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The severe soil erosion on the Loess Plateau is the root of the Yellow River problem, and the mathematical model of soil erosion is the core of intelligent soil and water conservation. How to extract soil and water conservation measures accurately, quickly, and quantitatively is of great significance for developing the parameterization method of soil and water conservation measures in the soil erosion model and quantitatively revealing the influence of soil and water conservation measures allocation on runoff and sediment yield process. It is the direct demand to support the research and development of distributed soil erosion models, soil erosion prediction, and digital twin watershed construction. Based on analyzing the current high-resolution remote sensing image data and deeply learning the development frontier of artificial intelligence, this paper studies and explores the technology and method of intelligent extraction of terrace measures underpinned by an artificial neural network algorithm model under deep learning. The main research results are as follows: (1) The current deep learning theory development and model structure are deeply studied. (2) The intelligent extraction algorithm of high-resolution remote sensing images based on depth learning is developed. (3) Accuracy analysis of the model results are carried out. The research results of the project will provide basic data for regional soil erosion prevention and control, and at the same time, provide technical support for high-precision parameter acquisition and calibration of distributed soil erosion model, which has important theoretical practice and application value.
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