sensing images, surpassing existing event-driven change methods.
Findings
Outperforms state-of-the-art methods in generating aligned spatiotemporal scenes.
Demonstrates potential for land-use planning and change detection tasks.
Shows high coherence in generated post-event scenes.
Abstract
Spatiotemporal image generation is a highly meaningful task, which can generate future scenes conditioned on given observations. However, existing change generation methods can only handle event-driven changes (e.g., new buildings) and fail to model cross-temporal variations (e.g., seasonal shifts). In this work, we propose ChangeBridge, a conditional spatiotemporal image generation model for remote sensing. Given pre-event images and multimodal event controls, ChangeBridge generates post-event scenes that are both spatially and temporally coherent. The core idea is a drift-asynchronous diffusion bridge. Specifically, it consists of three main modules: a) Composed Bridge Initialization, which replaces noise initialization. It starts the diffusion from a composed pre-event state, modeling a diffusion bridge process. b) Asynchronous Drift Diffusion, which uses a pixel-wise drift map,…
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