Generating Any Changes in the Noise Domain

Change detection is essential in Earth observation, yet current models heavily rely on large-scale annotated datasets. Generative models offer a promising alternative by synthesizing training data, but generating temporally coherent image pairs with realistic, semantically meaningful changes remains a significant challenge. Existing approaches typically simulate changes by generating pre- and post-change label maps using either heuristic rules (e.g., copy-pasting) or text prompts. However, the former offers limited change diversity, while the latter often fails to maintain spatial consistency between image pairs. We observe that the noise space of diffusion models encodes strong generative capacity and spatial controllability: localized perturbations in the noise can yield meaningful, interpretable changes in corresponding image regions. Motivated by this, we propose Noise2Change, a framework for simulating change directly in the noise domain. The key idea is to manipulate the semantic composition of the initial noise sampled from the noise domain, such that the diffusion process generates structurally consistent pre- and post-change images reflecting realistic transformations. Since the unperturbed noise is shared between both images, the resulting pairs exhibit strong temporal alignment and semantic coherence, effectively addressing the trade-off between realism and consistency. Concretely, we employ a discrete diffusion model to extract high-level semantics from the initial noise. Guided by these semantics, we introduce a change simulation strategy that optimizes the noise to encode intended changes. The modified noise is then used to drive the diffusion process, yielding pre- and post-change label maps with natural structural transitions. These maps are passed through a unified framework for image generation and label refinement, producing highly aligned image-label pairs. Our framework supports diverse change types across a wide range of scenarios. Extensive experiments on multiple change detection tasks demonstrate that our method achieves superior performance compared to existing generative approaches.