17 citations
- Osnabrück UniversityDE3 papers
- German Research Centre for Artificial IntelligenceDE2 papers
- Hochschule OsnabrückDE2 papers
- AgroParisTechFR1 paper
- AgroscopeCH1 paper
- Australia Telescope National FacilityAU1 paper
- Bielefeld UniversityDE1 paper
- Centre National de la Recherche ScientifiqueFR1 paper
- CITIC Group (China)CN1 paper
- Commonwealth Scientific and Industrial Research OrganisationAU1 paper
- Eberswalde University for Sustainable DevelopmentDE1 paper
- Institut Agro Montpelier1 paper
6 papers
Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Jonas Schmidinger, Viacheslav Barkov, Sebastian Vogel +2
Machine learning and geostatistics are two fundamentally different frameworks for predicting and spatially mapping soil properties. Geostatistics leverages the spatial structure of…
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…
Evaluate with the Inverse: Efficient Approximation of Latent Explanation Quality Distribution
Carlos Eiras-Franco, Anna Hedström, Marina M. -C. Höhne
Obtaining high-quality explanations of a model's output enables developers to identify and correct biases, align the system's behavior with human values, and ensure ethical complia…
LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping
J. Schmidinger, S. Vogel, V. Barkov +33
Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking s…
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…
Exploring the time variability of the Solar Wind using LOFAR pulsar data
S. C. Susarla, A. Chalumeau, C. Tiburzi +22
High-precision pulsar timing is highly dependent on precise and accurate modeling of any effects that impact the data. It was shown that commonly used Solar Wind models do not accu…