Cultivated lands in Northeast China exhibit extensive spatial distribution with pronounced regional heterogeneity in both soil types and cropping patterns. Although exhibiting gentle slopes overall, certain regions are characterized by prolonged slope lengths coupled with frequent freeze-thaw cycles and pronounced wind/water erosion, collectively enhancing spatiotemporal heterogeneity in surface vegetation cover and soil properties. These natural processes significantly increase spectral confusion between cultivated and noncultivated areas in remote sensing imagery, challenging conventional pixelbased or shallowfeature classification approaches in both accuracy and computational efficiency. Such limitations fundamentally constrain their applicability for highaccuracy cultivated land mapping. This study focuses on Youyi County, Shuangyashan City, using Planet remote sensing imagery and an enhanced RC-UNet semantic segmentation model for farmland parcel extraction. During model training, the U-Net backbone was replaced with ResNetto alleviate gradient vanishing and improve information transfer. Additionally, a hybrid channelspatial attention mechanism was incorporated to refine local details and boost segmentation accuracy. Results demonstrate that the improved RC-UNet model significantly enhances farmland boundary delineation, achieving 96.87% accuracy—a 3.09% increase over the standard UNet. Compared to SwinUNet, TransUNet, Unet++, DeepLabV3+, PSPNet, SVM, and RF models, it outperforms them by 1.65%, 2.62%, 2.28%, 3.13%, 5.44%, 6.55%, and 6.2%, respectively. The enhanced RC-UNet model demonstrates superior feature extraction capabilities, achieving an Intersection over Union (IoU) of > 90% in field boundary delineation. This advancement enables precise agricultural monitoring with < 3% area estimation error, which is critical for ensuring regional food security and promoting sustainable intensification practices.
Han et al. (2026) studied this question.