UniTS: Unified Time Series Generative Model for Remote Sensing

• URL: http://arxiv.org/abs/2512.04461v1
• Date: Thu, 04 Dec 2025 05:07:35 GMT
• Title: UniTS: Unified Time Series Generative Model for Remote Sensing
• Authors: Yuxiang Zhang, Shunlin Liang, Wenyuan Li, Han Ma, Jianglei Xu, Yichuan Ma, Jiangwei Xie, Wei Li, Mengmeng Zhang, Ran Tao, Xiang-Gen Xia,
• Abstract summary: Unified Time Series Generative Model (UniTS) is a general framework applicable to various time series tasks.<n>UniTS constructs evolution path from noise to targets under the guidance of task-specific conditions.<n>It exhibits exceptional generative and cognitive capabilities in both low-level and high-level time series tasks.
• Score: 36.5844767315001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the primary objectives of satellite remote sensing is to capture the complex dynamics of the Earth environment, which encompasses tasks such as reconstructing continuous cloud-free time series images, detecting land cover changes, and forecasting future surface evolution. However, existing methods typically require specialized models tailored to different tasks, lacking unified modeling of spatiotemporal features across multiple time series tasks. In this paper, we propose a Unified Time Series Generative Model (UniTS), a general framework applicable to various time series tasks, including time series reconstruction, time series cloud removal, time series semantic change detection, and time series forecasting. Based on the flow matching generative paradigm, UniTS constructs a deterministic evolution path from noise to targets under the guidance of task-specific conditions, achieving unified modeling of spatiotemporal representations for multiple tasks. The UniTS architecture consists of a diffusion transformer with spatio-temporal blocks, where we design an Adaptive Condition Injector (ACor) to enhance the model's conditional perception of multimodal inputs, enabling high-quality controllable generation. Additionally, we design a Spatiotemporal-aware Modulator (STM) to improve the ability of spatio-temporal blocks to capture complex spatiotemporal dependencies. Furthermore, we construct two high-quality multimodal time series datasets, TS-S12 and TS-S12CR, filling the gap of benchmark datasets for time series cloud removal and forecasting tasks. Extensive experiments demonstrate that UniTS exhibits exceptional generative and cognitive capabilities in both low-level and high-level time series tasks. It significantly outperforms existing methods, particularly when facing challenges such as severe cloud contamination, modality absence, and forecasting phenological variations.

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