2 papers
cs.LG2026
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…
cs.LG2025
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…