ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing

[2507.04678] ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2507.04678 (cs) [Submitted on 7 Jul 2025 ( v1 ), last revised 9 Apr 2026 (this version, v3)] Title: ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing Authors: Zhenghui Zhao , Chen Wu , Xiangyong Cao , Di Wang , Hongruixuan Chen , Datao Tang , Liangpei Zhang , Zhuo Zheng View a PDF of the paper titled ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing, by Zhenghui Zhao and 7 other authors View PDF HTML (experimental) 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, assigning different drift magnitudes to event and temporal evolution. This enables differentiated generation during the pre-to-post transition. c) Drift-Aware Denoising, which embeds the drift map into the denoising network, guiding drift-aware reconstruction. Experiments show that ChangeBridge can generate better cross-spatiotemporal aligned scenarios compared to state-of-the-art methods. Additionally, ChangeBridge shows great potential for land-use planning and as a data generation engine for a series of change detection tasks. Code is available at this https URL Comments: Accepted by CVPR 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2507.04678 [cs.CV] (or arXiv:2507.04678v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2507.04678 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhenghui Zhao [ view email ] [v1] Mon, 7 Jul 2025 05:51:55 UTC (2,042 KB) [v2] Fri, 12 Dec 2025 09:29:13 UTC (2,076 KB) [v3] Thu, 9 Apr 2026 07:56:37 UTC (1,956 KB) Full-text links: Access Paper: View a PDF of the paper titled ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing, by Zhenghui Zhao and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-07 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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