A Comprehensive Analysis of Soil Erosion in Coastal Areas Based on an Unmanned Aerial Vehicle and Deep Learning Approach

Soil is an important nonrenewable resource. Soil erosion is increasingly severe, and the accurate identification of soil erosion is crucial for ecological sustainability. In recent years, advancements in artificial intelligence have significantly contributed to the development of precise modeling technologies. This study utilizes high-resolution multispectral images captured by unmanned aerial vehicles and applies five machine learning models, namely convolutional neural network (CNN), support vector classification, random forest, extreme gradient boosting, and fully connected neural network, to identify regional soil erosion. The performance of each model is evaluated using F1-score, precision, and recall measurements. The results show that all models exhibit strong recognition capabilities, with CNN outperforming the others in both training and testing phases. Specifically, CNN achieved a recall rate of 0.99 on the training set and an F1-score of 0.98. Given the black-box nature of machine learning models, the shapley additive explanations method is further used for interpreting model outputs. The analysis reveals that the normalized difference salinity index and soil erodibility factor are the primary factors influencing soil erosion in the study area.