Deep Learning–Based Soil Classification from Sentinel-2 Multispectral Data

The research presented in this study addresses the subject of large-scale soil type classification. It is based on multispectral data from the Sentinel-2 satellite along with recent advances in deep learning for tabular data analysis. Initially we created a soil dataset aligned with the World Reference Base (WRB) classification system. This dataset was created by integrating Sentinel-2 spectral bands with different indices regarding vegetation, exposed soil conditions, mineralogical composition, and moisture dynamics. The study assesses the performance of different classification models, and some hybrid approaches using ensemble learning techniques. We applied and assessed several techniques for data balancing and augmentation to address the uneven class distribution that often exists in soil datasets. The results show that combining multispectral satellite features with specific spectral indices and various learning methods offers an effective and scalable way to generate WRB-consistent soil maps from Sentinel-2 data.

Acknowledgment:

This work is supported by the project "Romanian Hub for Artificial Intelligence-HRIA", Smart Growth, Digitization and Financial Instruments Program, MySMIS no. 351416.