Arable land is fundamental to agricultural production and a crucial component of ecosystems. However, its complex texture and distribution in remote sensing images make it susceptible to interference from other land cover types, such as water bodies, roads, and buildings, complicating accurate identification. Building on previous research, this study proposes an efficient and lightweight CNN-based network, U-MGA, to address the challenges of feature similarity between arable and non-arable areas, insufficient fine-grained feature extraction, and the underutilization of multi-scale information. Specifically, a Multi-Scale Adaptive Segmentation (MSAS) is designed during the feature extraction phase to provide multi-scale and multi-feature information, supporting the model’s feature reconstruction stage. In the reconstruction phase, the introduction of the Multi-Scale Contextual Module (MCM) and Group Aggregation Bridge (GAB) significantly enhances the efficiency and accuracy of multi-scale and fine-grained feature utilization. The experiments conducted on an arable land dataset based on GF-2 imagery and a publicly available dataset show that U-MGA outperforms mainstream networks (Unet, A2FPN, Segformer, FTUnetformer, DCSwin, and TransUnet) across six evaluation metrics (Overall Accuracy (OA), Precision, Recall, F1-score, Intersection-over-Union (IoU), and Kappa coefficient). Thus, this study provides an efficient and precise solution for the arable land recognition task, which is of significant importance for agricultural resource monitoring and ecological environmental protection.

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.