UniRT: A Unified Framework for Time-Series Remote Sensing Image Reconstruction and Change Detection

Remote sensing time series monitoring plays a vital role in capturing the dynamic evolution of the Earth’s surface. Recent deep learning based temporal change detection (TCD) methods have achieved remarkable progress under cloud-free optical image sequences. However, optical imagery is frequently affected by clouds and cloud shadows, resulting in pervasive and irregular data gaps that disrupt temporal continuity and sampling regularity. Consequently, current TCD approaches struggle to cope with highly dynamic surfaces and long-term or irregularly missing observations, often leading to inaccurate change detection results. To address these challenges, we propose UniRT, a unified framework that jointly performs time-series reconstruction and change detection, enabling robust monitoring from image sequences with missing observations. Specifically, a temporal-adaptive module is seamlessly embedded into a spatiotemporal learning framework while maintaining a lightweight architectural design. In addition, a time-aware decoder is introduced to better capture temporal dependencies and enhance robustness and generalization capability under irregular sampling conditions. Extensive experiments conducted on DynamicEarthNet and SpaceNet7 demonstrate that UniRT consistently outperforms state-of-the-art methods in temporal change detection, particularly in challenging scenarios characterized by severe data gaps and highly dynamic surface changes.