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

Han Li, Sheng Miao, Yansu Qi, Huiwen Gao, Haoyan Duan, Chao Liu () and Weijun Gao
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Han Li: School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
Sheng Miao: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China
Yansu Qi: College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266520, China
Huiwen Gao: School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China
Haoyan Duan: College of Architecture and Urban Planning, Qingdao University of Technology, Qingdao 266520, China
Chao Liu: School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
Weijun Gao: Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan

Sustainability, 2025, vol. 17, issue 3, 1-15

Abstract: 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.

Keywords: soil erosion; multispectral remote sensing; convolutional neural networks; SHapley Additive exPlanation; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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