The uptake of machine learning (ML) algorithms in digital soil map** (DSM) is
transforming the way soil scientists produce their maps. Within the past two decades, soil …

Large-scale modeling of environmental variables is an increasingly complex but necessary
task. In this paper, we review the literature on using machine learning to cope with …

Abstract Machine learning algorithms have become very popular for spatial map** of the
environment due to their ability to fit nonlinear and complex relationships. However, this …

While the application of machine-learning algorithms has been highly simplified in the last
years due to their well-documented integration in commonly used statistical programming …

When applied to structured data, conventional random cross-validation techniques can lead
to underestimation of prediction error, and may result in inappropriate model selection. We …

Abstract Machine learning algorithms find frequent application in spatial prediction of biotic
and abiotic environmental variables. However, the characteristics of spatial data, especially …

For decades, the distinction between statistical models and machine learning ones has
been clear. The former are optimized to produce interpretable results, whereas the latter …

Deep learning and particularly Convolutional Neural Networks (CNN) in concert with remote
sensing are becoming standard analytical tools in the geosciences. A series of studies has …

Spatial autocorrelation is inherent to remotely sensed data. Nearby pixels are more similar
than distant ones. This property can help to improve the classification performance, by …

Several spatial and non‐spatial Cross‐Validation (CV) methods have been used to perform
map validation when additional sampling for validation purposes is not possible, yet it is …