【摘要】 基于水土流失估算模型的土壤侵蚀评价研究需要高质量的输入数据，其中包括土地利用分类数据，然而传统的土地利用分类方法在面对多类别分类任务时存在效率较低以及“异物同谱”现象显著的问题。为此，本研究尝试将深度学习应用于土壤侵蚀评价中，采用以Swin Transformer为主干网络的语义分割模型实现高精度土地利用分类，并将分类结果应用于RUSLE模型对土壤侵蚀程度进行评价。经验证，语义分割模型的总体精度达到95%，且具有良好的泛化性能。土壤侵蚀评价结果表明鄱阳县土壤侵蚀以轻度侵蚀为主，空间上呈现带状和点状的分布特征。结果表明，基于深度学习的土地利用分类在土壤侵蚀评价领域具有较广泛的应用前景。更多还原

【Abstract】 Soil erosion evaluation based on soil erosion estimation model requires high-quality input data, including land use classification data, however, the traditional land use classification method has the problems of low efficiency and the phenomenon of “ heterogeneity and homogeneity” when facing the task of multi-class classification. Therefore, this study tries to apply deep learning to soil erosion evaluation, adopts the semantic segmentation model with Swin Transformer as the backbone network to realize high-precision land use classification, and applies the classification results to the RUSLE model to evaluate the degree of soil erosion. It is verified that the overall accuracy of the semantic segmentation model reaches 95% and has good generalization performance. The results of soil erosion evaluation show that soil erosion in Poyang County is dominated by light erosion, and spatially presents the distribution characteristics of band and point. The results show that land use classification based on deep learning has a wider application prospect in the field of soil erosion evaluation.更多还原