Proposal for a Soil Erosion Prediction System in Peruvian Territory Using Satellite Imagery

Cuadros-Huachua A.; Calderón-Niquin M. · Proceedings of the 2025 IEEE 32nd International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2025 (2025) · DOI: 10.1109/intercon67304.2025.11244646

Resumen

Soil erosion threatens agricultural and ecosystem stability in Peru, creating the need for efficient and scalable assessment tools beyond traditional monitoring. This research details the development of a soil erosion prediction system for the Piura region by integrating remote sensing, the RUSLE model, and deep learning. Our methodology involved two steps: first, building a multi-temporal geospatial database (2015-2024) on Google Earth Engine with 16 variables from Landsat 8, Sentinel-1, and MODIS. Second, developing two convolutional neural network architectures to predict the RUSLE A-factor. AU-Net model was used for direct spatial prediction, while a ConvL-STM U-Net model was designed for spatio-temporal forecasting. Evaluation in a test data set confirmed the high performance of both models. The U-Net accurately predicted the spatial distribution of erosion (R2=0.9895, MSE=0.0006, MAE=0.0021), and the ConvLSTM U-Net successfully forecasted its evolution (R2=0.7820, MSE=0.0036, MAE=0.0303). We conclude that this integrated system offers a powerful and scalable solution for erosion assessment. The ability to generate precise susceptibility maps and anticipate future behavior provides a valuable tool for land use planning and conservation strategies in vulnerable regions.

Instituciones: Universidad ESAN