Intelligent Extraction of Terracing Using the ASPP ArrU-Net Deep Learning Model for Soil and Water Conservation on the Loess Plateau

The prevention and control of soil erosion through water conservation measures is crucial. It imperative to accurately quickly extract information on these in order understand how their configuration affects the runoff sediment yield process. In this investigation, intelligent interpretation algorithms deep learning semantic segmentation models pertinent remote sensing imagery were examined scrutinized. Our objective was enhance accuracy automation by employing an advanced learning-based model for astute high-resolution images. Subsequently, algorithm tailored developed terracing imagery. Focusing Fenxi County Shanxi Province as experimental target, research we conducted a comparative analysis between our proposed alternative models. outcomes demonstrated that refined exhibited superior precision. Additionally, assessed model’s generalization capability utilizing Wafangdian City Liaoning another target performed with human interpretation. findings revealed possesses enhanced ability can substantially augment efficiency.