Abstract

Soil mapping in three dimensions (3D) is critical for understanding soil variability and supporting precision agriculture, environmental modeling, and land management. Deep learning (DL) techniques, leveraging convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offer powerful tools for processing complex geospatial data to generate high-resolution 3D soil maps. This article explores the application of DL in 3D soil mapping, focusing on its ability to integrate diverse data sources, such as remote sensing, geophysical surveys, and soil samples. We present a case study using a CNN-based model to predict soil properties (e.g., organic carbon, texture, pH) across a 100 km² agricultural region. Results demonstrate that DL models outperform traditional interpolation methods in accuracy and resolution. Challenges, including data scarcity and computational demands, are discussed, alongside future directions for improving 3D soil mapping with DL.