3 papers
cs.LG2026
From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy
Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers +1
Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties. Yet, translating high-dimensional, highly collin…
cs.LG2025
Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers +1
In the field of pedometrics, tabular machine learning is the predominant method for soil property prediction from remote and proximal soil sensing data, forming a central component…
cs.LG2024
An Efficient Model-Agnostic Approach for Uncertainty Estimation in Data-Restricted Pedometric Applications
Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers +1
This paper introduces a model-agnostic approach designed to enhance uncertainty estimation in the predictive modeling of soil properties, a crucial factor for advancing pedometrics…