soilcnn - Convolutional Neural Networks for Digital Soil Mapping

A framework, built on 'torch', for mapping a soil property from point observations and a stack of aligned rasters with a convolutional network that reads the neighbourhood of each point. It extracts the patches; resamples by spatial blocks or by k-fold nearest neighbour distance matching, after Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>; tunes and selects under a noise floor and the one-standard-error rule; refits the selected configuration under several seeds; and predicts median, mean and interval maps over rasters of any extent. The map's centre is the ensemble median over seeds; the mean corrects its back-transformation by smearing, after Duan (1983) <doi:10.1080/01621459.1983.10478017>. The intervals are conformal, calibrated three ways and each checked on the test set: on the cross-validated residuals; on a calibration set held out of every fold, as split conformal, after Lei et al. (2018) <doi:10.1080/01621459.2017.1307116>; or with every fold's model at each pixel, as CV+, after Barber et al. (2021) <doi:10.1214/20-AOS1965> -- with points, or whole groups of them, weighing alike, after Dunn et al. (2023) <doi:10.1080/01621459.2022.2060112>. The area of applicability of Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650> is mapped beside them. What a model learned is read by permutation, by SHAP values from expected gradients, after Erion et al. (2021) <doi:10.1038/s42256-021-00343-w>, by SAGE, after Covert et al. (2020) <doi:10.48550/arXiv.2004.00668>, by accumulated local effects, after Apley and Zhu (2020) <doi:10.1111/rssb.12377>, and by refits without each variable. Random forest and multilayer perceptron baselines run under the same folds.