LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

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 studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studie...

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Bibliographic Details
Main Authors:	Jonas Schmidinger, Sebastian Vogel, Viacheslav Barkov, Anh-Duy Pham, Robin Gebbers, Hamed Tavakoli, Jose Correa, Tiago R. Tavares, Patrick Filippi, Edward J. Jones, Vojtech Lukas, Eric Boenecke, Joerg Ruehlmann, Ingmar Schroeter, Eckart Kramer, Stefan Paetzold, Masakazu Kodaira, Alexandre M.J.-C. Wadoux, Luca Bragazza, Konrad Metzger, Jingyi Huang, Domingos S.M. Valente, Jose L. Safanelli, Eduardo L. Bottega, Ricardo S.D. Dalmolin, Csilla Farkas, Alexander Steiger, Taciara Z. Horst, Leonardo Ramirez-Lopez, Thomas Scholten, Felix Stumpf, Pablo Rosso, Marcelo M. Costa, Rodrigo S. Zandonadi, Johanna Wetterlind, Martin Atzmueller
Format:	Article
Language:	English
Published:	Elsevier 2025-07-01
Series:	Geoderma
Subjects:	Machine learning Benchmarking Open data Dataset collection Digital soil mapping Pedometrics
Online Access:	http://www.sciencedirect.com/science/article/pii/S0016706125001752
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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

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