FarmChanger: A Diffusion-Guided Network for Farmland Change Detection

Cultivated land is a vital resource that underpins human survival and sustainable social development. With the widespread use of high-resolution remote sensing imagery, conventional change detection methods often suffer from limited accuracy due to pseudo-changes and insufficient feature representation when dealing with complex land structures and significant seasonal variations. To address the challenges of representing multi-scale structures, mitigating pseudo-change interference, and accurately delineating boundaries in cultivated land change detection, this study proposes a diffusion-guided change detection network—FarmChanger. The network is designed based on the principles of adaptive feature extraction and diffusion-inspired feature refinement. These components are further integrated through cross-feature guidance to enhance spatial details, forming an end-to-end detection framework. Comprehensive evaluations on the CLCD and Peixian benchmark datasets demonstrate that FarmChanger achieves comparable or superior performance to mainstream models across multiple evaluation metrics, verifying its high accuracy and robustness in cultivated land dynamic monitoring tasks.